Identifying the presence of Parkinson's disease using low-frequency fluctuations in BOLD signals

Abnormalities of cerebral blood flow and the regional brain function in Parkinson's disease: a systematic review and multimodal neuroimaging meta-analysis

Background

Parkinson's disease (PD) is a neurodegenerative disease with high incidence rate. Resting state functional magnetic resonance imaging (rs-fMRI), as a widely used method for studying neurodegenerative diseases, has not yet been combined with two important indicators, amplitude low-frequency fluctuation (ALFF) and cerebral blood flow (CBF), for standardized analysis of PD.

Methods

In this study, we used seed-based d-mapping and permutation of subject images (SDM-PSI) software to investigate the changes in ALFF and CBF of PD patients. After obtaining the regions of PD with changes in ALFF or CBF, we conducted a multimodal analysis to identify brain regions where ALFF and CBF changed together or could not synchronize.

Results

The final study included 31 eligible trials with 37 data sets. The main analysis results showed that the ALFF of the left striatum and left anterior thalamic projection decreased in PD patients, while the CBF of the right superior frontal gyrus decreased. However, the results of multimodal analysis suggested that there were no statistically significant brain regions. In addition, the decrease of ALFF in the left striatum and the decrease of CBF in the right superior frontal gyrus was correlated with the decrease in clinical cognitive scores.

Conclusion

PD patients had a series of spontaneous brain activity abnormalities, mainly involving brain regions related to the striatum-thalamic-cortex circuit, and related to the clinical manifestations of PD. Among them, the left striatum and right superior frontal gyrus are more closely related to cognition.

Systematic review registration

https://www.crd.york.ac.uk/ PROSPERO (CRD42023390914).

Auto-Classification of Parkinson's Disease with Different Motor Subtypes Using Arterial Spin Labelling MRI Based on Machine Learning

The purpose of this study was to automatically classify different motor subtypes of Parkinson's disease (PD) on arterial spin labelling magnetic resonance imaging (ASL-MRI) data using support vector machine (SVM). This study included 38 subjects: 21 PD patients and 17 normal controls (NCs). Based on the Unified Parkinson's Disease Rating Scale (UPDRS) subscores, patients were divided into the tremor-dominant (TD) subtype and the postural instability gait difficulty (PIGD) subtype. The subjects were in a resting state during the acquisition of ASL-MRI data. The automated anatomical atlas 3 (AAL3) template was registered to obtain an ASL image of the same size and shape. We obtained the voxel values of 170 brain regions by considering the location coordinates of these regions and then normalized the data. The length of the feature vector depended on the number of voxel values in each brain region. Three binary classification models were utilized for classifying subjects' data, and we applied SVM to classify voxels in the brain regions. The left subgenual anterior cingulate cortex (ACC_sub_L) was clearly distinguished in both NCs and PD patients using SVM, and we obtained satisfactory diagnostic rates (accuracy = 92.31%, specificity = 96.97%, sensitivity = 84.21%, and AUCmax = 0.9585). For the right supramarginal gyrus (SupraMarginal_R), SVM distinguished the TD group from the other groups with satisfactory diagnostic rates (accuracy = 84.21%, sensitivity = 63.64%, specificity = 92.59%, and AUCmax = 0.9192). For the right intralaminar of thalamus (Thal_IL_R), SVM distinguished the PIGD group from the other groups with satisfactory diagnostic rates (accuracy = 89.47%, sensitivity = 70.00%, specificity = 6.43%, and AUCmax = 0.9464). These results are consistent with the changes in blood perfusion related to PD subtypes. In addition, the sensitive brain regions of the TD group and PIGD group involve the brain regions where the cerebellothalamocortical (CTC) and the striatal thalamocortical (STC) loops are located. Therefore, it is suggested that the blood perfusion patterns of the two loops may be different. These characteristic brain regions could become potential imaging markers of cerebral blood flow to distinguish TD from PIGD. Meanwhile, our findings provide an imaging basis for personalised treatment, thereby optimising clinical diagnostic and treatment approaches.

Patient-specific models link neurotransmitter receptor mechanisms with motor and visuospatial axes of Parkinson's disease

Parkinson's disease involves multiple neurotransmitter systems beyond the classical dopaminergic circuit, but their influence on structural and functional alterations is not well understood. Here, we use patient-specific causal brain modeling to identify latent neurotransmitter receptor-mediated mechanisms contributing to Parkinson's disease progression. Combining the spatial distribution of 15 receptors from post-mortem autoradiography with 6 neuroimaging-derived pathological factors, we detect a diverse set of receptors influencing gray matter atrophy, functional activity dysregulation, microstructural degeneration, and dendrite and dopaminergic transporter loss. Inter-individual variability in receptor mechanisms correlates with symptom severity along two distinct axes, representing motor and psychomotor symptoms with large GABAergic and glutamatergic contributions, and cholinergically-dominant visuospatial, psychiatric and memory dysfunction. Our work demonstrates that receptor architecture helps explain multi-factorial brain re-organization, and suggests that distinct, co-existing receptor-mediated processes underlie Parkinson's disease.

Intrinsic brain activity alterations in patients with Parkinson's disease

OBJECTIVE

The objective of this study is to explore the brain activity alterations in Parkinson's disease (PD) from the perspectives of neuronal activity, synchronization of neuronal activity, and coordination of whole-brain activity.

METHODS

In this study, we recruited 38 PD patients and 35 matched healthy controls (HCs). We explored intrinsic brain activity alterations in PD by comparing resting-state functional magnetic resonance imaging (rs-fMRI) metrics of the amplitude of low-frequency of fluctuation (ALFF), the fractional amplitude of low-frequency fluctuation (fALFF), percent amplitude of fluctuation (PerAF), regional homogeneity (ReHo), and degree centrality (DC). Two-sample t-tests were used to determine the differences between the two groups. Spearman correlation analysis was used to explore the relationships between abnormal ALFF, fALFF, PerAF, ReHo, and DC values and clinical indicators such as the Movement Disorder Society's Unified Parkinson's Disease Rating Scale (MDS-UPDRS), Hoehn and Yahr (H&Y) stage, and duration of disease.

RESULTS

Compared with the HCs, PD had increased ALFF,fALFF, and PerAF in the temporal lobe and cerebellum, and decreased ALFF,fALFF, and PerAF in the occipital-parietal lobe in the neuronal activity. In the synchronization of neuronal activity, PD patients had increased ReHo in the right inferior parietal lobule and decreased ReHo in the caudate. In the coordination of whole-brain activity, PD patients had increased DC in the cerebellum and decreased DC in the occipital lobe. Correlation analysis showed that there is a correlation between abnormal brain regions and clinical indicators in PD. Notably, the changes in occipital lobe brain activity were found in ALFF, fALFF, PerAF, and DC, and were most correlated with the clinical indicators of PD patients.

CONCLUSIONS

This study found that intrinsic brain function in several occipital-temporal-parietal and cerebellum regions was altered in PD patients, potentially related to the clinical indicators of PD. These results may enhance our understanding of the underlying neural mechanisms of PD and may contribute to further exploring the selection of therapeutic targets in PD patients.

Empathy for others versus for one's child: Associations with mothers' brain activation during a social cognitive task and with their toddlers' functioning

Caregivers who are higher in dispositional empathy tend to have children with better developmental outcomes; however, few studies have considered the role of child-directed (i.e., "parental") empathy, which may be relevant for the caregiver-child relationship. We hypothesized that mothers' parental empathy during their child's infancy will be a stronger predictor of their child's social-emotional functioning as a toddler than will mothers' dispositional empathy. We further explored whether parental and dispositional empathy have shared or distinct patterns of neural activation during a social-cognitive movie-watching task. In 118 mother-infant dyads, greater parental empathy assessed when infants were 6 months old was associated with more social-emotional competencies and fewer problems in the children 1 year later, even after adjusting for dispositional empathy. In contrast, dispositional empathy was not associated with child functioning when controlling for parental empathy. In a subset of 20 mothers, insula activation was positively associated with specific facets of both dispositional and parental empathy, whereas right temporoparietal junction activation was associated only with parental empathy. Thus, dispositional and parental empathy appear to be dissociable by both brain and behavioral metrics. Parental empathy may be a viable target for interventions, especially for toddlers at risk for developing social-emotional difficulties.

The White Matter Functional Abnormalities in Patients with Transient Ischemic Attack: A Reinforcement Learning Approach

Background

Transient ischemic attack (TIA) is a known risk factor for stroke. Abnormal alterations in the low-frequency range of the gray matter (GM) of the brain have been studied in patients with TIA. However, whether there are abnormal neural activities in the low-frequency range of the white matter (WM) in patients with TIA remains unknown. The current study applied two resting-state metrics to explore functional abnormalities in the low-frequency range of WM in patients with TIA. Furthermore, a reinforcement learning method was used to investigate whether altered WM function could be a diagnostic indicator of TIA.

Methods

We enrolled 48 patients with TIA and 41 age- and sex-matched healthy controls (HCs). Resting-state functional magnetic resonance imaging (rs-fMRI) and clinical/physiological/biochemical data were collected from each participant. We compared the group differences between patients with TIA and HCs in the low-frequency range of WM using two resting-state metrics: amplitude of low-frequency fluctuation (ALFF) and fractional ALFF (fALFF). The altered ALFF and fALFF values were defined as features of the reinforcement learning method involving a Q-learning algorithm.

Results

Compared with HCs, patients with TIA showed decreased ALFF in the right cingulate gyrus/right superior longitudinal fasciculus/left superior corona radiata and decreased fALFF in the right cerebral peduncle/right cingulate gyrus/middle cerebellar peduncle. Based on these two rs-fMRI metrics, an optimal Q-learning model was obtained with an accuracy of 82.02%, sensitivity of 85.42%, specificity of 78.05%, precision of 82.00%, and area under the curve (AUC) of 0.87.

Conclusion

The present study revealed abnormal WM functional alterations in the low-frequency range in patients with TIA. These results support the role of WM functional neural activity as a potential neuromarker in classifying patients with TIA and offer novel insights into the underlying mechanisms in patients with TIA from the perspective of WM function.

Neural progenitor cells derived from fibroblasts induced by small molecule compounds under hypoxia for treatment of Parkinson's disease in rats

Neural progenitor cells (NPCs) capable of self-renewal and differentiation into neural cell lineages offer broad prospects for cell therapy for neurodegenerative diseases. However, cell therapy based on NPC transplantation is limited by the inability to acquire sufficient quantities of NPCs. Previous studies have found that a chemical cocktail of valproic acid, CHIR99021, and Repsox (VCR) promotes mouse fibroblasts to differentiate into NPCs under hypoxic conditions. Therefore, we used VCR (0.5 mM valproic acid, 3 μM CHIR99021, and 1 μM Repsox) to induce the reprogramming of rat embryonic fibroblasts into NPCs under a hypoxic condition (5%). These NPCs exhibited typical neurosphere-like structures that can express NPC markers, such as Nestin, SRY-box transcription factor 2, and paired box 6 (Pax6), and could also differentiate into multiple types of functional neurons and astrocytes in vitro. They had similar gene expression profiles to those of rat brain-derived neural stem cells. Subsequently, the chemically-induced NPCs (ciNPCs) were stereotactically transplanted into the substantia nigra of 6-hydroxydopamine-lesioned parkinsonian rats. We found that the ciNPCs exhibited long-term survival, migrated long distances, and differentiated into multiple types of functional neurons and glial cells in vivo. Moreover, the parkinsonian behavioral defects of the parkinsonian model rats grafted with ciNPCs showed remarkable functional recovery. These findings suggest that rat fibroblasts can be directly transformed into NPCs using a chemical cocktail of VCR without introducing exogenous factors, which may be an attractive donor material for transplantation therapy for Parkinson's disease.

Artificial intelligence-enabled detection and assessment of Parkinson's disease using nocturnal breathing signals

There are currently no effective biomarkers for diagnosing Parkinson's disease (PD) or tracking its progression. Here, we developed an artificial intelligence (AI) model to detect PD and track its progression from nocturnal breathing signals. The model was evaluated on a large dataset comprising 7,671 individuals, using data from several hospitals in the United States, as well as multiple public datasets. The AI model can detect PD with an area-under-the-curve of 0.90 and 0.85 on held-out and external test sets, respectively. The AI model can also estimate PD severity and progression in accordance with the Movement Disorder Society Unified Parkinson's Disease Rating Scale (R = 0.94, P = 3.6 × 10-25). The AI model uses an attention layer that allows for interpreting its predictions with respect to sleep and electroencephalogram. Moreover, the model can assess PD in the home setting in a touchless manner, by extracting breathing from radio waves that bounce off a person's body during sleep. Our study demonstrates the feasibility of objective, noninvasive, at-home assessment of PD, and also provides initial evidence that this AI model may be useful for risk assessment before clinical diagnosis.

MALAT1 lncRNA and Parkinson's Disease: The role in the Pathophysiology and Significance for Diagnostic and Therapeutic Approaches

Parkinson's disease (PD) is the second most common age-related neurodegenerative disorder. PD is characterized by progressive loss of dopamine-producing neurons in the substantia nigra (SN) region of brain tissue followed by the α-synuclein-based Lewy bodies' formation. These conditions are manifested by various motor and non-motor symptoms such as resting tremor, limb rigidity, bradykinesia and posture instability, cognitive impairment, sleep disorders, and emotional and memory dysfunctions. Long non-coding RNAs (lncRNAs) are closely related to protein-coding genes and are involved in various biological processes. Metastasis-associated lung adenocarcinoma transcript 1 (MALAT1) lncRNA is involved in different pathways, including alternative splicing, transcriptional regulation, and post-transcriptional regulation, and also interacts with RNAs as a miRNA sponge. MALAT1 is highly expressed in brain tissues and several lines of evidence suggested it is probably involved in synapse generation and other neurophysiological pathways. This narrative review discussed all aspects of MALAT1-associated mechanisms involved in the PD pathogenesis, i.e., perturbed α-synuclein homeostasis, apoptosis and autophagy, and neuro-inflammation. Lastly, the possible applications of MALAT1 as a diagnostic biomarker and its importance to developing therapeutic strategies were highlighted. The literature search was conducted using neurodegeneration, neurodegenerative disorders, Parkinson's disease, lncRNA, and MALAT1 as search items in Google Scholar, Web of Knowledge, PubMed, and Scopus up to December 2021.

Altered Brain Activity in Patients With Comitant Strabismus Detected by Analysis of the Fractional Amplitude of Low-Frequency Fluctuation: A Resting-State Functional MRI Study

More and more studies showed that strabismus is not simply an ocular disease, but a neuro-ophthalmology disease. To analyze potential changes in brain activity and their relationship to behavioral performance in comitant strabismus patients and healthy controls. Our study recruited 28 patients with comitant strabismus and 28 people with matched weight, age range, and sex ratio as healthy controls. Using resting-state functional magnetic resonance imaging, we evaluated fALFF to compare spontaneous brain activity between comitant strabismus and healthy controls. We did hospital anxiety and depression scale questionnaires for these patients. We found significantly lower fALFF value in comitant strabismus patients compared with controls in the left frontal superior medial gyrus and the right middle cingulum. In the latter region, fALFF was significantly negatively correlated with the hospital anxiety and depression scale, as well as the duration of disease. Receiver operating characteristic curve analysis indicated that the fALFF method has clear potential for the diagnosis of comitant strabismus patients. These results revealed abnormal spontaneous activity in two brain regions of comitant strabismus patients, which may indicate underlying pathologic mechanisms and may help to advance clinical treatment.

A Transcriptome Analysis of mRNAs and Long Non-Coding RNAs in Patients with Parkinson's Disease

Parkinson's disease (PD) is the second most common neurodegenerative disorder. The number of cases of PD is expected to double by 2030, representing a heavy burden on the healthcare system. Clinical symptoms include the progressive loss of dopaminergic neurons in the substantia nigra of the midbrain, which leads to striatal dopamine deficiency and, subsequently, causes motor dysfunction. Certainly, the study of the transcriptome of the various RNAs plays a crucial role in the study of this neurodegenerative disease. In fact, the aim of this study was to evaluate the transcriptome in a cohort of subjects with PD compared with a control cohort. In particular we focused on mRNAs and long non-coding RNAs (lncRNA), using the Illumina NextSeq 550 DX System. Differential expression analysis revealed 716 transcripts with padj ≤ 0.05; among these, 630 were mRNA (coding protein), lncRNA, and MT_tRNA. Ingenuity pathway analysis (IPA, Qiagen) was used to perform the functional and pathway analysis. The highest statistically significant pathways were: IL-15 signaling, B cell receptor signaling, systemic lupus erythematosus in B cell signaling pathway, communication between innate and adaptive immune cells, and melatonin degradation II. Our findings further reinforce the important roles of mitochondria and lncRNA in PD and, in parallel, further support the concept of inverse comorbidity between PD and some cancers.

A Transcriptome Analysis of mRNAs and Long Non-Coding RNAs in Patients with Parkinson's Disease

Parkinson's disease (PD) is the second most common neurodegenerative disorder. The number of cases of PD is expected to double by 2030, representing a heavy burden on the healthcare system. Clinical symptoms include the progressive loss of dopaminergic neurons in the substantia nigra of the midbrain, which leads to striatal dopamine deficiency and, subsequently, causes motor dysfunction. Certainly, the study of the transcriptome of the various RNAs plays a crucial role in the study of this neurodegenerative disease. In fact, the aim of this study was to evaluate the transcriptome in a cohort of subjects with PD compared with a control cohort. In particular we focused on mRNAs and long non-coding RNAs (lncRNA), using the Illumina NextSeq 550 DX System. Differential expression analysis revealed 716 transcripts with padj ≤ 0.05; among these, 630 were mRNA (coding protein), lncRNA, and MT_tRNA. Ingenuity pathway analysis (IPA, Qiagen) was used to perform the functional and pathway analysis. The highest statistically significant pathways were: IL-15 signaling, B cell receptor signaling, systemic lupus erythematosus in B cell signaling pathway, communication between innate and adaptive immune cells, and melatonin degradation II. Our findings further reinforce the important roles of mitochondria and lncRNA in PD and, in parallel, further support the concept of inverse comorbidity between PD and some cancers.

Mining imaging and clinical data with machine learning approaches for the diagnosis and early detection of Parkinson's disease

Parkinson’s disease (PD) is a common, progressive, and currently incurable neurodegenerative movement disorder. The diagnosis of PD is challenging, especially in the differential diagnosis of parkinsonism and in early PD detection. Due to the advantages of machine learning such as learning complex data patterns and making inferences for individuals, machine-learning techniques have been increasingly applied to the diagnosis of PD, and have shown some promising results. Machine-learning-based imaging applications have made it possible to help differentiate parkinsonism and detect PD at early stages automatically in a number of neuroimaging studies. Comparative studies have shown that machine-learning-based SPECT image analysis applications in PD have outperformed conventional semi-quantitative analysis in detecting PD-associated dopaminergic degeneration, performed comparably well as experts’ visual inspection, and helped improve PD diagnostic accuracy of radiologists. Using combined multi-modal (imaging and clinical) data in these applications may further enhance PD diagnosis and early detection. To integrate machine-learning-based diagnostic applications into clinical systems, further validation and optimization of these applications are needed to make them accurate and reliable. It is anticipated that machine-learning techniques will further help improve differential diagnosis of parkinsonism and early detection of PD, which may reduce the error rate of PD diagnosis and help detect PD at pre-motor stage to make it possible for early treatments (e.g., neuroprotective treatment) to slow down PD progression, prevent severe motor symptoms from emerging, and relieve patients from suffering.

Machine learning approaches for parsing comorbidity/heterogeneity in antisociality and substance use disorders: A primer

By some accounts, as many as 93% of individuals diagnosed with antisocial personality disorder (ASPD) or psychopathy also meet criteria for some form of substance use disorder (SUD). This high level of comorbidity, combined with an overlapping biopsychosocial profile, and potentially interacting features, has made it difficult to delineate the shared/unique characteristics of each disorder. Moreover, while rarely acknowledged, both SUD and antisociality exist as highly heterogeneous disorders in need of more targeted parcellation. While emerging data-driven nosology for psychiatric disorders (e.g., Research Domain Criteria (RDoC), Hierarchical Taxonomy of Psychopathology (HiTOP)) offers the opportunity for a more systematic delineation of the externalizing spectrum, the interrogation of large, complex neuroimaging-based datasets may require data-driven approaches that are not yet widely employed in psychiatric neuroscience. With this in mind, the proposed article sets out to provide an introduction into machine learning methods for neuroimaging that can help parse comorbid, heterogeneous externalizing samples. The modest machine learning work conducted to date within the externalizing domain demonstrates the potential utility of the approach but remains highly nascent. Within the paper, we make suggestions for how future work can make use of machine learning methods, in combination with emerging psychiatric nosology systems, to further diagnostic and etiological understandings of the externalizing spectrum. Finally, we briefly consider some challenges that will need to be overcome to encourage further progress in the field.

Correlation of visual area with tremor improvement after MRgFUS thalamotomy in Parkinson's disease

OBJECTIVE

MRI-guided focused ultrasound (MRgFUS) thalamotomy is a novel and minimally invasive alternative for medication-refractory tremor in Parkinson's disease (PD). However, the impact of MRgFUS thalamotomy on spontaneous neuronal activity in PD remains unclear. The purpose of the current study was to evaluate the effects of MRgFUS thalamotomy on local fluctuations in neuronal activity as measured by the fractional amplitude of low-frequency fluctuations (fALFF) in patients with PD.

METHODS

Participants with PD undergoing MRgFUS thalamotomy were recruited. Tremor scores were assessed before and 3 and 12 months after treatment using the Clinical Rating Scale for Tremor. MRI data were collected before and 1 day, 1 week, 1 month, 3 months, and 12 months after thalamotomy. The fALFF was calculated. A whole-brain voxel-wise paired t-test was used to identify significant changes in fALFF at 12 months after treatment compared to baseline. Then fALFF in the regions with significant differences were extracted from fALFF maps of patients for further one-way repeated-measures ANOVA to investigate its dynamic alterations. The association between fALFF changes induced by thalamotomy and tremor improvement were evaluated using the nonparametric Spearman rank test.

RESULTS

Nine participants with PD (mean age ± SD 64.7 ± 6.1 years, 8 males) were evaluated. Voxel-based analysis showed that fALFF in the left occipital cortex (Brodmann area 17 [BA17]) significantly decreased at 12 months after thalamotomy compared to baseline (voxel p < 0.001, cluster p < 0.05 family-wise error [FWE] corrected). At baseline, fALFF in the left occipital BA17 in patients was elevated compared with that in 9 age- and gender-matched healthy subjects (p < 0.05). Longitudinal analysis displayed the dynamic changes of fALFF in this region (F (5,40) = 3.61, p = 0.009). There was a significant positive correlation between the falling trend in fALFF in the left occipital BA17 and hand tremor improvement after treatment over 3 time points (Spearman's rho = 0.44, p = 0.02).

CONCLUSIONS

The present study investigated the impact of MRgFUS ventral intermediate nucleus thalamotomy on spontaneous neural activity in medication-refractory tremor-dominant PD. The visual area is, for the first time, reported as relevant to tremor improvement in PD after MRgFUS thalamotomy, suggesting a distant effect of MRgFUS thalamotomy and the involvement of specific visuomotor networks in tremor control in PD.

Use of machine learning method on automatic classification of motor subtype of Parkinson's disease based on multilevel indices of rs-fMRI

OBJECTIVE

This study aimed to develop an automatic classifier to distinguish different motor subtypes of Parkinson's disease (PD) based on multilevel indices of resting-state functional magnetic resonance imaging (rs-fMRI).

METHODS

Ninety-six PD patients, which included thirty-nine postural instability and gait difficulty (PIGD) subtype and fifty-seven tremor-dominant (TD) subtype, were enrolled and allocated to training and validation datasets with a ratio of 7:3. A total of five types of index, consisting of mean regional homogeneity (mReHo), mean amplitude of low-frequency fluctuation (mALFF), degree of centrality (DC), voxel-mirrored homotopic connectivity (VMHC), and functional connectivity (FC), were extracted. The features were then selected using a two-sample t-test, the least absolute shrinkage and selection operator (LASSO), and Spearman's rank correlation coefficient. Finally, support vector machine (SVM) models based on the separate index and multilevel indices were built, and the performance of models was assessed via the area under the receiver operating characteristic curve (AUC). Feature importance was evaluated using Shapley additive explanation (SHAP) values.

RESULTS

The optimal SVM model was obtained based on multilevel rs-fMRI indices, with an AUC of 0.934 in the training dataset and an AUC of 0.917 in the validation dataset. The AUCs of the models based on the separate index were ranged from 0.783 to 0.858 for the training dataset and from 0.713 to 0.792 for the validation dataset. SHAP analysis revealed that functional activity and connectivity in frontal lobe and cerebellum were important features for differentiating PD subtypes.

CONCLUSIONS

Our findings demonstrated multilevel rs-fMRI indices could provide more comprehensive information on brain functionalteration. Furthermore, the machine learning method based on multilevel rs-fMRI indices might be served as an alternative approach for automatically classifying clinical subtypes in PD at the individual level.

Transcriptome profiling of five brain regions in a 6-hydroxydopamine rat model of Parkinson's disease

BACKGROUND

Parkinson's disease (PD) is a neurodegenerative disease, and its pathogenesis is unclear. Previous studies mainly focus on the lesions of substantia nigra (SN) and striatum (Str) in PD. However, lesions are not limited. The olfactory bulb (OB), subventricular zone (SVZ), and hippocampus (Hippo) are also affected in PD.

AIM

To reveal gene expression changes in the five brain regions (OB, SVZ, Str, SN, and Hippo), and to look for potential candidate genes and pathways that may be correlated with the pathogenesis of PD.

MATERIALS AND METHODS

We established control group and 6-hydroxydopamine (6-OHDA) PD model group, and detected gene expressions in the five brain regions using RNA-seq and real-time quantitative polymerase chain reaction (RT-qPCR). We further analyzed the RNA-seq data by bioinformatics.

RESULTS

We identified differentially expressed genes (DEGs) in all five brain regions. The DEGs were significantly enriched in the "dopaminergic synapse" and "retrograde endocannabinoid signaling," and Gi/o-GIRK is the shared cascade in the two pathways. We further identified Ephx2, Fam111a, and Gng2 as the potential candidate genes in the pathogenesis of PD for further studies.

CONCLUSION

Our study suggested that gene expressions change in the five brain regions following exposure to 6-OHDA. The "dopaminergic synapse," "retrograde endocannabinoid signaling," and Gi/o-GIRK may be the key pathways and cascade of the synaptic damage in 6-OHDA PD rats. Ephx2, Fam111a, and Gng2 may play critical roles in the pathogenesis of PD.

Frequency specific co-activation pattern analysis via sparse nonnegative tensor decomposition

BACKGROUND

Traditionally, the diagnosis of Parkinson's disease (PD) has been made based on symptoms. Extensive studies have demonstrated that PD may lead to variation of brain activity in different frequency bands. However, frequency specific dynamic alterations of PD have not yet been explored.

NEW METHOD

In order to address this gap, a novel sparse nonnegative tensor decomposition (SNTD) method was used to estimate frequency specific co-activation patterns (CAP). The difference between PD and healthy controls (HC) are investigated with the proposed framework.

RESULT

The difference between PD and HC mainly exists at frequency band 0.04-0.1 Hz in basal ganglia. We also found that the average intensity of PD in this frequency band is significantly correlated with the Hoehn and Yahr scale.

COMPARISON WITH EXISTING METHODS

Compared with conventional CAP approach, SNTD estimates frequency specific CAPs that show alterations in PD patients.

CONCLUSION

SNTD provides an alternative to K-means clustering used in conventional CAP analysis. With the proposed framework, frequency specific CAPs are extracted, and alterations in PD patients are also successfully discovered.

LncRNA MALAT1 potentiates inflammation disorder in Parkinson's disease

With this investigation, we investigated on the contribution of lncRNA MALAT1 to inflammation disorder in Parkinson's Disease (PD). Serum samples were gathered from sporadic PD patients and healthy controls, and single nucleotide polymorphisms (SNPs) of MALAT1, including rs11227209, rs3200401, rs4102217, rs591291, rs619586 and rs664589, were identified. Serum level of MALAT1 was quantified using RT-PCR, and IL-1β, IL-6, TNF-α and IFN-γ levels in serum were measured with ELISA kits. Inflammation cell models were established by treating PC12 cells with LPS, and cytokine production of pcDNA3.1-MALAT1/si-MALAT1-transfected PC12 cells was evaluated. The results showed that PD patients with high serum level of MALAT1 were associated with lower MMSE score and higher serum levels of IL-1β, IL-6, TNF-α and IFN-γ than patients carrying low serum level of MALAT1 (p < .05). Mutant alleles of SNPs in MALAT1, including rs3200401 (C>T) and rs4102217 (G>C), tended to elevate PD susceptibility and facilitate cytokine production, as compared with their wild alleles (p < .05). And LPS-exposed PC12 cells secreted larger amounts of inflammation cytokines in the pcDNA3.1-MALAT1 group than in the Mock group (p < .05). In conclusion, MALAT1 participated in modifying inflammation disorder underlying PD aetiology, suggesting that it might be a promising therapeutic target for PD.

Machine Learning for the Diagnosis of Parkinson's Disease: A Review of Literature

Diagnosis of Parkinson's disease (PD) is commonly based on medical observations and assessment of clinical signs, including the characterization of a variety of motor symptoms. However, traditional diagnostic approaches may suffer from subjectivity as they rely on the evaluation of movements that are sometimes subtle to human eyes and therefore difficult to classify, leading to possible misclassification. In the meantime, early non-motor symptoms of PD may be mild and can be caused by many other conditions. Therefore, these symptoms are often overlooked, making diagnosis of PD at an early stage challenging. To address these difficulties and to refine the diagnosis and assessment procedures of PD, machine learning methods have been implemented for the classification of PD and healthy controls or patients with similar clinical presentations (e.g., movement disorders or other Parkinsonian syndromes). To provide a comprehensive overview of data modalities and machine learning methods that have been used in the diagnosis and differential diagnosis of PD, in this study, we conducted a literature review of studies published until February 14, 2020, using the PubMed and IEEE Xplore databases. A total of 209 studies were included, extracted for relevant information and presented in this review, with an investigation of their aims, sources of data, types of data, machine learning methods and associated outcomes. These studies demonstrate a high potential for adaptation of machine learning methods and novel biomarkers in clinical decision making, leading to increasingly systematic, informed diagnosis of PD.

Fractional amplitude of low-frequency fluctuation in patients with neovascular glaucoma: a resting-state functional magnetic resonance imaging study

Background

Neovascular glaucoma (NVG) is a secondary refractory disease with a poor prognosis, and there are few advanced studies on its pathogenesis and treatment. In this research, the fractional amplitude of low-frequency fluctuation (fALFF) technology was used in resting-state functional magnetic resonance imaging (rsfMRI) to investigate intrinsic neuron activity in the patient's brain with NVG.

Methods

Sixteen patients with NVG (eight males and eight females) and 16 healthy controls (HCs) of similar age and sex were included. All patients and controls received rsfMRI scans, and the differences between the two groups in fALFF values were compared by independent sample t-test. Receiver operating characteristic (ROC) curves were used to compare fALFF values in the brain regions of NVG patients and HCs and assess accuracy. Finally, Pearson linear correlation analysis assessed the correlation between fALFF signals in brain regions and the clinical evaluation indicators of patients with NVG.

Results

In patients with NVG, fALFF signal values in the right Rolandic operculum, left anterior cingulate and paracingulate gyri, and right caudate were significantly decreased. In contrast, fALFF signal values in the left precuneus were significantly higher than those recorded in the HCs. Analysis of the ROC curve for each brain region showed that the area under the ROC curve of NVG patients was large (close to 1), and the accuracy was good. In the NVG group, the hospital anxiety and depression scale (r=-0.952, P<0.001) and left best-corrected visual acuity (r=-0.802, P<0.001) had a negative linear correlation with the fALFF signal value of the right Rolandic operculum. The hospital anxiety and depression scale had a negative linear correlation with the fALFF signal value of the right caudate (r=-0.948, P<0.001).

Conclusions

NVG patients showed dysfunction in several brain regions. These findings may assist in revealing the underlying neural mechanism of brain activity associated with NVG.

Multivariate radiomics models based on 18F-FDG hybrid PET/MRI for distinguishing between Parkinson's disease and multiple system atrophy

PURPOSE

To construct multivariate radiomics models using hybrid ¹⁸F-FDG PET/MRI for distinguishing between Parkinson's disease (PD) and multiple system atrophy (MSA).

METHODS

Ninety patients (60 with PD and 30 with MSA) were randomized to training and test sets in a 7:3 ratio. All patients underwent ¹⁸F-fluorodeoxyglucose (¹⁸F-FDG) PET/MRI to simultaneously obtain metabolic images (¹⁸F-FDG), structural MRI images (T1-weighted imaging (T1WI), T2-weighted imaging (T2WI) and T2-weighted fluid-attenuated inversion recovery (T2/FLAIR)) and functional MRI images (susceptibility-weighted imaging (SWI) and apparent diffusion coefficient). Using PET and five MRI sequences, we extracted 1172 radiomics features from the putamina and caudate nuclei. The radiomics signatures were constructed with the least absolute shrinkage and selection operator algorithm in the training set, with progressive optimization through single-sequence and double-sequence radiomics models. Multivariable logistic regression analysis was used to develop a clinical-radiomics model, combining the optimal multi-sequence radiomics signature with clinical characteristics and SUV values. The diagnostic performance of the models was assessed by receiver operating characteristic and decision curve analysis (DCA).

RESULTS

The radiomics signatures showed favourable diagnostic efficacy. The optimal model comprised structural (T1WI), functional (SWI) and metabolic (¹⁸F-FDG) sequences (Radscore_{FDG_T1WI_SWI}) with the area under curves (AUCs) of the training and test sets of 0.971 and 0.957, respectively. The integrated model, incorporating Radscore_{FDG_T1WI_SWI}, three clinical symptoms (disease duration, dysarthria and autonomic failure) and SUV_max, demonstrated satisfactory calibration and discrimination in the training and test sets (0.993 and 0.994, respectively). DCA indicated the highest clinical benefit of the clinical-radiomics integrated model.

CONCLUSIONS

The radiomics signature with metabolic, structural and functional information provided by hybrid ¹⁸F-FDG PET/MRI may achieve promising diagnostic efficacy for distinguishing between PD and MSA. The clinical-radiomics integrated model performed best.

Long Non-coding RNAs in Parkinson's Disease

Parkinson's disease (PD) is the second most prevalent neurodegenerative disorder and is associated with a range of motor and non-motor clinical symptoms. The underlying molecular pathogenesis of PD involves a variety of pathways and mechanisms, including α-synuclein proteostasis, mitochondrial dysfunction, oxidative stress, autophagy and apoptosis, neuroinflammation, and epigenetic regulation. Long non-coding RNAs (lncRNAs) are involved in the regulation of multiple pathological processes of PD. In this review, we provide an overview of large-scale studies on lncRNA expression profiling in PD patients and models, as well as highlight the impacts of lncRNAs on the pathogenesis of PD, which could provide basic information regarding the putative lncRNA-based biomarkers and therapeutic targets for the early diagnosis and treatment strategies for PD.

Assessment of the role of non-coding RNAs in the pathophysiology of Parkinson's disease

Parkinson's disease (PD) is the second main neurodegenerative disease causing motor abnormalities in the middle-aged and old individuals. In some cases, cognitive dysfunction also occurs. The clinical signs of PD are bradykinesia, rigidity and resting tremor. As these signs might be detected in other neurological conditions such as multiple systems atrophy and corticobasal degeneration, it is necessary to find specific and sensitive markers for this disorder. Non-coding RNAs are implicated in the different PD-associated features such as α-synuclein expression and Lewy body construction, mitochondrial dysfunction, apoptosis, neuroinflammation and defects in glial cell-derived neurotrophic factor. Several researches have confirmed dysregulation of long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) in brain tissues, plasma exosomes and leukocytes of affected individuals or animal models of PD. A number of these transcripts directly regulate the neurodegenerative process in PD. In the current study, we review the current data about dysregulation of ncRNAs and the role of their genomic variants in the pathogenesis of PD.

Aberrant Amplitude of Low-Frequency Fluctuations in Different Frequency Bands in Patients With Parkinson's Disease

Previous studies reported abnormal spontaneous neural activity in Parkinson's disease (PD) patients using resting-state functional magnetic resonance imaging (R-fMRI). However, the frequency-dependent neural activity in PD is largely unknown. Here, 35 PD patients and 35 age- and education-matched healthy controls (HCs) underwent R-fMRI scanning to investigate abnormal spontaneous neural activity of PD using the amplitude of low-frequency fluctuation (ALFF) approach within the conventional band (typical band: 0.01-0.08 Hz) and specific frequency bands (slow-5: 0.010-0.027 Hz and slow-4: 0.027-0.073 Hz). Compared with HCs, PD patients exhibited increased ALFF in the parieto-temporo-occipital regions, such as the bilateral inferior temporal gyrus/fusiform gyrus (ITG/FG) and left angular gyrus/posterior middle temporal gyrus (AG/pMTG), and displayed decreased ALFF in the left cerebellum, right precuneus, and left postcentral gyrus/supramarginal gyrus (PostC/SMG) in the typical band. PD patients showed greater increased ALFF in the left caudate/putamen, left anterior cingulate cortex/medial superior frontal gyrus (ACC/mSFG), left middle cingulate cortex (MCC), right ITG, and left hippocampus, along with greater decreased ALFF in the left pallidum in the slow-5 band, whereas greater increased ALFF in the left ITG/FG/hippocampus accompanied by greater decreased ALFF in the precentral gyrus/PostC was found in the slow-4 band (uncorrected). Additionally, the left caudate/putamen was positively correlated with levodopa equivalent daily dose (LEDD), Hoehn and Yahr (HY) stage, and disease duration. Our results suggest that PD is related to widespread abnormal brain activities and that the abnormalities of ALFF in PD are associated with specific frequency bands. Future studies should take frequency band effects into account when examining spontaneous neural activity in PD.

Transcriptome sequencing reveals aerobic exercise training-associated lncRNAs for improving Parkinson's disease

The present study aimed to evaluate the effect of aerobic exercise training (AET) on the performance of mice with Parkinson's disease (PD) and to explore the molecular mechanism of AET-associated long noncoding RNAs (lncRNAs) in PD treatment. The results showed that the behaviors of PD mice were significantly improved after 4 weeks of AET. The substantia nigra pars compacta of PD mice showed scattered large multipolar cells and surrounding neutrophils after AET. In addition, a total of 62 differentially expressed lncRNAs (DE-lncRNAs) were identified between the AET group and the PD group, including 55 up-regulated and 7 down-regulated DE-lncRNAs in the AET group. Furthermore, the target genes of DE-lncRNAs, including LOC102633466, LOC102637865, and LOC102638670, were mainly involved in ECM-receptor interaction, the Wnt pathway and the PI3K/AKT/mTOR pathway. Quantitative real-time polymerase chain reaction showed that these three DE-lncRNAs were significantly up-regulated in the AET group than in the PD group. The lncRNA-miRNA-mRNA ceRNA network suggested that these 3 DE-lncRNAs may improve PD via the ceRNA mechanism. In conclusion, this study suggests that aerobic exercise improves motor performance of PD mice and provides a foundation for further studies on the molecular mechanism of lncRNAs in treating PD.

A Study of the Relationship Between Uric Acid and Substantia Nigra Brain Connectivity in Patients With REM Sleep Behavior Disorder and Parkinson's Disease

Low levels of the natural antioxidant uric acid (UA) and the presence of REM sleep behavior disorder (RBD) are both associated with an increased likelihood of developing Parkinson's disease (PD). RBD and PD are also accompanied by basal ganglia dysfunction including decreased nigrostriatal and nigrocortical resting state functional connectivity. Despite these independent findings, the relationship between UA and substantia nigra (SN) functional connectivity remains unknown. In the present study, voxelwise analysis of covariance was used in a cross-sectional design to explore the relationship between UA and whole-brain SN functional connectivity using the eyes-open resting state fMRI method in controls without RBD, patients with idiopathic RBD, and PD patients with and without RBD. The results showed that controls exhibited a positive relationship between UA and SN functional connectivity with left lingual gyrus. The positive relationship was reduced in patients with RBD and PD with RBD, and the relationship was found to be negative in PD patients. These results are the first to show differential relationships between UA and SN functional connectivity among controls, prodromal, and diagnosed PD patients in a ventral occipital region previously documented to be metabolically and structurally altered in RBD and PD. More investigation, including replication in longitudinal designs with larger samples, is needed to understand the pathophysiological significance of these changes.

Shared functional neural substrates in Parkinson's disease and drug-induced parkinsonism: association with dopaminergic depletion

While drug-induced parkinsonism (DIP) is mainly caused by blockage of the dopaminergic pathway, multiple neurotransmitter systems besides the dopaminergic system are involved in Parkinson’s disease (PD). Therefore, alterations found in both DIP and PD might be manifestations of dopaminergic dysfunction. To prove this hypothesis, we aimed to define the areas commonly involved in DIP and PD and determine whether the overlapping areas were associated with the dopaminergic system. 68 PD patients, 69 DIP patients and 70 age-and sex-matched controls underwent resting-state functional MRI (rsfMRI). Regional homogeneity (ReHo), amplitude of low-frequency fluctuation (ALFF) and fractional ALFF were calculated and compared. Afterwards, we compared mean rsfMRI values extracted from the overlapping areas with uptake quantitatively measured on dopamine transporter (DAT) images and neuropsychological test results. Compared to the controls, both PD and DIP patients revealed altered rsfMRI values in the right insular cortex, right temporo-occipital cortex, and cerebellum. Among them, decreased ALFF in the right insular cortex and decreased ReHo in the right occipital cortex were correlated with decreased DAT uptake in the caudate as well as executive, visuospatial, and language function. Increased ReHo in the cerebellum was also correlated with decrease DAT uptake in the posterior and ventral anterior putamen, but not with cognitive function. In conclusion, the insular cortex, occipital cortex, and cerebellum were commonly affected in both PD and DIP patients and might be associated with altered dopaminergic modulation.

α-Synuclein Induces Progressive Changes in Brain Microstructure and Sensory-Evoked Brain Function That Precedes Locomotor Decline

In vivo functional and structural brain imaging of synucleinopathies in humans have provided a rich new understanding of the affected networks across the cortex and subcortex. Despite this progress, the temporal relationship between α-synuclein (α-syn) pathology and the functional and structural changes occurring in the brain is not well understood. Here, we examine the temporal relationship between locomotor ability, brain microstructure, functional brain activity, and α-syn pathology by longitudinally conducting rotarod, diffusion magnetic resonance imaging (MRI), resting-state functional MRI (fMRI), and sensory-evoked fMRI on 20 mice injected with α-syn fibrils and 20 PBS-injected mice at three timepoints (10 males and 10 females per group). Intramuscular injection of α-syn fibrils in the hindlimb of M83^+/- mice leads to progressive α-syn pathology along the spinal cord, brainstem, and midbrain by 16 weeks post-injection. Our results suggest that peripheral injection of α-syn has acute systemic effects on the central nervous system such that structural and resting-state functional activity changes occur in the brain by four weeks post-injection, well before α-syn pathology reaches the brain. At 12 weeks post-injection, a separate and distinct pattern of structural and sensory-evoked functional brain activity changes was observed that are co-localized with previously reported regions of α-syn pathology and immune activation. Microstructural changes in the pons at 12 weeks post-injection were found to predict survival time and preceded measurable locomotor deficits. This study provides preliminary evidence for diffusion and fMRI markers linked to the progression of synuclein pathology and has translational importance for understanding synucleinopathies in humans.SIGNIFICANCE STATEMENT α-Synuclein (α-syn) pathology plays a critical role in neurodegenerative diseases such as Parkinson's disease, dementia with Lewy bodies, and multiple system atrophy. The longitudinal effects of α-syn pathology on locomotion, brain microstructure, and functional brain activity are not well understood. Using high field imaging, we show preliminary evidence that peripheral injection of α-syn fibrils induces unique patterns of functional and structural changes that occur at different temporal stages of α-syn pathology progression. Our results challenge existing assumptions that α-syn pathology must precede changes in brain structure and function. Additionally, we show preliminary evidence that diffusion and functional magnetic resonance imaging (fMRI) are capable of resolving such changes and thus should be explored further as markers of disease progression.

The Future of Targeted Gene-Based Treatment Strategies and Biomarkers in Parkinson's Disease

Biomarkers and disease-modifying therapies are both urgent unmet medical needs in the treatment of Parkinson's disease (PD) and must be developed concurrently because of their interdependent relationship: biomarkers for the early detection of disease (i.e., prior to overt neurodegeneration) are necessary in order for patients to receive maximal therapeutic benefit and vice versa; disease-modifying therapies must become available for patients whose potential for disease diagnosis and prognosis can be predicted with biomarkers. This review provides an overview of the milestones achieved to date in the therapeutic strategy development of disease-modifying therapies and biomarkers for PD, with a focus on the most common and advanced genetically linked targets alpha-synuclein (SNCA), leucine-rich repeat kinase-2 (LRRK2) and glucocerebrosidase (GBA1). Furthermore, we discuss the convergence of the different pathways and the importance of patient stratification and how these advances may apply more broadly to idiopathic PD. The heterogeneity of PD poses a challenge for therapeutic and biomarker development, however, the one gene- one target approach has brought us closer than ever before to an unprecedented number of clinical trials and biomarker advancements.

Current Therapeutic Strategies and Perspectives for Neuroprotection in Parkinson's Disease

Parkinson's disease is a progressive neurodegenerative disorder of dopaminergic striatal neurons in basal ganglia. Treatment of Parkinson's disease (PD) through dopamine replacement strategies may provide improvement in early stages and this treatment response is related to dopaminergic neuronal mass which decreases in advanced stages. This treatment failure was revealed by many studies and levodopa treatment became ineffective or toxic in chronic stages of PD. Early diagnosis and neuroprotective agents may be a suitable approach for the treatment of PD. The essentials required for early diagnosis are biomarkers. Characterising the striatal neurons, understanding the status of dopaminergic pathways in different PD stages may reveal the effects of the drugs used in the treatment. This review updates on characterisation of striatal neurons, electrophysiology of dopaminergic pathways in PD, biomarkers of PD, approaches for success of neuroprotective agents in clinical trials. The literature was collected from the articles in database of PubMed, MedLine and other available literature resources.

Distinct Spontaneous Brain Activity Patterns in Different Biologically-Defined Alzheimer's Disease Cognitive Stage: A Preliminary Study

Background: The National Institute on Aging-Alzheimer's Association (NIA-AA) has proposed a biological definition of Alzheimer's disease (AD): individuals with both abnormal amyloid and tau biomarkers (A+T+) would be defined as AD. It remains unclear why different cognitive status is present in subjects with biological AD. Resting-state functional magnetic resonance imaging (rsfMRI) has provided an opportunity to reveal the brain activity patterns in a biologically-defined AD cohort. Accordingly, we aimed to investigate distinct brain activity patterns in subjects with existed AD pathology but in the different cognitive stages. Method: We selected individuals with AD pathology (A+T+) and healthy controls (HC, A-T-) based on the cerebrospinal fluid (CSF) biomarkers. According to the cognitive stage, we divided the A+T+ cohort into three groups: (1) preclinical AD; (2) prodromal AD; and (3) AD with dementia (d-AD). We compared spontaneous brain activity measured by a fractional amplitude of low-frequency fluctuation (fALFF) approach among four groups. Results: The analysis of covariance (ANCOVA) results showed significant differences in fALFF in the posterior cingulate cortex/precuneus (PCC/PCu). Further, compared to HC, we found increased fALFF values in the right inferior frontal gyrus (IFG) in the preclinical AD stage, whereas prodromal AD patients showed reduced fALFF in the bilateral precuneus, right middle frontal gyrus (MFG), right precentral gyrus, and postcentral gyrus. Within the d-AD group, both hyperactivity (right fusiform gyrus, right parahippocampal gyrus (PHG)/hippocampus, and inferior temporal gyrus) and hypoactivity (bilateral precuneus, left posterior cingulate cortex, left cuneus and superior occipital gyrus) were detected. Conclusion: We found the distinct brain activity patterns in different cognitive stages among the subjects defined as AD biologically. Our findings may be helpful in understanding mechanisms leading to cognitive changes in the AD pathophysiological process.

Alterations of regional homogeneity in Parkinson's disease with mild cognitive impairment: a preliminary resting-state fMRI study

PURPOSE

Mild cognitive impairment (MCI) is commonly observed in Parkinson's disease (PD), even in the early stages. However, the neural substrates of cognitive impairment in PD remain unclear. The aim of the current study was to investigate the change of local brain function in PD patients with MCI.

METHODS

Fifty patients with PD, including 25 PD patients with MCI (PD-MCI) and 25 PD patients with normal cognition (PD-NC), and 25 age- and sex-matched healthy controls (HC) were enrolled. Conventional magnetic resonance imaging (MRI), 3D structural images, and resting state-functional MRI (rs-fMRI) were performed in all subjects. Regional homogeneity (ReHo) was measured based on the rs-fMRI images to investigate the altered local brain functions.

RESULTS

Brain regions with decreased ReHo were located in the left posterior cerebellar lobe in PD sub-groups compared to the HC group, and the brain regions with increased ReHo were located in the limbic lobe (right precuneus/bilateral middle cingulate cortex) in PD-MCI compared with HC group. PD-MCI presented with increased ReHo in the bilateral precuneus/left superior parietal lobe and decreased ReHo in the left insula compared to PD-NC. ReHo values for the left precuneus were negatively related to neuropsychological scores, and ReHo values for the left insula were positively related to neuropsychological scores in PD subjects.

CONCLUSION

The present study demonstrated abnormal spontaneous synchrony in the left insula and left precuneus in patients with PD-MCI compared to PD-NC, which might provide a novel insight into the diagnosis and clinical treatment of cognitive impairment in PD.

Long noncoding RNAs in neurodevelopment and Parkinson's disease

Long noncoding RNAs (lncRNAs) are RNA molecules comprising more than 200 nucleotides, which are not translated into proteins. Many studies have shown that lncRNAs are involved in regulating a variety of biological processes, including immune, cancer, stress, development and differentiation at the transcriptional, epigenetic or post-transcriptional levels. Here, we review the role of lncRNAs in the process of neurodevelopment, neural differentiation, synaptic function, and pathogenesis of Parkinson's disease (PD). These pathomechanisms include protein misfolding and aggregation, disordered protein degradation, mitochondrial dysfunction, oxidative stress, autophagy, apoptosis, and neuroinflammation. This information will provide the basis of lncRNA-based disease diagnosis and drug treatment for PD.

Use of Magnetic Resonance Imaging and Artificial Intelligence in Studies of Diagnosis of Parkinson's Disease

Parkinson's disease (PD) is a common neurodegenerative disorder. It has a delitescent onset and a slow progress. The clinical manifestations of PD in patients are highly heterogeneous. Thus, PD diagnosis process is complex and mainly depends on the professional knowledge and experience of the physician. Magnetic resonance imaging (MRI) could detect the small changes in the brain of PD patients, and quantitative analysis of brain MRI may improve the clinical diagnosis efficiency. However, due to the complexity of clinical courses in PD and the high dimensionality in multimodal MRI data, traditional mathematical analysis could not effectively extract the huge information in them. Up to now, the accuracy of PD diagnosis in large sample size is still unsatisfying. As artificial intelligence (AI) is becoming more mature, varieties of statistical models and machine learning (ML) algorithms have been used for quantitative imaging data analysis to explore a diagnostic result. This review aims to state an overview of existing research recently that used statistical ML/AI methods to perform quantitative analysis of MR image data for the study of PD diagnosis. First we review the recent research in three subareas: diagnosis, differential diagnosis, and subtyping of PD. Then we described the overall workflow from MR image to classification result. Finally, we summarized a critical assessment of the current research and provide some recommendations for likely future research developments and trends.

The Effects of SNCA rs894278 on Resting-State Brain Activity in Parkinson's Disease

The pathogenesis of Parkinson's disease (PD) is not well established. The rs894278 polymorphism of SNCA has been associated with PD. We performed this study to investigate the relationship between rs894278 and PD status on resting-state brain activity, by analyzing the amplitude of low-frequency fluctuation (ALFF). A total of 81 PD patients and 64 healthy controls were recruited. Disease severity and PD stage were evaluated in PD patients using the unified Parkinson's disease rating scale (UPDRS) and the Hoehn and Yahr (HY) scale, while the cognitive function of all participants was assessed using the mini-mental state examination (MMSE). All participants were genotyped for the rs894278 SNP and underwent a resting state functional magnetic resonance imaging scan. We found that the ALFF values of PD patients in the lingual gyrus and left caudate were lower than those of HCs; and the ALFF values for the right fusiform of participants with G allele were lower than those of participants without G allele. And we further revealed higher ALFF values in bilateral fusiform in rs894278-G carriers than in rs894278-G non-carriers in the PD group and lower ALFF values in bilateral fusiform in rs894278-G carriers than in rs894278-G non-carriers in the HC group. Our findings show that rs894278 and PD status interactively affect the brain activity of PD patients and HCs, and changes in the brain connectomes may play a key role in the pathogenesis of PD. Thus, our work sheds light on the mechanism underlying PD pathogenesis.

Consistent decreased activity in the putamen in Parkinson's disease: a meta-analysis and an independent validation of resting-state fMRI

Background

Resting-state functional magnetic resonance imaging (RS-fMRI) has frequently been used to investigate local spontaneous brain activity in Parkinson's disease (PD) in a whole-brain, voxel-wise manner. To quantitatively integrate these studies, we conducted a coordinate-based (CB) meta-analysis using the signed differential mapping method on 15 studies that used amplitude of low-frequency fluctuation (ALFF) and 11 studies that used regional homogeneity (ReHo). All ALFF and ReHo studies compared PD patients with healthy controls. We also performed a validation RS-fMRI study of ALFF and ReHo in a frequency-dependent manner for a novel dataset consisting of 49 PD and 49 healthy controls.

Findings

Decreased ALFF was found in the left putamen in PD by meta-analysis. This finding was replicated in our independent validation dataset in the 0.027-0.073 Hz band but not in the conventional frequency band of 0.01-0.08 Hz.

Conclusions

Findings from the current study suggested that decreased ALFF in the putamen of PD patients is the most consistent finding. RS-fMRI is a promising technique for the precise localization of abnormal spontaneous activity in PD. However, more frequency-dependent studies using the same analytical methods are needed to replicate these results. Trial registration: NCT NCT03439163. Registered 20 February 2018, retrospectively registered.

Biomarkers of Parkinson's disease: Striatal sub-regional structural morphometry and diffusion MRI

Parkinson's disease (PD) is a progressive neurological disorder that has no reliable biomarkers. The aim of this study was to explore the potential of semi-automated sub-regional analysis of the striatum with magnetic resonance imaging (MRI) to distinguish PD patients from controls (i.e., as a diagnostic biomarker) and to compare PD patients at different stages of disease.

With 3 Tesla MRI, diffusion- and T1-weighted scans were obtained on two occasions in 24 PD patients and 18 age-matched, healthy controls. PD patients completed one session on and the other session off dopaminergic medication. The striatum was parcellated into seven functionally disparate sub-regions. The segmentation was guided by reciprocal connections to distinct cortical regions. Volume, surface-based morphometry, and integrity of white matter connections were calculated for each striatal sub-region.

Test-retest reliability of our volume, morphometry, and white matter integrity measures across scans was high, with correlations ranging from r = 0.452, p < 0.05 and r = 0.985, p < 0.001. Global measures of striatum such as total striatum, nucleus accumbens, caudate nuclei, and putamen were not significantly different between PD patients and controls, indicating poor sensitivity of these measures, which average across sub-regions that are functionally heterogeneous and differentially affected by PD, to act as diagnostic biomarkers. Further, these measures did not correlate significantly with disease severity, challenging their potential to serve as progression biomarkers. In contrast, a) decreased volume and b) inward surface displacement of caudal-motor striatum—the region first and most dopamine depleted in PD—distinguished PD patients from controls. Integrity of white matter cortico-striatal connections in caudal-motor and adjacent striatal sub-regions (i.e., executive and temporal striatum) was reduced for PD patients relative to controls. Finally, volume of limbic striatum, the only striatal sub-region innervated by the later-degenerating ventral tegmental area in PD, was reduced in later-stage compared to early stage PD patients a potential progression biomarker.

Segmenting striatum based on distinct cortical connectivity provided highly sensitive MRI measures for diagnosing and staging PD.

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Using 3T structural and diffusion tensor MRI, we explored potential biomarkers in PD.

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Striatum was parcellated into 7 functional sub-regions based on cortical connectivity.

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Volume of caudal-motor region was significantly smaller in PDs compared to controls.

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Volume of limbic region was sensitive to PD disease progression.

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Striatal sub-regions provided sensitive measures of the presence and progression of PD.

Recent Advances in Biomarkers for Parkinson's Disease

Parkinson's disease (PD) is one of the common progressive neurodegenerative disorders with several motor and non-motor symptoms. Most of the motor symptoms may appear at a late stage where most of the dopaminergic neurons have been already damaged. In order to provide better clinical intervention and treatment at the onset of disease, it is imperative to find accurate biomarkers for early diagnosis, including prodromal diagnosis and preclinical diagnosis. At the same time, these reliable biomarkers can also be utilized to monitor the progress of the disease. In this review article, we will discuss recent advances in the development of PD biomarkers from different aspects, including clinical, biochemical, neuroimaging and genetic aspects. Although various biomarkers for PD have been developed so far, their specificity and sensitivity are not ideal when applied individually. So, the combination of multimodal biomarkers will greatly improve the diagnostic accuracy and facilitate the implementation of personalized medicine.

Abnormal Spontaneous Brain Activity in Early Parkinson's Disease With Mild Cognitive Impairment: A Resting-State fMRI Study

Mild cognitive impairment (MCI) is a common symptom at the baseline of early Parkinson's disease (PD) diagnosis, but the neural mechanism is unclear. To address the issue, the present study employed resting-state functional magnetic resonance imaging data of 19 drug-naïve PD patients with normal cognition (PD-NC), 10 PD patients with MCI (PD-MCI) and 13 age- and gender-matched healthy controls (HC) from the Parkinson's progression markers initiative (PPMI) (http://www.ppmi-info.org/), and examined abnormal spontaneous brain activities in the PD-MCI. The pattern of spontaneous brain activity was measured by examining the amplitude of low-frequency fluctuations (ALFF) of blood oxygen level dependent signal. Voxel-wise one-way analysis of covariance and post hoc analyses of ALFF were performed under non-parametric permutation tests in a general linear model among the three groups, with age, gender and data center as additional covariates. Statistical significances in the post hoc analysis were corrected by a small volume correction with a cluster-level threshold of p < 0.05 (n = 10000 permutations, FWE-corrected). Correlations of clinical and neuropsychological assessments [i.e., Unified Parkinson's Disease Rating Scale (UPDRS) total score, Montreal Cognitive Assessment (MoCA) and cognitive domains] with the regional ALFF were performed in the PD-MCI group. Compared with the HC, both PD groups exhibited reduced ALFF in the occipital area (Calcarine_R/Cuneus_R). Specially, the PD-MCI group additionally exhibited increased ALFF in the opercular part of right inferior frontal gyrus (Frontal_Inf_Oper_R). Comparing with the PD-NC, the PD-MCI group exhibited significantly higher ALFF in the Frontal_Inf_Oper_R and left fusiform gyus (ps < 0.05). The correlation analysis revealed that the ALFF in the Frontal_Inf_Oper_R was positively correlated with the UPDRS total score (p < 0.05), but marginally negatively correlated with the MoCA score. For cognitive domains, the ALFF in the region also showed a significantly negative correlation with the score of SF test (p < 0.01) and a marginally negative correlation with the score of Symbol-Digit Modalities Test. Together, we concluded hyperactivity in the right inferior frontal gyrus in early PD with MCI, suggesting a compensatory recruitment in response to cognitive decline, which may shed light on thought of dementia progression and potentially comprehensive treatment in PD.

Correlations between CSF proteins and spontaneous neuronal activity in Parkinson's disease

The relationship between cerebrospinal fluid (CSF) proteins and brain function in Parkinson's disease (PD) is not explained clearly. We investigated the correlations between CSF proteins and spontaneous neuronal activity in PD patients via fractional amplitude of low-frequency fluctuation (fALFF) using the Parkinson's Progression Markers Initiative database. Twenty-eight PD patients underwent resting-state functional magnetic resonance imaging in "off" status and lumbar puncture within a month. Correlation analyses between CSF proteins and fALFF value in whole brain as well as clinical assessment scores were performed. We found CSF total tau (t-tau) level was negatively correlated with fALFF in posterior cingulate gyrus. And fALFF in posterior cingulate gyrus was positively correlated with Hopkins Verbal Learning Test-Revised recognition discrimination index. Besides, alpha-synuclein (α-syn) level was correlated with fALFF in bilateral inferior frontal gyrus. This study provides evidence that CSF proteins may have a relationship with brain function related to cognitive status in PD patients.

Altered Functional Brain Connectomes between Sporadic and Familial Parkinson's Patients

Familial Parkinson's disease (PD) is often caused by mutation of a certain gene, while sporadic PD is associated with variants of genes which can influence the susceptibility to PD. The goal of this study was to investigate the difference between the two forms of PD in terms of brain abnormalities using resting-state functional MRI and graph theory. Thirty-one familial PD patients and 36 sporadic PD patients underwent resting-state functional MRI scanning. Frequency-dependent functional connectivity was calculated for each subject using wavelet-based correlations of BOLD signal over 246 brain regions from Brainnetome Atlas. Graph theoretical analysis was then performed to analyze the topology of the functional network, and functional connectome differences were identified with a network-based statistical approach. Our results revealed a frequency-specific (0.016 and 0.031 Hz) connectome difference between familial and sporadic forms of PD, as indicated by an increase in assortativity and decrease in the nodal strength in the left medial amygdala of the familial PD group. In addition, the familial PD patients also showed a distinctive functional network between the left medial amygdala and regions related to retrieval of motion information. The present study indicates that the medial amygdala might be most vulnerable to both sporadic and familial PD. Our findings provide some new insights into disrupted resting-state functional connectomes between sporadic PD and familial PD.