Abnormal metabolic network activity in Parkinson's disease: test-retest reproducibility

PET-based brain molecular connectivity in neurodegenerative disease

PURPOSE OF REVIEW

Molecular imaging has traditionally been used and interpreted primarily in the context of localized and relatively static neurochemical processes. New understanding of brain function and development of novel molecular imaging protocols and analysis methods highlights the relevance of molecular networks that co-exist and interact with functional and structural networks. Although the concept and evidence of disease-specific metabolic brain patterns has existed for some time, only recently has such an approach been applied in the neurotransmitter domain and in the context of multitracer and multimodal studies. This review briefly summarizes initial findings and highlights emerging applications enabled by this new approach.

RECENT FINDINGS

Connectivity based approaches applied to molecular and multimodal imaging have uncovered molecular networks with neurodegeneration-related alterations to metabolism and neurotransmission that uniquely relate to clinical findings; better disease stratification paradigms; an improved understanding of the relationships between neurochemical and functional networks and their related alterations, although the directionality of these relationships are still unresolved; and a new understanding of the molecular underpinning of disease-related alteration in resting-state brain activity.

SUMMARY

Connectivity approaches are poised to greatly enhance the information that can be extracted from molecular imaging. While currently mostly contributing to enhancing understanding of brain function, they are highly likely to contribute to the identification of specific biomarkers that will improve disease management and clinical care.

Deep brain stimulation of the subthalamic nucleus for Parkinson's disease: A network imaging marker of the treatment response

Subthalamic nucleus deep brain stimulation (STN-DBS) alleviates motor symptoms of Parkinson's disease (PD), thereby improving quality of life. However, quantitative brain markers to evaluate DBS responses and select suitable patients for surgery are lacking. Here, we used metabolic brain imaging to identify a reproducible STN-DBS network for which individual expression levels increased with stimulation in proportion to motor benefit. Of note, measurements of network expression from metabolic and BOLD imaging obtained preoperatively predicted motor outcomes determined after DBS surgery. Based on these findings, we computed network expression in 175 PD patients, with time from diagnosis ranging from 0 to 21 years, and used the resulting data to predict the outcome of a potential STN-DBS procedure. While minimal benefit was predicted for patients with early disease, the proportion of potential responders increased after 4 years. Clinically meaningful improvement with stimulation was predicted in 18.9 - 27.3% of patients depending on disease duration.

Reproducibility and Sensitivity of Resting-State fMRI in Patients With Parkinson's Disease Using Cross Validation-Based Data Censoring

BACKGROUND

Uncontrollable body movements are typical symptoms of Parkinson's disease (PD), which results in inconsistent findings regarding resting-state functional connectivity (rsFC) networks, especially for group difference clusters. Systematically identifying the motion-associated data was highly demanded.

PURPOSE

To determine data censoring criteria using a quantitative cross validation-based data censoring (CVDC) method and to improve the detection of rsFC deficits in PD.

STUDY TYPE

Prospective.

SUBJECTS

Forty-one PD patients (68.63 ± 9.17 years, 44% female) and 20 healthy controls (66.83 ± 12.94 years, 55% female).

FIELD STRENGTH/SEQUENCE

3-T, T1-weighted gradient echo and EPI sequences.

ASSESSMENT

Clusters with significant differences between groups were found in three visual networks, default network, and right sensorimotor network. Five-fold cross-validation tests were performed using multiple motion exclusion criteria, and the selected criteria were determined based on cluster sizes, significance values, and Dice coefficients among the cross-validation tests. As a reference method, whole brain rsFC comparisons between groups were analyzed using a FMRIB Software Library (FSL) pipeline with default settings.

STATISTICAL TESTS

Group difference clusters were calculated using nonparametric permutation statistics of FSL-randomize. The family-wise error was corrected. Demographic information was evaluated using independent sample t-tests and Pearson's Chi-squared tests. The level of statistical significance was set at P < 0.05.

RESULTS

With the FSL processing pipeline, the mean Dice coefficient of the network clusters was 0.411, indicating a low reproducibility. With the proposed CVDC method, motion exclusion criteria were determined as frame-wise displacement >0.55 mm. Group-difference clusters showed a mean P-value of 0.01 and a 72% higher mean Dice coefficient compared to the FSL pipeline. Furthermore, the CVDC method was capable of detecting subtle rsFC deficits in the medial sensorimotor network and auditory network that were unobservable using the conventional pipeline.

DATA CONCLUSION

The CVDC method may provide superior sensitivity and improved reproducibility for detecting rsFC deficits in PD.

LEVEL OF EVIDENCE

1 TECHNICAL EFFICACY: Stage 2.

Synaptic loss and its association with symptom severity in Parkinson's disease

Parkinson's disease (PD) is the fastest growing neurodegenerative disease, but at present there is no cure, nor any disease-modifying treatments. Synaptic biomarkers from in vivo imaging have shown promise in imaging loss of synapses in PD and other neurodegenerative disorders. Here, we provide new clinical insights from a cross-sectional, high-resolution positron emission tomography (PET) study of 30 PD individuals and 30 age- and sex-matched healthy controls (HC) with the radiotracer [11C]UCB-J, which binds to synaptic vesicle glycoprotein 2A (SV2A), and is therefore, a biomarker of synaptic density in the living brain. We also examined a measure of relative brain perfusion from the early part of the same PET scan. Our results provide evidence for synaptic density loss in the substantia nigra that had been previously reported, but also extend this to other early-Braak stage regions known to be affected in PD (brainstem, caudate, olfactory cortex). Importantly, we also found a direct association between synaptic density loss in the nigra and severity of symptoms in patients. A greater extent and wider distribution of synaptic density loss in PD patients with longer illness duration suggests that [11C]UCB-J PET can be used to measure synapse loss with disease progression. We also demonstrate lower brain perfusion in PD vs. HC groups, with a greater extent of abnormalities in those with longer duration of illness, suggesting that [11C]UCB-J PET can simultaneously provide information on changes in brain perfusion. These results implicate synaptic imaging as a useful PD biomarker for future disease-modifying interventions.

Hippocampal Perfusion Affects Motor and Cognitive Functions in Parkinson Disease: An Early Phase 18 F-FP-CIT Positron Emission Tomography Study

OBJECTIVE

We investigated whether hippocampal perfusion changes are associated with cognitive decline, motor deficits, and the risk of dementia conversion in patients with Parkinson disease (PD).

METHODS

We recruited patients with newly diagnosed and nonmedicated PD and healthy participants who underwent dual phase 18 F-N-(3-fluoropropyl)-2β-carboxymethoxy-3β-(4-iodophenyl) nortropane positron emission tomography scans. Patients were classified into 3 groups according to hippocampal perfusion measured by standard uptake value ratios (SUVRs): (1) PD hippocampal hypoperfusion group (1 standard deviation [SD] below the mean hippocampal SUVR of healthy controls; PD-hippo-hypo), (2) PD hippocampal hyperperfusion group (1 SD above the mean; PD-hippo-hyper), and (3) the remaining patients (PD-hippo-normal). We compared the baseline cognitive performance, severity of motor deficits, hippocampal volume, striatal dopamine transporter (DAT) availability, and risk of dementia conversion among the groups.

RESULTS

We included 235 patients (PD-hippo-hypo, n = 21; PD-hippo-normal, n = 157; PD-hippo-hyper, n = 57) and 48 healthy participants. Patients in the PD-hippo-hypo group were older and had smaller hippocampal volumes than those in the other PD groups. The PD-hippo-hypo group showed less severely decreased DAT availability in the putamen than the other groups despite similar severities of motor deficit. The PD-hippo-hypo group had a higher risk of dementia conversion compared to the PD-hippo-normal (hazard ratio = 2.59, p = 0.013) and PD-hippo-hyper (hazard ratio = 3.73, p = 0.006) groups, despite similar cognitive performance at initial assessment between groups.

INTERPRETATION

Hippocampal hypoperfusion may indicate a reduced capacity to cope with neurodegenerative processes in terms of the development of motor deficits and cognitive decline in patients with PD. ANN NEUROL 2024;95:388-399.

Is the cingulate island sign a marker for early dementia conversion in Parkinson's disease?

BACKGROUND

To investigate whether the cingulate island sign (CIS) ratio (i.e., the ratio of regional uptake in the posterior cingulate cortex relative to the precuneus and cuneus on cerebral perfusion scans) is associated with early dementia conversion in Parkinson's disease (PD).

METHODS

We enrolled 226 patients with newly diagnosed PD and 48 healthy controls who underwent dual-phase 18 F-FP-CIT PET scans. Patients with PD were classified into three groups according to the CIS ratio on early-phase 18 F-FP-CIT PET images: a PD group with CIS or high CIS ratios (PD-CIS; n = 96), a PD group with inverse CIS or low CIS ratios (PD-iCIS; n = 40), and a PD group consisting of the remaining patients with normal CIS ratios (PD-nCIS; n = 90). We compared the risk of dementia conversion within a 5-year time point between the groups.

RESULTS

There were no significant differences in age, sex, education, or baseline cognitive function between the PD groups. The PD-CIS group had higher Unified Parkinson's Disease Rating Scale (UPDRS) motor scores and more severely decreased dopamine transporter availability in the putamen. The PD-iCIS group had a smaller hippocampal volume compared with the other groups. The risk of dementia conversion in the PD-CIS group did not differ from that in the PD-iCIS and PD-nCIS groups. Meanwhile, the PD-iCIS group had a higher risk of dementia conversion than the PD-nCIS group.

CONCLUSION

The results of this study suggest that inverse CIS, rather than CIS, is relevant to early dementia conversion in patients with PD.

Association between gene expression and functional-metabolic architecture in Parkinson's disease

Gene expression plays a critical role in the pathogenesis of Parkinson's disease (PD). How gene expression profiles are correlated with functional-metabolic architecture remains obscure. We enrolled 34 PD patients and 25 age-and-sex-matched healthy controls for simultaneous 18 F-FDG-PET/functional MRI scanning during resting state. We investigated the functional gradients and the ratio of standard uptake value. Principal component analysis was used to further combine the functional gradients and glucose metabolism into functional-metabolic architecture. Using partial least squares (PLS) regression, we introduced the transcriptomic data from the Allen Institute of Brain Sciences to identify gene expression patterns underlying the affected functional-metabolic architecture in PD. Between-group comparisons revealed significantly higher gradient variation in the visual, somatomotor, dorsal attention, frontoparietal, default mode, and subcortical network (pFDR  < .048) in PD. Increased FDG-uptake was found in the somatomotor and ventral attention network while decreased FDG-uptake was found in the visual network (pFDR  < .008). Spatial correlation analysis showed consistently affected patterns of functional gradients and metabolism (p = 2.47 × 10-8 ). PLS analysis and gene ontological analyses further revealed that genes were mainly enriched for metabolic, catabolic, cellular response to ions, and regulation of DNA transcription and RNA biosynthesis. In conclusion, our study provided genetic pathological mechanism to explain imaging-defined brain functional-metabolic architecture of PD.

The remodeling of metabolic brain pattern in patients with extracranial diffuse large B-cell lymphoma

BACKGROUND

Owing to the advances in diagnosis and therapy, survival or remission rates for lymphoma have improved prominently. Apart from the lymphoma- and chemotherapy-related somatic symptom burden, increasing attention has been drawn to the health-related quality of life. The application of 18F-fluorodeoxyglucose positron emission tomography-computed tomography (18F-FDG PET/CT) has been routinely recommended for the staging and response assessment of FDG-avid lymphoma. However, up till now, only a few researches have investigated the brain metabolic impairments in patients with pre-treatment lymphoma. The determination of the lymphoma-related metabolic brain pattern would facilitate exploring the tailored therapeutic regimen to alleviate not only the physiological, but also the psychological symptoms. In this retrospective study, we aimed to establish the diffuse large B-cell lymphoma-related pattern (DLBCLRP) of metabolic brain network and investigate the correlations between DLBCLRP and several indexes of the staging and response assessment.

RESULTS

The established DLBCLRP was characterized by the increased metabolic activity in bilateral cerebellum, brainstem, thalamus, striatum, hippocampus, amygdala, parahippocampal gyrus and right middle temporal gyrus and by the decreased metabolic activity in bilateral occipital lobe, parietal lobe, anterior cingulate gyrus, midcingulate cortex and medial frontal gyrus. Significant difference in the baseline expression of DLBCLRP was found among complete metabolic response (CMR), partial metabolic response (PMR) and progressive metabolic disease (PMD) groups (P < 0.01). DLBCLRP expressions were also significantly or tended to be positively correlated with international prognostic index (IPI) (rs = 0.306, P < 0.05), lg(total metabolic tumor volume, TMTV) (r = 0.298, P < 0.05) and lg(total lesion glycolysis, TLG) (r = 0.233, P = 0.064). Though no significant correlation of DLBCLRP expression was found with Ann Arbor staging or tumor SUVmax (P > 0.05), the post-treatment declines of DLBCLRP expression were significantly positively correlated with Ann Arbor staging (rs = 0.284, P < 0.05) and IPI (rs = 0.297, P < 0.05).

CONCLUSIONS

The proposed DLBCLRP would lay the foundation for further investigating the cerebral dysfunction related to DLBCL itself and/or treatments. Besides, the expression of DLBCLRP was associated with the tumor burden of lymphoma, implying a potential biomarker for prognosis.

Functional Brain Networks to Evaluate Treatment Responses in Parkinson's Disease

Network analysis of functional brain scans acquired with [18F]-fluorodeoxyglucose positron emission tomography (FDG PET, to map cerebral glucose metabolism), or resting-state functional magnetic resonance imaging (rs-fMRI, to map blood oxygen level-dependent brain activity) has increasingly been used to identify and validate reproducible circuit abnormalities associated with neurodegenerative disorders such as Parkinson's disease (PD). In addition to serving as imaging markers of the underlying disease process, these networks can be used singly or in combination as an adjunct to clinical diagnosis and as a screening tool for therapeutics trials. Disease networks can also be used to measure rates of progression in natural history studies and to assess treatment responses in individual subjects. Recent imaging studies in PD subjects scanned before and after treatment have revealed therapeutic effects beyond the modulation of established disease networks. Rather, other mechanisms of action may be at play, such as the induction of novel functional brain networks directly by treatment. To date, specific treatment-induced networks have been described in association with novel interventions for PD such as subthalamic adeno-associated virus glutamic acid decarboxylase (AAV2-GAD) gene therapy, as well as sham surgery or oral placebo under blinded conditions. Indeed, changes in the expression of these networks with treatment have been found to correlate consistently with clinical outcome. In aggregate, these attributes suggest a role for functional brain networks as biomarkers in future clinical trials.

Differential Implications of Cerebral Hypoperfusion and Hyperperfusion in Parkinson's Disease

BACKGROUND

Patients with Parkinson's disease (PD) exhibit widespread brain perfusion changes.

OBJECTIVE

This study investigated whether cerebral regions with hypoperfusion and hyperperfusion have differential effects on motor and cognitive symptoms in PD using early-phase 18 F-N-(3-fluoropropyl)-2β-carboxymethoxy-3β-(4-iodophenyl) nortropane (18 F-FP-CIT) positron emission tomography (PET) scans.

METHODS

We enrolled 394 patients with newly diagnosed PD who underwent dual-phase 18 F-FP-CIT PET scans. Indices reflecting associated changes in regional cerebral hypoperfusion and hyperperfusion on early-phase 18 F-FP-CIT PET scans were calculated as PD[hypo] and PD[hyper] , respectively. The associations of PD[hypo] and PD[hyper] on motor and cognitive symptoms at baseline were assessed using multivariate linear regression. Also, Cox regression and linear mixed models were performed to investigate the effects of baseline PD[hypo] and PD[hyper] on longitudinal outcomes.

RESULTS

There was a weak correlation between PD[hypo] and PD[hyper] (γ = -0.19, P < 0.001). PD[hypo] was associated with baseline Unified Parkinson's Disease Rating Scale Part III scores (β = -1.02, P = 0.045), rapid increases in dopaminergic medications (β = -18.02, P < 0.001), and a higher risk for developing freezing of gait (hazard ratio [HR] = 0.67, P = 0.019), whereas PD[hyper] was not associated. Regarding cognitive function, PD[hypo] was more relevant to the baseline cognitive performance levels of visuospatial, memory, and frontal/executive function than PD[hyper] . However, greater PD[hyper] was associated with future dementia conversion (HR = 1.43, P = 0.004), whereas PD[hypo] was not associated.

CONCLUSIONS

These findings suggest that PD[hypo] and PD[hyper] may differentially affect motor and cognitive functions in patients with PD. © 2023 International Parkinson and Movement Disorder Society.

Automatic covariance pattern analysis outperforms visual reading of 18 F-fluorodeoxyglucose-positron emission tomography (FDG-PET) in variant progressive supranuclear palsy

BACKGROUND

To date, studies on positron emission tomography (PET) with 18 F-fluorodeoxyglucose (FDG) in progressive supranuclear palsy (PSP) usually included PSP cohorts overrepresenting patients with Richardson's syndrome (PSP-RS).

OBJECTIVES

To evaluate FDG-PET in a patient sample representing the broad phenotypic PSP spectrum typically encountered in routine clinical practice.

METHODS

This retrospective, multicenter study included 41 PSP patients, 21 (51%) with RS and 20 (49%) with non-RS variants of PSP (vPSP), and 46 age-matched healthy controls. Two state-of-the art methods for the interpretation of FDG-PET were compared: visual analysis supported by voxel-based statistical testing (five readers) and automatic covariance pattern analysis using a predefined PSP-related pattern.

RESULTS

Sensitivity and specificity of the majority visual read for the detection of PSP in the whole cohort were 74% and 72%, respectively. The percentage of false-negative cases was 10% in the PSP-RS subsample and 43% in the vPSP subsample. Automatic covariance pattern analysis provided sensitivity and specificity of 93% and 83% in the whole cohort. The percentage of false-negative cases was 0% in the PSP-RS subsample and 15% in the vPSP subsample.

CONCLUSIONS

Visual interpretation of FDG-PET supported by voxel-based testing provides good accuracy for the detection of PSP-RS, but only fair sensitivity for vPSP. Automatic covariance pattern analysis outperforms visual interpretation in the detection of PSP-RS, provides clinically useful sensitivity for vPSP, and reduces the rate of false-positive findings. Thus, pattern expression analysis is clinically useful to complement visual reading and voxel-based testing of FDG-PET in suspected PSP. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Long-term, repeated doses of intravenous autologous mesenchymal stem cells for a patient with Parkinson's disease: a case report

Parkinson's disease (PD) is a neurodegenerative disease that involves the loss of dopaminergic neurons in the substantia nigra pars compacta of the basal ganglia. Clinically, patient presentation involves a combination of motor and non-motor symptoms characterized by progressive worsening over time and significant decreases in overall quality-of-life. Despite there being no fully restorative cure for PD, Mesenchymal Stem Cell (MSC) therapy offers promising therapeutic potential. Here, we report a case of a 77-year-old female, living with idiopathic Parkinson's Disease for over 17 years. The patient received multiple infusions of autologous Hope Biosciences adipose-derived MSCs (HB-adMSCs). A total of 26 infusion treatments of HB-adMSCs were administered over the course of ~2 years that resulted in marked improvements in her typical Parkinsonian symptoms, as demonstrated by the decreases in her UPDRS (Unified Parkinson's Disease Rating Scale) scores. Changes in clinical scores mirrored concurrent changes in regional brain metabolism as quantified by FDG-PET (Fluorodeoxyglucose-Positron Emission Tomography), compared to baseline. Long-term, multiple infusions of HB-adMSCs were safely tolerated by the patient without any serious adverse events. Further research is needed to evaluate the safety and efficacy of HB-adMSC therapy for the treatment of PD patients.

Parkinson's disease progression: Increasing expression of an invariant common core subnetwork

Notable success has been achieved in the study of neurodegenerative conditions using reduction techniques such as principal component analysis (PCA) and sparse inverse covariance estimation (SICE) in positron emission tomography (PET) data despite their widely differing approach. In a recent study of SICE applied to metabolic scans from Parkinson's disease (PD) patients, we showed that by using PCA to prespecify disease-related partition layers, we were able to optimize maps of functional metabolic connectivity within the relevant networks. Here, we show the potential of SICE, enhanced by disease-specific subnetwork partitions, to identify key regional hubs and their connections, and track their associations in PD patients with increasing disease duration. This approach enabled the identification of a core zone that included elements of the striatum, pons, cerebellar vermis, and parietal cortex and provided a deeper understanding of progressive changes in their connectivity. This subnetwork constituted a robust invariant disease feature that was unrelated to phenotype. Mean expression levels for this subnetwork increased steadily in a group of 70 PD patients spanning a range of symptom durations between 1 and 21 years. The findings were confirmed in a validation sample of 69 patients with up to 32 years of symptoms. The common core elements represent possible targets for disease modification, while their connections to external regions may be better suited for symptomatic treatment.

Multimodal imaging study of the 5-HT1A receptor biased agonist, NLX-112, in a model of L-DOPA-induced dyskinesia

INTRODUCTION

The leading treatment for motor signs of Parkinson's disease is L-DOPA, but, upon extended use, it can lead to levodopa-induced dyskinesia (LID). Serotonergic neurons are involved in LID etiology and previous pre-clinical studies have shown that NLX-112, a 5-HT1A biased agonist, has robust antidyskinetic effects. Here, we investigated its effects in hemiparkinsonian (HPK) rats with a unilateral nigrostriatal 6-OHDA lesion.

METHODS

We compared HPK rats with LID (i.e., sensitized to the dyskinetic effects of chronic L-DOPA) and without LID (HPK-non-LID), using [18F]FDG PET imaging and fMRI functional connectivity following systemic treatment with saline, L-DOPA, NLX-112 or L-DOPA + NLX-112.

RESULTS

In HPK-non-LID rats, [18F]FDG PET experiments showed that L-DOPA led to hypermetabolism in motor areas (cerebellum, brainstem, and mesencephalic locomotor region) and to hypometabolism in cortical regions. L-DOPA effects were also observed in HPK-LID rats, with the additional emergence of hypermetabolism in raphe nuclei and hypometabolism in hippocampus and striatum. NLX-112 attenuated L-DOPA-induced raphe hypermetabolism and cingulate cortex hypometabolism in HPK-LID rats. Moreover, in fMRI experiments NLX-112 partially corrected the altered neural circuit connectivity profile in HPK-LID rats, through activity in regions rich in 5-HT1A receptors.

CONCLUSION

This neuroimaging study sheds light for the first time on the brain activation patterns of HPK-LID rats. The 5-HT1A receptor agonist, NLX-112, prevents occurrence of LID, likely by activating pre-synaptic autoreceptors in the raphe nuclei, resulting in a partial restoration of brain metabolic and connectivity profiles. In addition, NLX-112 also rescues L-DOPA-induced deficits in cortical activation, suggesting potential benefit against non-motor symptoms of Parkinson's disease.

The reconfiguration pattern of individual brain metabolic connectome for Parkinson's disease identification

¹⁸F-Fluorodeoxyglucose positron emission tomography (¹⁸F-FDG PET) is widely employed to reveal metabolic abnormalities linked to Parkinson's disease (PD) at a systemic level. However, the individual metabolic connectome details with PD based on ¹⁸F-FDG PET remain largely unknown. To alleviate this issue, we derived a novel brain network estimation method for individual metabolic connectome, that is, Jensen-Shannon Divergence Similarity Estimation (JSSE). Further, intergroup difference between the individual's metabolic brain network and its global/local graph metrics was analyzed to investigate the metabolic connectome's alterations. To further improve the PD diagnosis performance, multiple kernel support vector machine (MKSVM) is conducted for identifying PD from normal control (NC), which combines both topological metrics and connection. Resultantly, PD individuals showed higher nodal topological properties (including assortativity, modularity score, and characteristic path length) than NC individuals, whereas global efficiency and synchronization were lower. Moreover, 45 most significant connections were affected. Further, consensus connections in occipital, parietal, and frontal regions were decrease in PD while increase in subcortical, temporal, and prefrontal regions. The abnormal metabolic network measurements depicted an ideal classification in identifying PD of NC with an accuracy up to 91.84%. The JSSE method identified the individual-level metabolic connectome of ¹⁸F-FDG PET, providing more dimensional and systematic mechanism insights for PD.

The effect of Parkin gene S/N 167 polymorphism on resting spontaneous brain functional activity in Parkinson's Disease

BACKGROUND

Genetic susceptibility plays a significant role in Parkinson's disease (PD) development. Carriers of the Parkin S/N167 mutation may have an increased risk of PD and altered spontaneous brain activity.

OBJECTIVE

This study aims to investigate the potential pathogenesis of PD through a comparative analysis of the amplitude of low-frequency fluctuations (ALFF) in resting-state functional magnetic resonance imaging (rs-fMRI) of subjects with Parkin gene S/N 167 polymorphisms, and to examine the association between spontaneous brain activity and clinical scale scores of PD.

METHODS

A total of 69 PD patients and 84 healthy controls (HC) were included in the study. Each subject was genotyped for the Parkin gene S/N 167 polymorphism and underwent rs-fMRI scans. ALFF analysis was employed to evaluate the relationship among genotypes, interactive brain regions, and clinical symptoms in PD.

RESULTS

PD patients exhibited decreased ALFF values in the right anterior lobe and vermis of the cerebellum compared to HC. No significant interaction was found between the gene's main effect and the "group × genotype" effect on brain ALFF values. One-factor ANOVA revealed no significant difference in ALFF values between PD subgroups; however, the ALFF values in the right anterior lobe and vermis of the cerebellum were lower in the PD-G and PD-GA groups compared to the HC-G and HC-GA groups. Spearman correlation analysis demonstrated that ALFF values in the PD-GG and PD-GA groups were negatively associated with UPDRS-III scores in the bilateral lingual gyrus (Lingual R/L).

CONCLUSION

Parkin gene S/N 167 polymorphisms may influence brain functional activity in specific brain regions, and ALFF values are associated with motor symptoms in PD patients.

Comparison of univariate and multivariate analyses for brain [18F]FDG PET data in α-synucleinopathies

BACKGROUND

Brain imaging with [18F]FDG-PET can support the diagnostic work-up of patients with α-synucleinopathies. Validated data analysis approaches are necessary to evaluate disease-specific brain metabolism patterns in neurodegenerative disorders. This study compared the univariate Statistical Parametric Mapping (SPM) single-subject procedure and the multivariate Scaled Subprofile Model/Principal Component Analysis (SSM/PCA) in a cohort of patients with α-synucleinopathies.

METHODS

We included [18F]FDG-PET scans of 122 subjects within the α-synucleinopathy spectrum: Parkinson's Disease (PD) normal cognition on long-term follow-up (PD - low risk to dementia (LDR); n = 28), PD who developed dementia on clinical follow-up (PD - high risk of dementia (HDR); n = 16), Dementia with Lewy Bodies (DLB; n = 67), and Multiple System Atrophy (MSA; n = 11). We also included [18F]FDG-PET scans of isolated REM sleep behaviour disorder (iRBD; n = 51) subjects with a high risk of developing a manifest α-synucleinopathy. Each [18F]FDG-PET scan was compared with 112 healthy controls using SPM procedures. In the SSM/PCA approach, we computed the individual scores of previously identified patterns for PD, DLB, and MSA: PD-related patterns (PDRP), DLBRP, and MSARP. We used ROC curves to compare the diagnostic performances of SPM t-maps (visual rating) and SSM/PCA individual pattern scores in identifying each clinical condition across the spectrum. Specifically, we used the clinical diagnoses ("gold standard") as our reference in ROC curves to evaluate the accuracy of the two methods. Experts in movement disorders and dementia made all the diagnoses according to the current clinical criteria of each disease (PD, DLB and MSA).

RESULTS

The visual rating of SPM t-maps showed higher performance (AUC: 0.995, specificity: 0.989, sensitivity 1.000) than PDRP z-scores (AUC: 0.818, specificity: 0.734, sensitivity 1.000) in differentiating PD-LDR from other α-synucleinopathies (PD-HDR, DLB and MSA). This result was mainly driven by the ability of SPM t-maps to reveal the limited or absent brain hypometabolism characteristics of PD-LDR. Both SPM t-maps visual rating and SSM/PCA z-scores showed high performance in identifying DLB (DLBRP = AUC: 0.909, specificity: 0.873, sensitivity 0.866; SPM t-maps = AUC: 0.892, specificity: 0.872, sensitivity 0.910) and MSA (MSARP: AUC: 0.921, specificity: 0.811, sensitivity 1.000; SPM t-maps: AUC: 1.000, specificity: 1.000, sensitivity 1.000) from other α-synucleinopathies. PD-HDR and DLB were comparable for the brain hypo and hypermetabolism patterns, thus not allowing differentiation by SPM t-maps or SSM/PCA. Of note, we found a gradual increase of PDRP and DLBRP expression in the continuum from iRBD to PD-HDR and DLB, where the DLB patients had the highest scores. SSM/PCA could differentiate iRBD from DLB, reflecting specifically the differences in disease staging and severity (AUC: 0.938, specificity: 0.821, sensitivity 0.941).

CONCLUSIONS

SPM-single subject maps and SSM/PCA are both valid methods in supporting diagnosis within the α-synucleinopathy spectrum, with different strengths and pitfalls. The former reveals dysfunctional brain topographies at the individual level with high accuracy for all the specific subtype patterns, and particularly also the normal maps; the latter provides a reliable quantification, independent from the rater experience, particularly in tracking the disease severity and staging. Thus, our findings suggest that differences in data analysis approaches exist and should be considered in clinical settings. However, combining both methods might offer the best diagnostic performance.

Principal component analysis of synaptic density measured with [11C]UCB-J PET in early Alzheimer's disease

BACKGROUND

Synaptic loss is considered an early pathological event and major structural correlate of cognitive impairment in Alzheimer's disease (AD). We used principal component analysis (PCA) to identify regional patterns of covariance in synaptic density using [11C]UCB-J PET and assessed the association between principal components (PC) subject scores with cognitive performance.

METHODS

[11C]UCB-J binding was measured in 45 amyloid + participants with AD and 19 amyloid- cognitively normal participants aged 55-85. A validated neuropsychological battery assessed performance across five cognitive domains. PCA was applied to the pooled sample using distribution volume ratios (DVR) standardized (z-scored) by region from 42 bilateral regions of interest (ROI).

RESULTS

Parallel analysis determined three significant PCs explaining 70.2% of the total variance. PC1 was characterized by positive loadings with similar contributions across the majority of ROIs. PC2 was characterized by positive and negative loadings with strongest contributions from subcortical and parietooccipital cortical regions, respectively, while PC3 was characterized by positive and negative loadings with strongest contributions from rostral and caudal cortical regions, respectively. Within the AD group, PC1 subject scores were positively correlated with performance across all cognitive domains (Pearson r = 0.24-0.40, P = 0.06-0.006), PC2 subject scores were inversely correlated with age (Pearson r = -0.45, P = 0.002) and PC3 subject scores were significantly correlated with CDR-sb (Pearson r = 0.46, P = 0.04). No significant correlations were observed between cognitive performance and PC subject scores in CN participants.

CONCLUSIONS

This data-driven approach defined specific spatial patterns of synaptic density correlated with unique participant characteristics within the AD group. Our findings reinforce synaptic density as a robust biomarker of disease presence and severity in the early stages of AD.

Case report: Levodopa-responsive parkinsonism with akinetic mutism after ventriculo-peritoneal shunt

Background

Parkinsonism and akinetic mutism (AM) following ventriculo-peritoneal shunt (VPS) without underdrainage used to be considered rare, but may be underdiagnosed in daily clinical practice. Although the pathophysiology is still unclear, in several case reports, the parkinsonism and AM after VPS shows responsiveness to dopaminergic treatment.

Case presentation

We report a 19-year-old male that presented with severe parkinsonism and AM after VPS. Meanwhile, ¹⁸F-FDG-PET showed a cortical and subcortical hypometabolism. Fortunately, levodopa dramatically improved patient's symptoms and brain hypometabolism. This report provides support for the possibility that dopamine deficiency inhibits brain metabolism, and further elucidates the pathogenesis of parkinsonism and AM.

Conclusion

This report highlights the presentation of a treatable parkinsonism and points out that Levodopa and/or dopamine agonist should be the first choice if the patients develop parkinson-like symptoms after VPS.

Sporadic Creutzfeldt-Jakob disease is associated with reorganization of metabolic connectivity in a pathological brain network

BACKGROUND AND PURPOSE

Although sporadic Creutzfeldt-Jakob disease (sCJD) is a rare cause of dementia, it is critical to understand its functional networks as the prion protein spread throughout the brain may share similar mechanisms with other more common neurodegenerative disorders. In this study, the metabolic brain network associated with sCJD was investigated and its internal network organization was explored.

METHODS

We explored 2-[¹⁸ F]fluoro-2-deoxy-d-glucose positron emission tomography (FDG-PET) brain scans of 29 sCJD patients, 56 normal controls (NCs) and 46 other dementia patients from two independent centers. sCJD-related pattern (CJDRP) was identified in a cohort of 16 pathologically proven sCJD patients and 16 age-matched NCs using scaled subprofile modeling/principal component analysis and was prospectively validated in an independent cohort of 13 sCJD patients and 20 NCs. The pattern's specificity was tested on other dementia patients and its clinical relevance by clinical correlations. The pattern's internal organization was further studied using graph theory methods.

RESULTS

The CJDRP was characterized by relative hypometabolism in the bilateral caudate, thalami, middle and superior frontal gyri, parietal lobe and posterior cingulum in association with relative hypermetabolism in the hippocampi, parahippocampal gyri and cerebellum. The pattern's expression significantly discriminated sCJD from NCs and other dementia patients (p < 0.005; receiver operating characteristic analysis CJD vs. NCs area under the curve [AUC] 0.90-0.96, sCJD vs. Alzheimer's disease AUC 0.78, sCJD vs. behavioral variant of frontotemporal dementia AUC 0.84). The pattern's expression significantly correlated with cognitive, functional decline and disease duration. The metabolic connectivity analysis revealed inefficient information transfer with specific network reorganization.

CONCLUSIONS

The CJDRP is a robust metabolic biomarker of sCJD. Due to its excellent clinical correlations it has the potential to monitor disease in emerging disease-modifying trials.

Right posterior hypometabolism in Pisa syndrome of Parkinson’s disease: A key to explain body schema perception deficit?

BACKGROUND

Pisa syndrome (PS) is a trunk postural abnormality in Parkinson's disease (PD). Its pathophysiology is still debated: peripheral and central mechanisms have been hypothesized.

OBJECTIVE

To investigate the role of nigrostriatal dopaminergic deafferentation and of brain metabolism impairment in the onset PS in PD patients.

METHODS

We retrospectively selected 34 PD patients who developed PS (PS+) and who had previously undergone dopamine transporter (DaT)-SPECT and/or brain F-18 fluorodeoxyglucose PET (FDG-PET). PS + patients were divided considering leaning body side in left ((l)PS+) or right ((r)PS+). DaT-SPECT specific-to-non-displaceable binding ratio (SBR) of striatal regions (BasGan V2 software) were compared between 30 PS+ and 60 PD patients without PS (PS-) as well as between 16 (l)PS+ and 14 (r)PS + patients. Voxel-based analysis (SPM12) was used to compare FDG-PET among 22 PS+, 22 PS- and 42 healthy controls (HC) and between 9 (r)PS+ and 13 (l)PS+.

RESULTS

No significant DaT-SPECT SBR differences were found between PS+ and PS- groups or between (r)PD+ and (l)PS + subgroups. Compared to HC, significant hypometabolism in PS+ was found in bilateral temporal-parietal regions, mainly in the right hemisphere, whereas the right Brodmann area 39 (BA39) was relatively hypometabolic both in the (r)PS+ and in the (l)PS+. BA39 and bilateral posterior cingulate cortex were significantly hypometabolic in PS + than in PS- group.

CONCLUSIONS

As a hub of the network supervising the body schema perception, the involvement of the right posterior hypometabolism supports the hypothesis PS is a result of a somatosensory perceptive deficit rather than a nigrostriatal dopaminergic unbalance.

PET imaging in animal models of Parkinson's disease

Alpha-synucleinopathies, such as Parkinson's disease, dementia with Lewy bodies and multiple system atrophy, are characterized by aberrant accumulation of alpha-synuclein and synaptic dysfunction leading to motor and cognitive deficits. Animal models of alpha-synucleinopathy have greatly facilitated the mechanistic understanding of the disease and the development of therapeutics. Various transgenic, alpha-synuclein fibril-injected, and toxin-injected animal models of Parkinson's disease and multiple system atrophy that recapitulate the disease pathology have been developed and widely used. Recent advances in positron emission tomography have allowed the noninvasive visualization of molecular alterations, underpinning behavioral dysfunctions in the brains of animal models and the longitudinal monitoring of treatment effects. Imaging studies in these disease animal models have employed multi-tracer PET designs to reveal dopaminergic deficits together with other molecular alterations. This review focuses on the development of new positron emission tomography tracers and studies of alpha-synuclein, synaptic vesicle glycoprotein 2A neurotransmitter receptor deficits such as dopaminergic receptor, dopaminergic transporter, serotonergic receptor, vesicular monoamine transporter 2, hypometabolism, neuroinflammation, mitochondrial dysfunction and leucine rich repeat kinase 2 in animal models of Parkinson's disease. The outstanding challenges and emerging applications are outlined, such as investigating the gut-brain-axis by using positron emission tomography in animal models, and provide a future outlook.

Functional brain networks in the evaluation of patients with neurodegenerative disorders

Network analytical tools are increasingly being applied to brain imaging maps of resting metabolic activity (PET) or blood oxygenation-dependent signals (functional MRI) to characterize the abnormal neural circuitry that underlies brain diseases. This approach is particularly valuable for the study of neurodegenerative disorders, which are characterized by stereotyped spread of pathology along discrete neural pathways. Identification and validation of disease-specific brain networks facilitate the quantitative assessment of pathway changes over time and during the course of treatment. Network abnormalities can often be identified before symptom onset and can be used to track disease progression even in the preclinical period. Likewise, network activity can be modulated by treatment and might therefore be used as a marker of efficacy in clinical trials. Finally, early differential diagnosis can be achieved by simultaneously measuring the activity levels of multiple disease networks in an individual patient's scans. Although these techniques were originally developed for PET, over the past several years analogous methods have been introduced for functional MRI, a more accessible non-invasive imaging modality. This advance is expected to broaden the application of network tools to large and diverse patient populations.

Severity-dependent functional connectome and the association with glucose metabolism in the sensorimotor cortex of Parkinson's disease

Functional MRI studies have achieved promising outcomes in revealing abnormal functional connectivity in Parkinson's disease (PD). The primary sensorimotor area (PSMA) received a large amount of attention because it closely correlates with motor deficits. While functional connectivity represents signaling between PSMA and other brain regions, the metabolic mechanism behind PSMA connectivity has rarely been well established. By introducing hybrid PET/MRI scanning, the current study enrolled 33 advanced PD patients during medication-off condition and 25 age-and-sex-matched healthy controls (HCs), aiming to not only identify the abnormal functional connectome pattern of the PSMA, but also to simultaneously investigate how PSMA functional connectome correlates with glucose metabolism. We calculated degree centrality (DC) and the ratio of standard uptake value (SUVr) using resting state fMRI and ¹⁸F-FDG-PET data. A two-sample t-test revealed significantly decreased PSMA DC (P_FWE < 0.014) in PD patients. The PSMA DC also correlated negatively with H-Y stage (P = 0.031). We found a widespread reduction of H-Y stage associated (P-values < 0.041) functional connectivity between PSMA and the visual network, attention network, somatomotor network, limbic network, frontoparietal network as well as the default mode network. The PSMA DC correlated positively with FDG-uptake in the HCs (P = 0.039) but not in the PD patients (P > 0.44). In summary, we identified disease severity-dependent PSMA functional connectome which in addition uncoupled with glucose metabolism in PD patients. The current study highlighted the critical role of simultaneous PET/fMRI in revealing the functional-metabolic mechanism in the PSMA of PD patients.

Simultaneous PET/fMRI revealed increased motor area input to subthalamic nucleus in Parkinson's disease

Invasive electrophysiological recordings in patients with Parkinson's disease (PD) are extremely difficult for cross-sectional comparisons with healthy controls. Noninvasive approaches for identifying information flow between the motor area and the subthalamic nucleus (STN) are critical for evaluation of treatment strategy. We aimed to investigate the direction of the cortical-STN hyperdirect pathway using simultaneous 18F-FDG-PET/functional magnetic resonance imaging (fMRI). Data were acquired during resting state on 34 PD patients and 25 controls. The ratio of standard uptake value for PET images and the STN functional connectivity (FC) maps for fMRI data were generated. The metabolic connectivity mapping (MCM) approach that combines PET and fMRI data was used to evaluate the direction of the connectivity. Results showed that PD patients exhibited both increased FDG uptake and STN-FC in the sensorimotor area (PFDR < 0.05). MCM analysis showed higher cortical-STN MCM value in the PD group (F = 6.63, P = 0.013) in the left precentral gyrus. There was a high spatial overlap between the increased glucose metabolism and increased STN-FC in the sensorimotor area in PD. The MCM approach further revealed an exaggerated cortical input to the STN in PD, supporting the precentral gyrus as a target for treatment such as the repetitive transcranial magnetic stimulation.

The convergent and divergent patterns in brain perfusion between Alzheimer's disease and Parkinson's disease with dementia: An ASL MRI study

Background

Aberrant brain blood perfusion changes have been found to play an important role in the progress of Alzheimer's disease (AD) and Parkinson's disease with dementia (PDD). However, the convergent and divergent patterns in brain perfusion between two dementias remain poorly documented.

Objective

To explore the impaired brain perfusion pattern and investigate their overlaps and differences between AD and PDD using normalized cerebral blood flow (CBF).

Methods

The regional perfusion in patients with AD and PDD as well as healthy control (HC) subjects were explored using the three-dimensional arterial spin labeling. The normalized CBF values were compared across the three groups and further explored the potential linkages to clinical assessments.

Results

In total, 24 patients with AD, 26 patients with PDD, and 35 HC subjects were enrolled. Relative to the HC group, both the AD group and the PDD group showed reduced normalized CBF mainly in regions of the temporal and frontal gyrus, whereas preserved perfusion presented in the sensorimotor cortex and basal ganglia area. Compared with the AD group, the PDD group showed decreased perfusion in the right putamen and right supplementary motor area (SMA), while preserved perfusion in the right inferior parietal lobule (IPL) and right precuneus. In the AD group, significant correlations were observed between the normalized CBF values in the right IPL and scores of global cognitive function (P = 0.033, ρ = 0.442), between the normalized CBF values in the right precuneus and the scores of memory function (P = 0.049,ρ = 0.406). The normalized CBF in the right putamen was significantly linked to cores of motor symptoms (P = 0.017, ρ = 0.214) in the PDD group.

Conclusion

Our findings suggested convergent and divergent patterns of brain hemodynamic dysregulation between AD and PDD and contributed to a better understanding of the pathophysiological mechanisms.

Nigral glucose metabolism as a diagnostic marker of neurodegenerative parkinsonian syndromes

Parkinson’s disease (PD), multiple system atrophy (MSA), and progressive supranuclear palsy (PSP) are characterized by nigrostriatal degeneration. We used [18F]FDG PET to assess glucose metabolism of the substantia nigra (SN) in patients with these diseases and evaluated its ability to discriminate neurodegenerative parkinsonian syndromes (NP) from controls. We retrospectively evaluated [18F]FDG PET scans of 171 patients with NP (n = 115 PD, n = 35 MSA, n = 21 PSP) and 48 controls (13 healthy controls [HC] and 35 control patients). Mean normalized bilateral [18F]FDG uptake in the SN was calculated and compared between groups with covariance and receiver operating characteristic (ROC) analyses (selection of the optimal cut-off required a minimum specificity of 90% to meet the clinical need of a confirmatory test). PD patients were additionally stratified by the expression of the well-established PD-related metabolic pattern (PDRP; elevated expression defined as 2 standard deviations above the mean value of HC). [18F]FDG uptake was significantly lower in NP (Cohen’s d = 1.09, p < 0.001) and its subgroups (PD, d = 1.10, p < 0.001; MSA, d = 0.97, p < 0.001; PSP, d = 1.79, p < 0.001) than in controls. ROC analysis for discriminating NP vs. controls revealed an area under the curve of 0.81 and a sensitivity and specificity of 56 and 92%. Moreover, nigral metabolism was below the cut-off in 60% of PD patients without elevated PDRP expression. Glucose metabolism of the SN can distinguish patients with NP from controls with good diagnostic accuracy and can be used as a marker of nigral degeneration. Its evaluation is particularly valuable in PD patients without elevated PDRP expression and may thus help to narrow the diagnostic gap of [18F]FDG PET in neurodegenerative parkinsonism (i.e., identification of patients with PD without cortical involvement).

Characteristics of very late-onset schizophrenia-like psychosis as prodromal dementia with Lewy bodies: a cross-sectional study

Background

This study aimed to identify cases of potential prodromal DLB in very late-onset schizophrenia-like psychosis (VLOSLP), using indicative biomarkers of dementia with Lewy bodies (DLB), and to evaluate the characteristics of psychosis as prodromal DLB.

Methods

Data of patients with VLOSLP without dementia and Parkinsonism, who underwent testing for at least one indicative biomarker of DLB, were retrospectively collected from the database of the psychiatry clinic at the Osaka University Hospital. Patients were divided into two groups based on the positive (VLOSLP+LB) and negative (VLOSLP–LB) results of the indicative biomarkers of DLB. Age, gender, cognitive battery scores, prevalence of each type of delusions and hallucinations, cerebral volume, and cerebral perfusion were compared between the two groups.

Results

Eleven VLOSLP+LB and 23 VLOSLP–LB participants were enrolled. There were no significant differences in age, proportion of females, and MMSE scores between the two groups. The standardized score of the digit symbol substitution test was significantly lower in the VLOSLP+LB than in VLOSLP–LB group (6.9 [3.1] vs. 10.0 [2.7], p = 0.005). The prevalence of visual hallucinations was significantly higher in the VLOSLP+LB group than in the VLOSLP-LB group (81.8% vs. 26.1%, p = 0.003). Auditory hallucinations were prevalent in both groups (43.5% in VLOSLP–LB, and 45.5% in VLOSLP+LB). Among patients with auditory hallucinations, auditory hallucinations without coexistent visual hallucinations tended to be more prevalent in VLOSLP–LB (7 out of 10) than in VLOSLP+LB patients (1 out of 5). Although cerebral volume was not different in any region, cerebral perfusion in the posterior region, including the occipital lobe, was significantly lower in the VLOSLP+LB group.

Conclusions

Psychomotor slowing, visual hallucinations, and reduced perfusion in the occipital lobe may be suggestive of prodromal DLB in VLOSLP patients, even though the clinical manifestations were similar in many respects between VLOSLP+LB and VLOSLP–LB. Although auditory hallucinations were prevalent in both groups, most patients in VLOSLP+LB complained of auditory hallucinations along with visual hallucinations. Future studies with a larger number of patients without selection bias are desirable.

Consistent Abnormalities in Metabolic Patterns of Lewy Body Dementias

BACKGROUND

Whether dementia with Lewy bodies (DLB) and Parkinson's disease (PD) dementia (PDD) represent the same disease, distinct entities, or conditions within the same spectrum remains controversial.

OBJECTIVE

The objective of this study was to provide new insight into this debate by separately identifying disease-specific metabolic patterns and comparing them with each other and with previously established PD-related pattern (PDRP).

METHODS

Patients with DLB (n = 67), patients with PDD (n = 50), and healthy control subjects (HCs; n = 15) with brain ¹⁸ F-fluorodeoxyglucose positron emission tomography were enrolled as cohorts A and B for pattern identification and validation, respectively. Patients with PD (n = 30) were included for discrimination. Twenty-one participants had two scans. The principal component analysis was applied for pattern identification (DLB-related pattern [DLBRP], PDD-related pattern [PDDRP]). Similarities and differences among three patterns were assessed by pattern topography, pattern expression, clinical correlations cross-sectionally, and pattern expression changes longitudinally.

RESULTS

DLBRP and PDDRP shared highly similar topographies, with relative hypometabolism mainly in the middle temporal gyrus, middle occipital gyrus, lingual gyrus, precuneus, cuneus, angular gyrus, superior and inferior parietal gyrus, middle and inferior frontal gyrus, cingulate, and caudate, and relative hypermetabolism in the cerebellum, putamen, thalamus, precentral/postcentral gyrus, and paracentral lobule, which were more extensive than the PDRP. Patients with DLB and PDD could not be distinguished successfully by any pattern, but patients with PD were easily recognized, especially by DLBRP and PDDRP. The pattern expression of DLBRP and PDDRP showed similar efficacy in cross-sectional disease severity assessment and longitudinal progression monitoring.

CONCLUSIONS

The consistent abnormalities in metabolic patterns of DLB and PDD might underline the potential continuum across the clinical spectrum from PD to DLB. © 2022 International Parkinson and Movement Disorder Society.

Subthalamic Nucleus Modulation of the Pontine Nuclei and Its Targeting of the Cerebellar Cortex

The subthalamic nucleus (STN) has been implicated in motor and nonmotor tasks, and is an effective target of deep brain stimulation for the treatment of Parkinson's disease, likely in part because of the STN's projections outside of the basal ganglia to other brain regions. While there is some evidence of a disynaptic connection between the STN and the cerebellum via the pontine nuclei (PN), how the STN modulates the activity of the neurons in the PN remains unknown. Here we addressed this question using a combination of anatomical tracings, optogenetics, and in vivo electrophysiology in both wild-type (WT) and transgenic mice of both sexes. Approximately half of recorded neurons in the PN, which were located primarily in the medial area, responded with short latency to both single pulses and trains of optogenetic stimulation of channelrhodopsin (ChR2)-expressing STN axons in awake, head-restrained mice. Furthermore, the increase in the activity of PN neurons correlated with the strength of activation of STN axons, suggesting that the STN projections to the PN could, in principle, encode information in a graded manner. In addition, transsynaptic retrograde tracing confirmed that the STN sends disynaptic projections to the cerebellar cortex. These results suggest that the STN sends robust functional projections to the PN, which then propagate to the cerebellum, and have important implications for understanding motor control of normal conditions, and Parkinsonian symptoms, where this pathway may have a role in the therapeutic efficacy of STN deep brain stimulation.SIGNIFICANCE STATEMENT The primary excitatory nucleus in the basal ganglia, the subthalamic nucleus, is known to play a role in pathways modulating movement. The pontine nuclei are the main precerebellar nuclei, which transmit signals through their axonal projections to the cerebellum as mossy fibers. The pathway we have functionally characterized in this paper represents an additional cortex-independent pathway capable of relaying information between the basal ganglia and cerebellum. The effectiveness of subthalamic nucleus deep brain stimulation in Parkinson's disease suggests that this pathway could be explored as an avenue of investigation for therapeutic purposes.

Effects of Bilateral Subthalamic Nucleus Stimulation on Depressive Symptoms and Cerebral Glucose Metabolism in Parkinson's Disease: A 18F-Fluorodeoxyglucose Positron Emission Tomography/Computerized Tomography Study

Subthalamic nucleus (STN) deep brain stimulation (DBS) can improve motor symptoms in Parkinson's disease (PD), as well as potentially improving otherwise intractable comorbid depressive symptoms. To address the latter issue, we evaluated the severity of depressive symptoms along with the severity of motor symptoms in 18 PD patients (mean age, 58.4 ± 5.4 years; 9 males, 9 females; mean PD duration, 9.4 ± 4.4 years) with treatment-resistant depression (TRD) before and after approximately 1 year of STN-DBS treatment. Moreover, to gain more insight into the brain mechanism mediating the therapeutic action of STN-DBS, we utilized ¹⁸F-fluorodeoxyglucose (FDG) positron emission tomography (PET) to assess cerebral regional glucose metabolism in the patients at baseline and 1-year follow-up. Additionally, the baseline PET data from patients were compared with PET data from an age- and sex-matched control group of 16 healthy volunteers. Among them, 12 PD patients underwent post-operative follow-up PET scans. Results showed that the severity of both motor and depressive symptoms in patients with PD-TRD was reduced significantly at 1-year follow-up. Also, patients used significantly less antiparkinsonian medications and antidepressants at 1-year follow-up, as well as experiencing improved daily functioning and a better quality of life. Moreover, relative to the PET data from healthy controls, PD-TRD patients displayed widespread abnormalities in cerebral regional glucose metabolism before STN-DBS treatment, which were partially recovered at 1-year follow-up. Additionally, significant correlations were observed between the patients' improvements in depressive symptoms following STN-DBS and post-operative changes in glucose metabolism in brain regions implicated in emotion regulation. These results support the view that STN-DBS provides a promising treatment option for managing both motor and depressive symptoms in patients who suffer from PD with TRD. However, the results should be interpreted with caution due to the observational nature of the study, small sample size, and relatively short follow-up.

Voxel-based analysis of the metabolic asymmetrical and network patterns in hypermetabolism-associated crossed cerebellar diaschisis

Crossed cerebellar diaschisis (CCD) has been widely investigated in patients with supratentorial hypometabolism, however, the available evidence about the metabolic feature of CCD in patients with contralateral supratentorial hypermetabolism is lacking. This study aimed to assess the metabolic asymmetrical profile, network pattern and predisposing factors for the hypermetabolism-associated CCD, by using voxel-based asymmetry index (AI) and brain network analyses. Seventy CCD positive (CCD+) and 99 CCD negative (CCD-) patients with unilateral supratentorial hypermetabolism were introduced. Among different brain regions with AI_max or AI_min, striatum & thalamus was accompanied by the highest positive rate of CCD (85.7% or 70.1%, respectively). CCD+ group had significantly greater AI_max (median [IQR], 0.62 [0.44-0.84] vs. 0.47 [0.35-0.61]), supratentorial hypermetabolic volume (1183.5 [399.3-3026.8] vs. 386.0 [152.0-1193.0]) and hypometabolic volume (37796.5 [24741.8-53278.0] vs. 3337.0 [1020.0-17193.0]), and lower AI_min (-0.85 [-1.05--0.73] vs. -0.49 [-0.68--0.35]) compared with CCD- group (all P < 0.001). Logistic regression analysis manifested that patients with AI_min located at striatum & thalamus were 16.4 times more likely to present CCD than those at frontal lobe (OR = 16.393; 95% CI, 4.463-60.207; P < 0.001), and the occurrence of CCD was also associated with AI_max (OR = 49.594; 95% CI, 5.519-445.653; P < 0.001) and AI_min (OR = 3.133 × 10^-4, 95% CI, 1.693 × 10^-5-5.799 × 10^-3, P < 0.001). Brain network analysis indicated that the relative hypermetabolism in the contralateral supplementary motor cortex (SMC) and precuneus gyrus were constant in the CCD related patterns. These results demonstrated that the greater AI_max, lower AI_min and AI_min located at striatum & thalamus should be predisposing factors for CCD in patients with unilateral supratentorial hypermetabolism. Relative increased activities in the contralateral SMC and precuneus gyrus might be attributed to a compensatory mechanism for the abnormal brain network related to CCD.

Gait‐Related Metabolic Covariance Networks at Rest in Parkinson's Disease

Background

Gait impairments are characteristic motor manifestations and significant predictors of poor quality of life in Parkinson's disease (PD). Neuroimaging biomarkers for gait impairments in PD could facilitate effective interventions to improve these symptoms and are highly warranted.

Objective

The aim of this study was to identify neural networks of discrete gait impairments in PD.

Methods

Fifty‐five participants with early‐stage PD and 20 age‐matched healthy volunteers underwent quantitative gait assessment deriving 12 discrete spatiotemporal gait characteristics and [¹⁸F]‐2‐fluoro‐2‐deoxyglucose‐positron emission tomography measuring resting cerebral glucose metabolism. A multivariate spatial covariance approach was used to identify metabolic brain networks that were related to discrete gait characteristics in PD.

Results

In PD, we identified two metabolic gait‐related covariance networks. The first correlated with mean step velocity and mean step length (pace gait network), which involved relatively increased and decreased metabolism in frontal cortices, including the dorsolateral prefrontal and orbital frontal, insula, supplementary motor area, ventrolateral thalamus, cerebellum, and cuneus. The second correlated with swing time variability and step time variability (temporal variability gait network), which included relatively increased and decreased metabolism in sensorimotor, superior parietal cortex, basal ganglia, insula, hippocampus, red nucleus, and mediodorsal thalamus. Expression of both networks was significantly elevated in participants with PD relative to healthy volunteers and were not related to levodopa dosage or motor severity.

Conclusions

We have identified two novel gait‐related brain networks of altered glucose metabolism at rest. These gait networks could serve as a potential neuroimaging biomarker of gait impairments in PD and facilitate development of therapeutic strategies for these disabling symptoms. © 2022 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society

A replication study, systematic review and meta-analysis of automated image-based diagnosis in parkinsonism

Differential diagnosis of parkinsonism early upon symptom onset is often challenging for clinicians and stressful for patients. Several neuroimaging methods have been previously evaluated; however specific routines remain to be established. The aim of this study was to systematically assess the diagnostic accuracy of a previously developed ¹⁸F-fluorodeoxyglucose positron emission tomography (FDG-PET) based automated algorithm in the diagnosis of parkinsonian syndromes, including unpublished data from a prospective cohort. A series of 35 patients prospectively recruited in a movement disorder clinic in Stockholm were assessed, followed by systematic literature review and meta-analysis. In our cohort, automated image-based classification method showed excellent sensitivity and specificity for Parkinson Disease (PD) vs. atypical parkinsonian syndromes (APS), in line with the results of the meta-analysis (pooled sensitivity and specificity 0.84; 95% CI 0.79-0.88 and 0.96; 95% CI 0.91 -0.98, respectively). In conclusion, FDG-PET automated analysis has an excellent potential to distinguish between PD and APS early in the disease course and may be a valuable tool in clinical routine as well as in research applications.

Metabolic imaging and plasticity

Positron emission tomography greatly advanced our understanding on the underlying neural mechanisms of movement disorders. PET with flurodeoxyglucose (FDG) is especially useful as it depicts regional metabolic activity level that can predict patients' symptoms. Multivariate pattern analysis has been used to determine and quantify the co-varying brain networks associated with specific clinical traits of neurodegenerative disease. The result is a biomarker, useful for diagnosis, treatments, and follow up studies. Parkinsonian traits and parkinsonisms are associated with specific spatial pattern of metabolic abnormality useful for differential diagnosis. This approach has also been used for monitoring disease progression and novel treatment responses mostly in Parkinson's disease. In this book chapter, we, illustrate and discuss the significance of the brain networks associated with disease and their modification with neuroplastic changes.

Novel Analysis Identifying Functional Connectivity Patterns Associated with Posttraumatic Stress Disorder

Posttraumatic stress disorder (PTSD) is a prevalent psychiatric disorder that can result from experiencing traumatic events. Accurate diagnosis and optimal treatment strategies can be difficult to achieve, due to the heterogeneous etiology and symptomology of PTSD, and overlap with other psychiatric disorders. Advancing our understanding of PTSD pathophysiology is therefore critical. While functional connectivity alterations have shown promise for elucidating the neurobiological mechanisms of PTSD, previous findings have been inconsistent. Eleven patients with PTSD in our first cohort (PTSD-A) and 11 trauma-exposed controls (TEC) underwent functional magnetic resonance imaging. First, we investigated the intrinsic connectivity within known resting state networks (eg, default mode, salience, and central executive networks) previously implicated in functional abnormalities with PTSD symptoms. Second, the overall topology of network structure was compared between PTSD-A and TEC using graph theory. Finally, we used a novel combination of graph theory analysis and scaled subprofile modeling (SSM) to identify a disease-related, covarying pattern of brain network organization. No significant group differences were found in intrinsic connectivity of known resting state networks and graph theory metrics (clustering coefficients, characteristic path length, smallworldness, global and local efficiencies, and degree centrality). The graph theory/SSM analysis revealed a topographical pattern of altered degree centrality differentiating PTSD-A from TEC. This PTSD-related network pattern expression was additionally investigated in a separate cohort of 33 subjects who were scanned with a different MRI scanner (22 patients with PTSD or PTSD-B, and 11 healthy trauma-naïve controls or TNC). Across all participant groups, pattern expression scores were significantly lower in the TEC group, while PTSD-A, PTSD-B, and TNC subject profiles did not differ from each other. Expression level of the pattern was correlated with symptom severity in the PTSD-B group. This method offers potential in developing objective biomarkers associated with PTSD. Possible interpretations and clinical implications will be discussed.

[18F]Nifene PET/CT Imaging in Mice: Improved Methods and Preliminary Studies of α4β2* Nicotinic Acetylcholinergic Receptors in Transgenic A53T Mouse Model of α-Synucleinopathy and Post-Mortem Human Parkinson's Disease

We report [¹⁸F]nifene binding to α4β2* nicotinic acetylcholinergic receptors (nAChRs) in Parkinson’s disease (PD). The study used transgenic Hualpha-Syn(A53T) PD mouse model of α-synucleinopathy for PET/CT studies in vivo and autoradiography in vitro. Additionally, postmortem human PD brain sections comprising of anterior cingulate were used in vitro to assess translation to human studies. Because the small size of mice brain poses challenges for PET imaging, improved methods for radiosynthesis of [¹⁸F]nifene and simplified PET/CT procedures in mice were developed by comparing intravenous (IV) and intraperitoneal (IP) administered [¹⁸F]nifene. An optimal PET/CT imaging time of 30–60 min post injection of [¹⁸F]nifene was established to provide thalamus to cerebellum ratio of 2.5 (with IV) and 2 (with IP). Transgenic Hualpha-Syn(A53T) mice brain slices exhibited 20–35% decrease while in vivo a 20–30% decrease of [¹⁸F]nifene was observed. Lewy bodies and α-synuclein aggregates were confirmed in human PD brain sections which lowered the [¹⁸F]nifene binding by more than 50% in anterior cingulate. Thus [¹⁸F]nifene offers a valuable tool for PET imaging studies of PD.

The Molecular Neuroimaging of Tremor

Purpose of Review

Tremor is a hyperkinetic movement disorder most commonly encountered in essential tremor (ET) and Parkinson’s disease (PD). The purpose of this review is to summarize molecular neuroimaging studies with major implications on pathophysiological and clinical features of tremor.

Recent Findings

Oscillatory brain activity responsible for tremor manifestation is thought to originate in a cerebello-thalamo-cortical network. Molecular neuroimaging has helped clarify metabolic aspects and neurotransmitter influences on the main tremor network. In ET, recent positron emission tomography (PET) studies are built on previous knowledge and highlighted the possibility of investigating metabolic brain changes after treatments, in the attempt to establish therapeutic biomarkers. In PD, molecular neuroimaging has advanced the knowledge of non-dopaminergic determinants of tremor, providing insights into serotonergic and noradrenergic contributions.

Summary

Recent advances have greatly extended the knowledge of tremor pathophysiology and it is now necessary to translate such knowledge in more efficacious treatments for this symptom.

The role of neuroimaging in Parkinson's disease

Parkinson's disease (PD) is a neurodegenerative disorder that affects millions of people worldwide. Two hallmarks of PD are the accumulation of alpha-synuclein and the loss of dopaminergic neurons in the brain. There is no cure for PD, and all existing treatments focus on alleviating the symptoms. PD diagnosis is also based on the symptoms, such as abnormalities of movement, mood, and cognition observed in the patients. Molecular imaging methods such as magnetic resonance imaging (MRI), single-photon emission computed tomography (SPECT), and positron emission tomography (PET) can detect objective alterations in the neurochemical machinery of the brain and help diagnose and study neurodegenerative diseases. This review addresses the application of functional MRI, PET, and SPECT in PD patients. We provide an overview of the imaging targets, discuss the rationale behind target selection, the agents (tracers) with which the imaging can be performed, and the main findings regarding each target's state in PD. Molecular imaging has proven itself effective in supporting clinical diagnosis of PD and has helped reveal that PD is a heterogeneous disorder, which has important implications for the development of future therapies. However, the application of molecular imaging for early diagnosis of PD or for differentiation between PD and atypical parkinsonisms has remained challenging. The final section of the review is dedicated to new imaging targets with which one can detect the PD-related pathological changes upstream from dopaminergic degeneration. The foremost of those targets is alpha-synuclein. We discuss the progress of tracer development achieved so far and challenges on the path toward alpha-synuclein imaging in humans.

Altered cerebral perfusion and microstructure in advanced Parkinson's disease and their associations with clinical features

OBJECTIVE

To explore the whole cerebral perfusion and microstructure alteration patterns in Parkinson's disease (PD) and the associations of these patterns with clinical features.

METHODS

Forty-one subjects [20 PD patients and 21 healthy controls (HCs)] underwent arterial spin labeling (ASL), diffusion tensor imaging (DTI) and 3D T1-weighted imaging (T1WI) MRI. The cerebral blood flow (CBF) of the whole brain and the fractional anisotropy (FA), axial diffusivity (AD), radial diffusivity (RD) and mean diffusivity (MD) of subcortical and cerebellar regions were measured and compared between groups. Pearson's correlation was calculated between MRI measurements and clinical features [Unified Parkinson's Disease Rating Scale (UPDRS), UPDRS III, Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA) and olfactory test scores].

RESULTS

Compared to HCs, PD patients showed lower CBF in the frontal, parietal and temporal lobes but higher CBF in bilateral hippocampi, red nuclei, right substantia nigra, thalamus and most cerebellar regions. The MD in the right thalamus and several regions in the cerebellum increased in PD compared to HCs. In PD patients, the total UPDRS, UPDRS III, MoCA, MMSE and olfactory test scores were related to FA or CBF in cerebellum. (all p < 0.05).

CONCLUSION

Hypoperfusion in cortical regions, together with hyperperfusion in subcortical and cerebellar regions may be the characteristic perfusion pattern in advanced PD patients. The microstructures of the right thalamus and cerebellum were changed in PD patients. The cognitive, motor and olfactory performance of PD patients is closely related to the perfusion and microstructure of the brain, especially the cerebellum.

A disease-specific metabolic imaging marker for diagnosis and progression evaluation of semantic variant primary progressive aphasia

BACKGROUND AND PURPOSE

The diagnosis and monitoring of semantic variant primary progressive aphasia (sv-PPA) are clinically challenging. We aimed to establish a distinctive metabolic pattern in sv-PPA for diagnosis and severity evaluation.

METHODS

Fifteen sv-PPA patients and 15 controls were enrolled to identify sv-PPA-related pattern (sv-PPARP) by principal component analysis of ¹⁸ F-fluorodeoxyglucose positron emission tomography. Eighteen Alzheimer disease dementia (AD) and 14 behavioral variant frontotemporal dementia (bv-FTD) patients were enrolled to test the discriminatory power. Correspondingly, regional metabolic activities extracted from the voxelwise analysis were evaluated for the discriminatory power.

RESULTS

The sv-PPARP was characterized as decreased metabolic activity mainly in the bilateral temporal lobe (left predominance), middle orbitofrontal gyrus, left hippocampus/parahippocampus gyrus, fusiform gyrus, insula, inferior orbitofrontal gyrus, and striatum, with increased activity in the bilateral lingual gyrus, cuneus, calcarine gyrus, and right precentral and postcentral gyrus. The pattern expression had significant discriminatory power (area under the curve [AUC] = 0.98, sensitivity = 100%, specificity = 94.4%) in distinguishing sv-PPA from AD, and the asymmetry index offered complementary discriminatory power (AUC = 0.91, sensitivity = 86.7%, specificity = 92.9%) in distinguishing sv-PPA from bv-FTD. In sv-PPA patients, the pattern expression correlated with Boston Naming Test scores at baseline and showed significant increase in the subset of patients with follow-up. The voxelwise analysis showed similar topography, and the regional metabolic activities had equivalent or better discriminatory power and clinical correlations with Boston Naming Test scores. The ability to reflect disease progression in longitudinal follow-up seemed to be inferior to the pattern expression.

CONCLUSIONS

The sv-PPARP might serve as an objective biomarker for diagnosis and progression evaluation.

[ 18 F]FDG PET/CT Studies in Transgenic Hualpha-Syn (A53T) Parkinson's Disease Mouse Model of α-Synucleinopathy

Transgenic mice line M83 that express the A53T mutant α-synuclein protein at six times the level of endogenous mice α-synuclein are a model of α-synucleinopathy found in Parkinson's disease (PD). This Hualpha-Syn (A53T) PD model is useful in assessing non-motor deficits at earlier stages of onset of PD. We report findings on metabolic changes using [18F]FDG PET/CT in the Hualpha-Syn (A53T) PD mouse model in comparison to non-carrier mice. Whole-body PET/CT imaging of male and female mice were carried out 2 h after [18F]FDG ip administration under 3% isoflurane anesthesia. Brain images were analyzed with PET images coregistered to a mouse brain MRI template. Hualpha-Syn (A53T) mice had significantly lower [18F]FDG uptake in several brain regions compared to the no-carrier mice. Significant hind limb muscle and lower spinal cord [18F]FDG hypometabolism at 9 months of age in A53T PD mice was also indicative of neurodegenerative disease, with a progressive motoric dysfunction leading to death. Significant decrease (up to 30%) in [18F]FDG uptake were observed in 9-month old male and female Hualpha-Syn (A53) mice. This is consistent with the cortical hypometabolism in PD patients. Hualpha-Syn (A53) mice may thus be a suitable model for studies related to PD α-synucleinopathy for the discovery of new biomarkers.

Parkinson Disease-Related Brain Metabolic Patterns and Neurodegeneration in Isolated REM Sleep Behavior Disorder

OBJECTIVE

To elucidate the role of Parkinson disease (PD)-related brain metabolic patterns as a biomarker in isolated REM sleep behavior disorder (iRBD) for future disease conversion.

METHODS

This is a prospective cohort study consisting of 30 patients with iRBD, 25 patients with de novo PD with a premorbid history of RBD, 21 patients with longstanding PD on stable treatment, and 24 healthy controls. The iRBD group was longitudinally followed up. All participants underwent ¹⁸F-fluorodeoxyglucose (FDG) PET and were evaluated with olfaction, cognition, and the Movement Disorders Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS) at baseline. From FDG-PET scans, we derived metabolic patterns from the longstanding PD group (PD-RP) and de novo PD group with RBD (dnPDRBD-RP). Subsequently, we calculated the PD-RP and dnPDRBD-RP scores in patients with iRBD. We validated the metabolic patterns in each PD group and separate iRBD cohort (n = 14).

RESULTS

The 2 patterns significantly correlated with each other and were spatially overlapping yet distinct. The MDS-UPDRS motor scores significantly correlated with PD-RP (p = 0.013) but not with dnPDRBD-RP (p = 0.076). In contrast, dnPDRBD-RP correlated with olfaction in butanol threshold test (p = 0.018) in patients with iRBD, but PD-RP did not (p = 0.21). High dnPDRBD-RP in patients with iRBD predicted future phenoconversion with all cutoff ranges from 1.5 to 3 SD of the control value, whereas predictability of PD-RP was only significant in a partial range of cutoff.

CONCLUSION

The dnPDRBD-RP is an efficient neuroimaging biomarker that reflects prodromal features of PD and predicts phenoconversion in iRBD that can be applied individually.

CLASSIFICATION OF EVIDENCE

This study provides Class IV evidence that a de novo PD pattern on FDG-PET predicts future conversion to neurodegenerative disease in patients with iRBD.

Co-registration Analysis of Fluorodopa and Fluorodeoxyglucose Positron Emission Tomography for Differentiating Multiple System Atrophy Parkinsonism Type From Parkinson's Disease

It is difficult to differentiate between Parkinson's disease and multiple system atrophy parkinsonian subtype (MSA-P) because of the overlap of their signs and symptoms. Enormous efforts have been made to develop positron emission tomography (PET) imaging to differentiate these diseases. This study aimed to investigate the co-registration analysis of ¹⁸F-fluorodopa and ¹⁸F-flurodeoxyglucose PET images to visualize the difference between Parkinson's disease and MSA-P. We enrolled 29 Parkinson's disease patients, 28 MSA-P patients, and 10 healthy controls, who underwent both ¹⁸F-fluorodopa and ¹⁸F-flurodeoxyglucose PET scans. Patients with Parkinson's disease and MSA-P exhibited reduced bilateral striatal ¹⁸F-fluorodopa uptake (p < 0.05, vs. healthy controls). Both regional specific uptake ratio analysis and statistical parametric mapping analysis of ¹⁸F-flurodeoxyglucose PET revealed hypometabolism in the bilateral putamen of MSA-P patients and hypermetabolism in the bilateral putamen of Parkinson's disease patients. There was a significant positive correlation between ¹⁸F-flurodeoxyglucose uptake and ¹⁸F-fluorodopa uptake in the contralateral posterior putamen of MSA-P patients (rs = 0.558, p = 0.002). Both ¹⁸F-flurodeoxyglucose and ¹⁸F-fluorodopa PET images showed that the striatum was rabbit-shaped in the healthy control group segmentation analysis. A defective rabbit-shaped striatum was observed in the ¹⁸F-fluorodopa PET image of patients with Parkinson's disease and MSA-P. In the segmentation analysis of ¹⁸F-flurodeoxyglucose PET image, an intact rabbit-shaped striatum was observed in Parkinson's disease patients, whereas a defective rabbit-shaped striatum was observed in MSA-P patients. These findings suggest that there were significant differences in the co-registration analysis of ¹⁸F-flurodeoxyglucose and ¹⁸F-fluorodopa PET images, which could be used in the individual analysis to differentiate Parkinson's disease from MSA-P.

Neuropathological correlation supports automated image-based differential diagnosis in parkinsonism

PURPOSE

Up to 25% of patients diagnosed as idiopathic Parkinson's disease (IPD) have an atypical parkinsonian syndrome (APS). We had previously validated an automated image-based algorithm to discriminate between IPD, multiple system atrophy (MSA), and progressive supranuclear palsy (PSP). While the algorithm was accurate with respect to the final clinical diagnosis after long-term expert follow-up, its relationship to the initial referral diagnosis and to the neuropathological gold standard is not known.

METHODS

Patients with an uncertain diagnosis of parkinsonism were referred for ¹⁸F-fluorodeoxyglucose (FDG) PET to classify patients as IPD or as APS based on the automated algorithm. Patients were followed by a movement disorder specialist and subsequently underwent neuropathological examination. The image-based classification was compared to the neuropathological diagnosis in 15 patients with parkinsonism.

RESULTS

At the time of referral to PET, the clinical impression was only 66.7% accurate. The algorithm correctly identified 80% of the cases as IPD or APS (p = 0.02) and 87.5% of the APS cases as MSA or PSP (p = 0.03). The final clinical diagnosis was 93.3% accurate (p < 0.001), but needed several years of expert follow-up.

CONCLUSION

The image-based classifications agreed well with autopsy and can help to improve diagnostic accuracy during the period of clinical uncertainty.

Dynamic 18F-FPCIT PET: Quantification of Parkinson's disease metabolic networks and nigrostriatal dopaminergic dysfunction in a single imaging session

Previous multi-center imaging studies with 18F-FDG PET have established the presence of Parkinson's disease motor- and cognition-related metabolic patterns termed PDRP and PDCP in patients with this disorder. Given that in PD cerebral perfusion and glucose metabolism are typically coupled in the absence of medication, we determined whether subject expression of these disease networks can be quantified in early-phase images from dynamic 18F-FPCIT PET scans acquired to assess striatal dopamine transporter (DAT) binding. Methods: We studied a cohort of early-stage PD patients and age-matched healthy control subjects who underwent 18F-FPCIT at baseline; scans were repeated 4 years later in a smaller subset of patients. The early 18F-FPCIT frames, which reflect cerebral perfusion, were used to compute PDRP and PDCP expression (subject scores) in each subject, and compared to analogous measures computed based on 18F-FDG PET scan when additionally available. The late 18F-FPCIT frames were used to measure caudate and putamen DAT binding in the same individuals. Results: PDRP subject scores from early-phase 18F-FPCIT and 18F-FDG scans were elevated and striatal DAT binding reduced in PD versus healthy subjects. The PDRP scores from 18F-FPCIT correlated with clinical motor ratings, disease duration, and with corresponding measures from 18F-FDG PET. In addition to correlating with disease duration and analogous 18F-FDG PET values, PDCP scores correlated with DAT binding in the caudate/anterior putamen. PDRP and PDCP subject scores using either method rose over 4 years whereas striatal DAT binding declined over the same time period. Conclusion: Early-phase images obtained with 18F-FPCIT PET can provide an alternative to 18F-FDG PET for PD network quantification. This technique therefore allows PDRP/PDCP expression and caudate/putamen DAT binding to be evaluated with a single tracer in one scanning session.

Modulating brain networks associated with cognitive deficits in Parkinson's disease

Parkinson’s disease (PD) is a relatively well characterised neurological disorder that primarily affects motor and cognitive functions. This paper reviews on how transcranial direct current stimulation (tDCS) can be used to modulate brain networks associated with cognitive deficits in PD. We first provide an overview of brain network abnormalities in PD, by introducing the brain network modulation approaches such as pharmacological interventions and brain stimulation techniques. We then present the potential underlying mechanisms of tDCS technique, and specifically highlight how tDCS can be applied to modulate brain network abnormality associated with cognitive dysfunction among PD patients. More importantly, we address the limitations of existing studies and suggest possible future directions, with the aim of helping researchers to further develop the use of tDCS technique in clinical settings.

Pretreatment brain volumes can affect the effectiveness of deep brain stimulation in Parkinson's disease patients

We aimed to assess whether brain volumes may affect the results of deep brain stimulation (DBS) in patients with Parkinson’s disease (PD). Eighty-one consecutive patients with PD (male:female 40:41), treated with DBS between June 2012 and December 2017, were enrolled. Total and regional brain volumes were measured using automated brain volumetry (NeuroQuant). The Unified Parkinson Disease Rating Scale motor score quotient was used to assess changes in clinical outcome and compare the preoperative regional brain volume in patients categorized into the higher motor improvement and lower motor improvement groups based on changes in the postoperative scores. The study groups showed significant volume differences in multiple brain areas. In the higher motor improvement group, the anterior cingulate and right thalamus showed high volumes after false discovery rate (FDR) correction. In the lower motor improvement group, the left caudate, paracentral, right primary sensory and left primary motor cortex showed high volume, but no area showed high volumes after FDR correction. Our data suggest that the effectiveness of DBS in patients with PD may be affected by decreased brain volume in different areas, including the cingulate gyrus and thalamus. Preoperative volumetry could help predict outcomes in patients with PD undergoing DBS.

Spectral guided sparse inverse covariance estimation of metabolic networks in Parkinson's disease

In neurodegenerative disorders, a clearer understanding of the underlying aberrant networks facilitates the search for effective therapeutic targets and potential cures. [¹⁸F]-fluorodeoxyglucose (FDG) positron emission tomography (PET) imaging data of brain metabolism reflects the distribution of glucose consumption known to be directly related to neural activity. In FDG PET resting-state metabolic data, characteristic disease-related patterns have been identified in group analysis of various neurodegenerative conditions using principal component analysis of multivariate spatial covariance. Notably, among several parkinsonian syndromes, the identified Parkinson’s disease-related pattern (PDRP) has been repeatedly validated as an imaging biomarker of PD in independent groups worldwide. Although the primary nodal associations of this network are known, its connectivity is not fully understood. Here, we describe a novel approach to elucidate functional principal component (PC) network connections by performing graph theoretical sparse network derivation directly within the disease relevant PC partition layer of the whole brain data rather than by searching for associations retrospectively in whole brain sparse representations. Using sparse inverse covariance estimation of each overlapping PC partition layer separately, a single coherent network is detected for each layer in contrast to more spatially modular segmentation in whole brain data analysis. Using this approach, the major nodal hubs of the PD disease network are identified and their characteristic functional pathways are clearly distinguished within the basal ganglia, midbrain and parietal areas. Network associations are further clarified using Laplacian spectral analysis of the adjacency matrices. In addition, the innate discriminative capacity of the eigenvector centrality of the graph derived networks in differentiating PD versus healthy external data provides evidence of their validity.