Parkinson's disease-related perfusion and glucose metabolic brain patterns identified with PCASL-MRI and FDG-PET imaging

Brain-thyroid crosstalk: 18F-FDG-PET/MRI evidence in patients with follicular thyroid adenomas

OBJECTIVE

The hypothalamic-pituitary-thyroid axis has been well-known. However, whether follicular thyroid adenoma (FTA) could affect brain glucose metabolism is still unknown. Therefore, we explored the brain glucose metabolic characteristics of FTA with Fluorodeoxyglucose F18 positron emission tomography/magnetic resonance imaging.

METHODS

Totally 30 FTA patients without clinical symptoms (FTA group), and 60 age- and sex-matched healthy controls (HC group) were included and randomly divided into cohort A and B in 2:1 ratio. Cohort A was analyzed with scaled sub-profile model/principal component analysis (SSM/PCA) for pattern identification. Cohort B was calculated the individual scores to validate expression of the pattern. Then we calculated the metabolic connectivity based on characteristics of the pattern to investigate the underlying mechanism. Finally, we constructed metabolic brain networks and analyzed the topological properties to further explore the brain metabolic model.

RESULTS

In SSM/PCA, FTA group showed an almost global, left-right symmetrical pattern. In metabolic connectivity, FTA group showed increased metabolic connectivity in brain regions of the sensorimotor network, ventral default mode network (DMN), posterior salient network, right executive control network (ECN), visuospatial network and language network when compared to HC group, and showed decreased connectivity in dorsal DMN and left ECN. In topological properties of brain network, FTA group showed an increased betweenness centrality (BC) in left rolandic operculum, a decreased BC in superior temporal gyrus, increased BC and Degree in right precentral gyrus, increased D in right parahippocampal gyrus and left hippocampus, and decreased D and efficiency in right orbital part of middle frontal gyrus (FDR correction for multiple comparisons, P < 0.05).

CONCLUSION

Although FTA patients are not yet symptomatic, their brain metabolic characteristics include extensive brain alterations, disrupted internal connectivity, not only involving brain regions associated with endocrine activity, but also brain networks and regions associated with motor, emotion and cognition.

Arterial spin labeling MRI based perfusion pattern related to motor dysfunction and L-DOPA reactivity in Parkinson's disease

OBJECTIVE

Identifying intrinsic pattern of Parkinson's disease (PD) helps to better understand of PD and provide insights to disease identification and treatment monitoring. Here we confirmed the PD-related covariance pattern (PDRP) by using arterial spin labelling technology (ASL-PDRP) and explore its potential for predicting motor progression and levodopa (L-DOPA) reactivity reduction.

METHODS

Data from an original cohort of 179 PD and 62 normal controls (NC) and a validation cohort including 36 PD and 19 NC to construct and validate the ASL-PDRP. The correlations between the pattern and motor symptoms were analyzed cross-sectionally and longitudinally (71 PD owned longitudinal data) with hierarchical linear regression analysis. Kaplan-Meier analysis was conducted in 54 L-DOPA-managed PD patients to predict the levodopa reactivity reduction.

RESULTS

The first principal component was predominantly recognized as the ASL-PDRP, with its expression being higher in PD than NC in both sets (original: P = 0.017, AUC = 0.598; validation: P = 0.024, AUC = 0.661). The pattern expression was associated with UPDRS III (P = 0.006) and sub-symptoms (axial: P < 0.001; rigidity: P = 0.003; bradykinesia: P = 0.015) at baseline. The ASL-PDRP could predict the progression of UPDRS III (P = 0.021, β = 4.930). Higher expression of the pattern had slower rate of levodopa reactivity reduction in PD patients with axial symptom (P = 0.031).

CONCLUSION

The identified ASL-PDRP may have potential for characterizing PD with the ability to predict motor progression and L-DOPA reactivity reduction.

Cholinergic Denervation Patterns in Parkinson's Disease Associated With Cognitive Impairment Across Domains

Cognitive impairment is considered to be one of the key features of Parkinson's disease (PD), ultimately resulting in PD-related dementia in approximately 80% of patients over the course of the disease. Several distinct cognitive syndromes of PD have been suggested, driven by different neurotransmitter deficiencies and thus requiring different treatment regimes. In this study, we aimed to identify characteristic brain covariance patterns that reveal how cholinergic denervation is related to PD and to cognitive impairment, focusing on four domains, including attention, executive functioning, memory, and visuospatial cognition. We applied scaled sub-profile model principal component analysis to reveal cholinergic-specific disease-related and cognition-related covariance patterns using [18F]fluoroethoxybenzovesamicol PET imaging. Stepwise logistic regression was applied to predict disease state (PD vs. healthy control). Linear regression models were applied to predict cognitive functioning within the PD group, for each cognitive domain separately. We assessed the performance of the identified patterns with leave-one-out cross validation and performed bootstrapping to assess pattern stability. We included 34 PD patients with various levels of cognitive dysfunction and 10 healthy controls, with similar age, sex, and educational level. The disease-related cholinergic pattern was strongly discriminative (AUC 0.91), and was most prominent in posterior brain regions, with lower tracer uptake in patients compared to controls. We found largely overlapping cholinergic-specific patterns across cognitive domains, with positive correlations between tracer uptake in the opercular cortex, left dorsolateral prefrontal cortex and posterior cingulate gyrus, among other regions, and attention, executive, and visuospatial functioning. Cross validation showed significant correlations between predicted and measured cognition scores, with the exception of memory. We identified a robust structural covariance pattern for the assessment of cholinergic dysfunction related to PD, as well as overlapping cholinergic patterns related to attentional, executive- and visuospatial impairment in PD patients.

Association of Relative Brain Hyperperfusion Independent of Dopamine Depletion With Motor Dysfunction in Patients With Parkinson Disease

BACKGROUND AND OBJECTIVES

Parkinson disease (PD) exhibits a characteristic pattern of brain perfusion or metabolism, thereby being considered network disorder. Using dual-phase N-(3-fluoropropyl)-2β-carbomethoxy-3β-(4-iodophenyl) nortropane (18F-FP-CIT) PET, we investigated the role of brain perfusion in motor symptoms and disease progression, independent of striatal dopamine depletion.

METHODS

We recruited patients with de novo PD and healthy controls (HCs) who underwent dual-phase 18F-FP-CIT PET and brain MRI. All patients underwent the Unified PD Rating Scale (UPDRS) and were followed up for ≥5 years. A subset of patients (n = 51) underwent follow-up UPDRS and brain MRI. Early-phase images evaluated brain perfusion, while delayed-phase images evaluated dopamine transporter availability. We compared early-phase 18F-FP-CIT uptakes (SUVRE) between PD and HC groups. Then, we investigated the association of SVURE and delayed-phase 18F-FP-CIT uptakes (SUVRD) with motor symptoms in PD. Standardized residuals (SRs) of the SUVRE in the hyperperfusion region (SUVRE-HYPER) were obtained from the linear regression of the SUVRD in the posterior putamen (SUVRD-PP), the main region of dopamine deficit. Subsequently, we investigated the association of the SR with baseline and longitudinal motor symptoms and brain atrophy.

RESULTS

Compared with HC (n = 30), patients with PD (n = 168) showed relative hyperperfusion in the primary motor cortex, thalamus, pons, hippocampus, and cerebellum and relative hypoperfusion in the prefrontal and temporo-parieto-occipital cortices, which is consistent with a PD-related metabolic pattern. Motor symptoms were negatively correlated with SUVRD-PP (standardized β = 0.402, p < 0.001) and positively correlated with SUVRE-HYPER (standardized β = 0.292, p < 0.001), but not with SUVRE in the hypoperfusion regions. Regardless of SUVRD-PP, SUVRE-HYPER was independently associated with motor dysfunction, especially rigidity (standardized β = 0.214, p = 0.012). The SR of SUVRE-HYPER was significantly associated with the UPDRS part III total score. Longitudinally, the baseline SR of SUVRE-HYPER was not associated with long-term motor complications but with an increase in the UPDRS part III total score (p = 0.017) and a decrease in brain volume.

DISCUSSION

These results suggest that aberrant relative brain hyperperfusion, independent of striatal dopamine depletion, was associated with baseline and longitudinal motor deficits and progression of neurodegeneration in PD.

Occipital hypoperfusion and motor reserve in Parkinson's disease: an early-phase 18F-FP-CIT PET study

Individual variability exists in parkinsonian motor symptoms despite a similar degree of nigrostriatal dopamine depletion in Parkinson's disease (PD), called motor reserve. We enrolled 397 patients newly diagnosed with PD who underwent dual-phase 18F-FP-CIT PET upon initial assessment. Individual motor reserve was estimated based on initial parkinsonian motor symptoms and striatal dopamine transporter availability using a residual model. Patients with low motor reserve (the lowest quartile group, n = 100) exhibited decreased uptake in the occipital region compared to those with high motor reserve (the highest quartile group, n = 100) on early-phase 18F-FP-CIT PET images. Patients with high motor reserve had a lower risk of conversion to dementia than the those with low motor reserve, whereas the effect of PD groups on the risk of dementia conversion was not mediated by occipital hypoperfusion. These findings suggest that cerebral hypoperfusion in the occipital region is associated with low motor reserve in patients with PD.

Positron emission tomography neuroimaging of [18F]fluorodeoxyglucose uptake and related behavior in the Pink1-/- rat model of Parkinson disease

Introduction

Parkinson disease (PD) is a neurodegenerative condition affecting multiple sensorimotor and cognitive systems. The Pink1-/- rat model exhibits vocal, cognitive, and limb use deficits seen in idiopathic PD. We sought to measure glucose metabolism in brain regions in Pink1-/- and wild type (WT) rats, and to associate these to measures of ultrasonic vocalization, cognition, and limb use behavior.

Methods

Pink1-/- (n = 12) and WT (n = 14) rats were imaged by [18F]fluorodeoxyglucose (FDG) positron emission tomography (PET) in a repeated measures design at approximately 10 months of age and 6 weeks later. Relative regional glucose metabolism was indexed by whole brain normalized FDG uptake, which was calculated for 18 regions identified a priori for comparison. Behavioral measures included tests of communication via ultrasonic vocalization, cognition with 5-Choice Serial Reaction Time Test (5-CSRTT), and limb use with Cylinder Test and Challenge Beam.

Results

Relative glucose metabolism was significantly different in Pink1-/- rats in prelimbic area, striatum, nucleus ambiguus, globus pallidus, and posterior parietal association cortex compared to WT controls. For behavioral measures, Pink1-/- rats demonstrated quieter vocalizations with a restricted frequency range, and they showed increased number of foot-faults and hindlimb steps (shuffling) in limb motor tests. Significant behavior vs. brain correlations included associations of ultrasonic vocalization parameters with glucose metabolism indices in locus coeruleus and substantia nigra.

Conclusion

FDG PET reveals abnormalities in relative regional brain glucose metabolism in Pink1-/- rats in brain regions that are important to cognition, vocalization, and limb motor control that are also impacted by Parkinson disease. This method may be useful for mechanistic studies of behavioral deficits and therapeutic interventions in translational studies in the Pink1-/- PD model.

Individual-specific metabolic network based on 18F-FDG PET revealing multi-level aberrant metabolisms in Parkinson's disease

Metabolic network analysis in Parkinson's disease (PD) based on 18F-FDG PET has revealed PD-related metabolic patterns. However, alterations at the systemic metabolic network level and at the connection level between different brain regions still remain unknown. This study aimed to explore metabolic network alterations at multiple network levels among PD patients using an individual-specific metabolic network (ISMN) approach. 18F-FDG-PET images of patients with PD (n = 34) and healthy subjects (n = 47) were collected. Healthy subjects were further separated into reference group (n = 28) and control group (n = 19) randomly. Standardized uptake value normalized by lean body mass ratio (SULr) maps was calculated from the PET images. ISMNs were constructed based on SULr maps for PD patients and controls with reference to the reference group. Comparisons of nodal and edge features were performed between PD and control groups. Correlation analysis was conducted between multilevel network properties and clinical scales in PD group. A linear classifier was trained based on nodal or edge features to distinguish PD from controls. The distance from each patient's ISMN to the group-level difference network showed a negative correlation with Hoehn and Yahr stage (r = -0.390, p = .023). Eight nodes from ISMN were identified which exhibited significantly increased nodal degree in PD patients compared to controls (p < .05). Eleven edges were observed which demonstrated significant distinctions in Z-score values in comparisons to the control group (p < .05). Furthermore, the nodal and edge features showed comparable performances in PD diagnosis compared to the traditional SULr values, with area under the receiver operating characteristic curve larger than 0.91. The proposed ISMN approach revealed systemic metabolic deviations, as well as nodal and edge distinctions in PD, which might be supplementary to the existing findings on PD-related metabolic patterns.

Parkinson's disease cerebrovascular reactivity pattern: A feasibility study

A mounting body of research points to cerebrovascular dysfunction as a fundamental element in the pathophysiology of Parkinson's disease (PD). In the current feasibility study, blood-oxygen-level-dependent (BOLD) MRI was used to measure cerebrovascular reactivity (CVR) in response to hypercapnia in 26 PD patients and 16 healthy controls (HC), and aimed to find a multivariate pattern specific to PD. Whole-brain maps of CVR amplitude (i.e., magnitude of response to CO2) and latency (i.e., time to reach maximum amplitude) were computed, which were further analyzed using scaled sub-profile model principal component analysis (SSM-PCA) with leave-one-out cross-validation. A meaningful pattern based on CVR latency was identified, which was named the PD CVR pattern (PD-CVRP). This pattern was characterized by relatively increased latency in basal ganglia, sensorimotor cortex, supplementary motor area, thalamus and visual cortex, as well as decreased latency in the cerebral white matter, relative to HC. There were no significant associations with clinical measures, though sample size may have limited our ability to detect significant associations. In summary, the PD-CVRP highlights the importance of cerebrovascular dysfunction in PD, and may be a potential biomarker for future clinical research and practice.

Unsupervised Pattern Analysis to Differentiate Multiple Sclerosis Phenotypes Using Principal Component Analysis on Various MRI Sequences

Background: Multiple sclerosis (MS) has two main phenotypes: relapse-remitting MS (RRMS) and progressive MS (PMS), distinguished by disability profiles and treatment response. Differentiating them using conventional MRI is challenging. Objective: This study explores the use of scaled subprofile modelling using principal component analysis (SSM/PCA) on MRI data to distinguish between MS phenotypes. Methods: MRI scans were performed on patients with RRMS (n = 30) and patients with PMS (n = 20), using the standard sequences T1w, T2w, T2w-FLAIR, and the myelin-sensitive sequences magnetisation transfer (MT) ratio (MTR), quantitative MT (qMT), inhomogeneous MT ratio (ihMTR), and quantitative inhomogeneous MT (qihMT). Results: SSM/PCA analysis of qihMT images best differentiated PMS from RRMS, with the highest specificity (87%) and positive predictive value (PPV) (83%), but a lower sensitivity (67%) and negative predictive value (NPV) (72%). Conversely, T1w data analysis showed the highest sensitivity (93%) and NPV (89%), with a lower PPV (67%) and specificity (53%). Phenotype classification agreement between T1w and qihMT was observed in 57% of patients. In the subset with concordant classifications, the sensitivity, specificity, PPV, and NPV were 100%, 88%, 90%, and 100%, respectively. Conclusions: SSM/PCA on MRI data revealed distinctive patterns for MS phenotypes. Optimal discrimination occurred with qihMT and T1w sequences, with qihMT identifying PMS and T1w identifying RRMS. When qihMT and T1w analyses align, MS phenotype prediction improves.

Acetyl-DL-leucine in two individuals with REM sleep behavior disorder improves symptoms, reverses loss of striatal dopamine-transporter binding and stabilizes pathological metabolic brain pattern-case reports

Isolated REM Sleep Behavior Disorder (iRBD) is considered a prodrome of Parkinson's disease (PD). We investigate whether the potentially disease-modifying compound acetyl-DL-leucine (ADLL; 5 g/d) has an effect on prodromal PD progression in 2 iRBD-patients. Outcome parameters are RBD-severity sum-score (RBD-SS-3), dopamine-transporter single-photon emission computerized tomography (DAT-SPECT) and metabolic "Parkinson-Disease-related-Pattern (PDRP)"-z-score in 18F-fluorodeoxyglucose positron emission tomography (FDG-PET). After 3 weeks ADLL-treatment, the RBD-SS-3 drops markedly in both patients and remains reduced for >18 months of ADLL-treatment. In patient 1 (female), the DAT-SPECT putaminal binding ratio (PBR) decreases in the 5 years pretreatment from normal (1.88) to pathological (1.22) and the patient's FDG-PET-PDRP-z-score rises from 1.72 to 3.28 (pathological). After 22 months of ADLL-treatment, the DAT-SPECT-PBR increases to 1.67 and the FDG-PET-PDRP-z-score stabilizes at 3.18. Similar results are seen in patient 2 (male): his DAT-SPECT-PBR rises from a pretreatment value of 1.42 to 1.72 (close to normal) and the FDG-PET-PDRP-z-score decreases from 1.02 to 0.30 after 18 months of ADLL-treatment. These results support exploration of whether ADLL may have disease-modifying properties in prodromal PD.

Quantifying cerebral blood flow changes using arterial spin labeling: A comparative study of idiopathic rapid eye movement sleep behavior disorder and Parkinson's disease

INTRODUCTION

Idiopathic rapid eye movement sleep behavior disorder (iRBD) and Parkinson's disease (PD) have been found to have changes in cerebral perfusion and overlap of some of the lesioned brain areas. However, a consensus regarding the specific location and diagnostic significance of these cerebral blood perfusion alternations remains elusive in both iRBD and PD. The present study evaluated the patterns of cerebral blood flow changes in iRBD and PD.

MATERIAL AND METHODS

A total of 59 right-handed subjects were enrolled, including 15 patients with iRBD, 20 patients with PD, and 24 healthy controls (HC). They were randomly divided into groups at a ratio of 4 to 1 for training and testing. A PASL sequence was employed to obtain quantitative cerebral blood flow (CBF) maps. The CBF values were calculated from these acquired maps. In addition, AutoGluon was employed to construct a classifier for CBF features selection and classification. An independent t-test was performed for CBF variations, with age and sex as nuisance variables. The performance of the feature was evaluated using receiver operating characteristic (ROC) curves. A significance level of P < 0.05 was considered significant. CBF in several brain regions, including the left median cingulate and paracingulate gyri and the right middle occipital gyrus (MOG), showed significant differences between PD and HC, demonstrating good classification performance. The combined model that integrates all features achieved even higher performance with an AUC of 0.9380. Additionally, CBF values in multiple brain regions, including the right MOG and the left angular gyrus, displayed significant differences between PD and iRBD. Particularly, CBF values in the left angular gyrus exhibited good performance in classifying PD and iRBD. The combined model achieved improved performance, with an AUC of 0.8533. No significant differences were found in brain regions when comparing CBF values between iRBD and HC subjects.

CONCLUSIONS

ASL-based quantitative CBF change features can offer reliable biomarkers to assist in the diagnosis of PD. Regarding the characteristic of CBF in the right MOG, it is anticipated to serve as an imaging biomarker for predicting the progression of iRBD to PD.

Early-phase amyloid PET reproduces metabolic signatures of cognitive decline in Parkinson's disease

INTRODUCTION

Recent work suggests that amyloid beta (Aβ) positron emission tomography (PET) tracer uptake shortly after injection ("early phase") reflects brain metabolism and perfusion. We assessed this modality in a predominantly amyloid-negative neurodegenerative condition, Parkinson's disease (PD), and hypothesized that early-phase 18F-florbetaben (eFBB) uptake would reproduce characteristic hypometabolism and hypoperfusion patterns associated with cognitive decline in PD.

METHODS

One hundred fifteen PD patients across the spectrum of cognitive impairment underwent dual-phase Aβ PET, structural and arterial spin labeling (ASL) magnetic resonance imaging (MRI), and neuropsychological assessments. Multiple linear regression models compared eFBB uptake to cognitive performance and ASL MRI perfusion.

RESULTS

Reduced eFBB uptake was associated with cognitive performance in brain regions previously linked to hypometabolism-associated cognitive decline in PD, independent of amyloid status. Furthermore, eFBB uptake correlated with cerebral perfusion across widespread regions.

DISCUSSION

EFBB uptake is a potential surrogate measure for cerebral perfusion/metabolism. A dual-phase PET imaging approach may serve as a clinical tool for assessing cognitive impairment.

Highlights

Images taken at amyloid beta (Aβ) positron emission tomography tracer injection may reflect brain perfusion and metabolism.Parkinson's disease (PD) is a predominantly amyloid-negative condition.Early-phase florbetaben (eFBB) in PD was associated with cognitive performance.eFBB uptake reflects hypometabolism-related cognitive decline in PD.eFBB correlated with arterial spin labeling magnetic resonance imaging measured cerebral perfusion.eFBB distinguished dementia from normal cognition and mild cognitive impairment.Findings were independent of late-phase Aβ burden.Thus, eFBB may serve as a surrogate measure for brain metabolism/perfusion.

Investigating the aspect of asymmetry in brain-first versus body-first Parkinson's disease

Parkinson's disease (PD) is characterized by a progressive loss of dopaminergic neurons in the substantia nigra. Recent literature has proposed two subgroups of PD. The "body-first subtype" is associated with a prodrome of isolated REM-sleep Behavior Disorder (iRBD) and a relatively symmetric brain degeneration. The "brain-first subtype" is suggested to have a more asymmetric degeneration and a prodromal stage without RBD. This study aims to investigate the proposed difference in symmetry of the degeneration pattern in the presumed body and brain-first PD subtypes. We analyzed 123I-FP-CIT (DAT SPECT) and 18F-FDG PET brain imaging in three groups of patients (iRBD, n = 20, de novo PD with prodromal RBD, n = 22, and de novo PD without RBD, n = 16) and evaluated dopaminergic and glucose metabolic symmetry. The RBD status of all patients was confirmed with video-polysomnography. The PD groups did not differ from each other with regard to the relative or absolute asymmetry of DAT uptake in the putamen (p = 1.0 and p = 0.4, respectively). The patient groups also did not differ from each other with regard to the symmetry of expression of the PD-related metabolic pattern (PDRP) in each hemisphere. The PD groups had no difference in symmetry considering mean FDG uptake in left and right regions of interest and generally had the same degree of symmetry as controls, while the iRBD patients had nine regions with abnormal left-right differences (p < 0.001). Our findings do not support the asymmetry aspect of the "body-first" versus "brain-first" hypothesis.

Clinical Value of Hybrid PET/MR Imaging: Brain Imaging Using PET/MR Imaging

Hybrid PET/MR imaging offers a unique opportunity to acquire MR imaging and PET information during a single imaging session. PET/MR imaging has numerous advantages, including enhanced diagnostic accuracy, improved disease characterization, and better treatment planning and monitoring. It enables the immediate integration of anatomic, functional, and metabolic imaging information, allowing for personalized characterization and monitoring of neurologic diseases. This review presents recent advances in PET/MR imaging and highlights advantages in clinical practice for neuro-oncology, epilepsy, and neurodegenerative disorders. PET/MR imaging provides valuable information about brain tumor metabolism, perfusion, and anatomic features, aiding in accurate delineation, treatment response assessment, and prognostication.

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.

Role of Arterial Spin Labeling (ASL) Images in Parkinson's Disease (PD): A Systematic Review

RATIONALE AND OBJECTIVES

Parkinson's disease is a chronic progressive neurodegenerative disorder with standard structural MRIs often showing no gross abnormalities. Quantitative perfusion MRI modality Arterial Spin Labeling (ASL) is helpful in identifying PD specific perfusion patterns. Absolute Cerebral blood flow (CBF) measurement using ASL provides insights into regional perfusion abnormalities. We reviewed the role of ASL to identify specific brain regions responsible for motor, non-motor symptoms and neurovascular changes observed in PD. Challenges in assessing the blood perfusion level are discussed with future development for improving the evaluation of ASL perfusion maps.

MATERIALS AND METHODS

We included CBF quantification studies using ASL for PD diagnosis. A systematic search was performed in Pubmed, Scopus and Web of Science. The perfusion parameters CBF and arterial arrival time (AAT) measured using ASL were considered for brain region assessment. Clinical aspects of PD have been analyzed using ASL perfusion maps.

RESULTS

The systematic search identified 153 unique records. Thirty articles were selected after verification of inclusion and exclusion criteria. Voxel and region-based analyses in white and gray matter tissues have been performed to identify PD-specific perfusion patterns by reported articles. Predominant brain regions such as basal ganglia sub-regions, frontoparietal network, precuneus, occipital lobe, sensory motor area regions, visual network, which are associated with motor and non-motor symptoms in PD, were identified with CBF hypoperfusion, indicating neuronal loss and cerebrovascular dysfunction.

CONCLUSION

CBF and AAT values derived from ASL can potentially be used as biomarkers to discriminate PD from similar brain-related disorders.

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.

Louis EK, Savica R, Fields JA, Benarroch EE, Lowe V, Petersen RC, Boeve BF, Kantarci K. Brain glucose metabolism and nigrostriatal degeneration in isolated rapid eye movement sleep behaviour disorder

Alterations of cerebral glucose metabolism can be detected in patients with isolated rapid eye movement sleep behaviour disorder, a prodromal feature of neurodegenerative diseases with α-synuclein pathology. However, metabolic characteristics that determine clinical progression in isolated rapid eye movement sleep behaviour disorder and their association with other biomarkers need to be elucidated. We investigated the pattern of cerebral glucose metabolism on 18F-fluorodeoxyglucose PET in patients with isolated rapid eye movement sleep behaviour disorder, differentiating between those who clinically progressed and those who remained stable over time. Second, we studied the association between 18F-fluorodeoxyglucose PET and lower dopamine transporter availability in the putamen, another hallmark of synucleinopathies. Patients with isolated rapid eye movement sleep behaviour disorder from the Mayo Clinic Alzheimer's Disease Research Center and Center for Sleep Medicine (n = 22) and age-and sex-matched clinically unimpaired controls (clinically unimpaired; n = 44) from the Mayo Clinic Study of Aging were included. All participants underwent 18F-fluorodeoxyglucose PET and dopamine transporter imaging with iodine 123-radiolabeled 2β-carbomethoxy-3β-(4-iodophenyl)-N-(3-fluoropropyl) nortropane on single-photon emission computerized tomography. A subset of patients with isolated rapid eye movement sleep behaviour disorder with follow-up evaluations (n = 17) was classified as isolated rapid eye movement sleep behaviour disorder progressors (n = 7) if they developed mild cognitive impairment or Parkinson's disease; or isolated rapid eye movement sleep behaviour disorder stables (n = 10) if they remained with a diagnosis of isolated rapid eye movement sleep behaviour disorder with no cognitive impairment. Glucose metabolic abnormalities in isolated rapid eye movement sleep behaviour disorder were determined by comparing atlas-based regional 18F-fluorodeoxyglucose PET uptake between isolated rapid eye movement sleep behaviour disorder and clinically unimpaired. Associations between 18F-fluorodeoxyglucose PET and dopamine transporter availability in the putamen were analyzed with Pearson's correlation within the nigrostriatal pathway structures and with voxel-based analysis in the cortex. Patients with isolated rapid eye movement sleep behaviour disorder had lower glucose metabolism in the substantia nigra, retrosplenial cortex, angular cortex, and thalamus, and higher metabolism in the amygdala and entorhinal cortex compared with clinically unimpaired. Patients with isolated rapid eye movement sleep behaviour disorder who clinically progressed over time were characterized by higher glucose metabolism in the amygdala and entorhinal cortex, and lower glucose metabolism in the cerebellum compared with clinically unimpaired. Lower dopamine transporter availability in the putamen was associated with higher glucose metabolism in the pallidum within the nigrostriatal pathway; and with higher 18F-fluorodeoxyglucose uptake in the amygdala, insula, and temporal pole on a voxel-based analysis, although these associations did not survive after correcting for multiple comparisons. Our findings suggest that cerebral glucose metabolism in isolated rapid eye movement sleep behaviour disorder is characterized by hypometabolism in regions frequently affected during the prodromal stage of synucleinopathies, potentially reflecting synaptic dysfunction. Hypermetabolism is also seen in isolated rapid eye movement sleep behaviour disorder, suggesting that synaptic metabolic disruptions may be leading to a lack of inhibition, compensatory mechanisms, or microglial activation, especially in regions associated with nigrostriatal degeneration.

FDG-PET combined with learning vector quantization allows classification of neurodegenerative diseases and reveals the trajectory of idiopathic REM sleep behavior disorder

BACKGROUND AND OBJECTIVES

¹⁸F-fluorodeoxyglucose (FDG) positron emission tomography (PET) combined with principal component analysis (PCA) has been applied to identify disease-related brain patterns in neurodegenerative disorders such as Parkinson's disease (PD), Dementia with Lewy Bodies (DLB) and Alzheimer's disease (AD). These patterns are used to quantify functional brain changes at the single subject level. This is especially relevant in determining disease progression in idiopathic REM sleep behavior disorder (iRBD), a prodromal stage of PD and DLB. However, the PCA method is limited in discriminating between neurodegenerative conditions. More advanced machine learning algorithms may provide a solution. In this study, we apply Generalized Matrix Learning Vector Quantization (GMLVQ) to FDG-PET scans of healthy controls, and patients with AD, PD and DLB. Scans of iRBD patients, scanned twice with an approximate 4 year interval, were projected into GMLVQ space to visualize their trajectory.

METHODS

We applied a combination of SSM/PCA and GMLVQ as a classifier on FDG-PET data of healthy controls, AD, DLB, and PD patients. We determined the diagnostic performance by performing a ten times repeated ten fold cross validation. We analyzed the validity of the classification system by inspecting the GMLVQ space. First by the projection of the patients into this space. Second by representing the axis, that span this decision space, into a voxel map. Furthermore, we projected a cohort of RBD patients, whom have been scanned twice (approximately 4 years apart), into the same decision space and visualized their trajectories.

RESULTS

The GMLVQ prototypes, relevance diagonal, and decision space voxel maps showed metabolic patterns that agree with previously identified disease-related brain patterns. The GMLVQ decision space showed a plausible quantification of FDG-PET data. Distance traveled by iRBD subjects through GMLVQ space per year (i.e. velocity) was correlated with the change in motor symptoms per year (Spearman's rho =0.62, P=0.004).

CONCLUSION

In this proof-of-concept study, we show that GMLVQ provides a classification of patients with neurodegenerative disorders, and may be useful in future studies investigating speed of progression in prodromal disease stages.

Brain Glucose Hypometabolism and Iron Accumulation in Different Brain Regions in Alzheimer's and Parkinson's Diseases

The aim of this study was to examine the relationship between the presence of glucose hypometabolism (GHM) and brain iron accumulation (BIA), two potential pathological mechanisms in neurodegenerative disease, in different regions of the brain in people with late-onset Alzheimer's disease (AD) or Parkinson's disease (PD). Studies that conducted fluorodeoxyglucose positron emission tomography (FDG-PET) to map GHM or quantitative susceptibility mapping-magnetic resonance imaging (QSM-MRI) to map BIA in the brains of patients with AD or PD were reviewed. Regions of the brain where GHM or BIA were reported in each disease were compared. In AD, both GHM and BIA were reported in the hippocampus, temporal, and parietal lobes. GHM alone was reported in the cingulate gyrus, precuneus and occipital lobe. BIA alone was reported in the caudate nucleus, putamen and globus pallidus. In PD, both GHM and BIA were reported in thalamus, globus pallidus, putamen, hippocampus, and temporal and frontal lobes. GHM alone was reported in cingulate gyrus, caudate nucleus, cerebellum, and parietal and occipital lobes. BIA alone was reported in the substantia nigra and red nucleus. GHM and BIA are observed independent of one another in various brain regions in both AD and PD. This suggests that GHM is not always necessary or sufficient to cause BIA and vice versa. Hypothesis-driven FDG-PET and QSM-MRI imaging studies, where both are conducted on individuals with AD or PD, are needed to confirm or disprove the observations presented here about the potential relationship or lack thereof between GHM and BIA in AD and PD.

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.

The Contribution of Working Memory Areas to Verbal Learning and Recall in Primary Progressive Aphasia

Recent evidence of domain-specific working memory (WM) systems has identified the areas and networks which are involved in phonological, orthographic, and semantic WM, as well as in higher level domain-general WM functions. The contribution of these areas throughout the process of verbal learning and recall is still unclear. In the present study, we asked, what is the contribution of domain-specific specialized WM systems in the course of verbal learning and recall? To answer this question, we regressed the perfusion data from pseudo-continuous arterial spin labeling (pCASL) MRI with all the immediate, consecutive, and delayed recall stages of the Rey Auditory Verbal Learning Test (RAVLT) from a group of patients with Primary Progressive Aphasia (PPA), a neurodegenerative syndrome in which language is the primary deficit. We found that the early stages of verbal learning involve the areas with subserving phonological processing (left superior temporal gyrus), as well as semantic WM memory (left angular gyrus, AG_L). As learning unfolds, areas with subserving semantic WM (AG_L), as well as lexical/semantic (inferior temporal and fusiform gyri, temporal pole), and episodic memory (hippocampal complex) become more involved. Finally, a delayed recall depends entirely on semantic and episodic memory areas (hippocampal complex, temporal pole, and gyri). Our results suggest that AG_L subserving domain-specific (semantic) WM is involved only during verbal learning, but a delayed recall depends only on medial and cortical temporal areas.

Computer-Aided Classification Framework of Parkinsonian Disorders Using 11C-CFT PET Imaging

Purpose

To investigate the usefulness of a novel computer-aided classification framework for the differential diagnosis of parkinsonian disorders (PDs) based on ¹¹C-methyl-N-2β-carbomethoxy-3β-(4-fluorophenyl)-tropanel (¹¹C-CFT) positron emission tomography (PET) imaging.

Methods

Patients with different forms of PDs—including Parkinson's disease (PD), multiple system atrophy (MSA) and progressive supranuclear palsy (PSP)—underwent dopamine transporter (DAT) imaging with ¹¹C-CFT PET. A novel multistep computer-aided classification framework—consisting of magnetic resonance imaging (MRI)-assisted PET segmentation, feature extraction and prediction, and automatic subject classification—was developed. A random forest method was used to assess the diagnostic relevance of different regions to the classification process. Finally, the performance of the computer-aided classification system was tested using various training strategies involving patients with early and advanced disease stages.

Results

Accuracy values for identifying PD, MSA, and PSP were 85.0, 82.2, and 89.7%, respectively—with an overall accuracy of 80.4%. The caudate and putamen provided the highest diagnostic relevance to the proposed classification framework, whereas the contribution of midbrain was negligible. With the exception of sensitivity for diagnosing PSP, the strategy comprising both early and advanced disease stages performed better in terms of sensitivity, specificity, positive predictive value, and negative predictive value within each PDs subtype.

Conclusions

The proposed computer-aided classification framework based on ¹¹C-CFT PET imaging holds promise for improving the differential diagnosis of PDs.

Machine Learning Identifies Six Genetic Variants and Alterations in the Heart Atrial Appendage as Key Contributors to PD Risk Predictivity

Parkinson's disease (PD) is a complex neurodegenerative disease with a range of causes and clinical presentations. Over 76 genetic loci (comprising 90 SNPs) have been associated with PD by the most recent GWAS meta-analysis. Most of these PD-associated variants are located in non-coding regions of the genome and it is difficult to understand what they are doing and how they contribute to the aetiology of PD. We hypothesised that PD-associated genetic variants modulate disease risk through tissue-specific expression quantitative trait loci (eQTL) effects. We developed and validated a machine learning approach that integrated tissue-specific eQTL data on known PD-associated genetic variants with PD case and control genotypes from the Wellcome Trust Case Control Consortium. In so doing, our analysis ranked the tissue-specific transcription effects for PD-associated genetic variants and estimated their relative contributions to PD risk. We identified roles for SNPs that are connected with INPP5P, CNTN1, GBA and SNCA in PD. Ranking the variants and tissue-specific eQTL effects contributing most to the machine learning model suggested a key role in the risk of developing PD for two variants (rs7617877 and rs6808178) and eQTL associated transcriptional changes of EAF1-AS1 within the heart atrial appendage. Similarly, effects associated with eQTLs located within the Brain Cerebellum were also recognized to confer major PD risk. These findings were replicated in two additional, independent cohorts (the UK Biobank, and NeuroX) and thus warrant further mechanistic investigations to determine if these transcriptional changes could act as early contributors to PD risk and disease development.

Individual interregional perfusion between the left dorsolateral prefrontal cortex stimulation targets and the subgenual anterior cortex predicts response and remission to aiTBS treatment in medication-resistant depression: The influence of behavioral inhibition

BACKGROUND

Accelerated intermittent Theta Burst Stimulation (aiTBS) has been put forward as an effective treatment to alleviate depressive symptoms. Baseline functional connectivity (FC) patterns between the left dorsolateral prefrontal cortex (DLPFC) and the subgenual anterior cortex (sgACC) have gained a lot of attention as a potential biomarker for response. However, arterial spin labeling (ASL) - measuring regional cerebral blood flow - may allow a more straightforward physiological interpretation of such interregional functional connections.

OBJECTIVES

We investigated whether baseline covariance perfusion connectivity between the individually stimulated left DLPFC targets and sgACC could predict meaningful clinical outcome. Considering that individual characteristics may influence efficacy prediction, all patients were also assessed with the Behavioral Inhibition System/Behavioral Activation System (BIS/BAS) scale.

METHODS

After baseline ASL scanning, forty-one medication-resistant depressed patients received twenty sessions of neuronavigated left DLPFC aiTBS in an accelerated sham-controlled crossover fashion, where all stimulation sessions were spread over four days (Trial registration: http://clinicaltrials.gov/show/NCT01832805).

RESULTS

Stronger individual baseline interregional covariance perfusion connectivity patterns predicted response and/or remission. Furthermore, responders and remitters with higher BIS scores displayed stronger baseline interregional perfusion connections.

CONCLUSIONS

Targeting the left DLPFC with aiTBS based on personal structural imaging data only may not be the most optimal method to enhance meaningful antidepressant responses. Individual baseline interregional perfusion connectivity could be an important added brain imaging method for individual optimization of more valid stimulation targets within the left DLPFC. Additional therapies dealing with behavioral inhibition may be warranted.

Imaging modalities in differential diagnosis of Parkinson’s disease: opportunities and challenges

Background

Parkinson’s disease (PD) diagnosis is yet largely based on the related clinical aspects. However, genetics, biomarkers, and neuroimaging studies have demonstrated a confirming role in the diagnosis, and future developments might be used in a pre-symptomatic phase of the disease.

Main text

This review provides an update on the current applications of neuroimaging modalities for PD diagnosis. A literature search was performed to find published studies that were involved on the application of different imaging modalities for PD diagnosis. An organized search of PubMed/MEDLINE, Embase, ProQuest, Scopus, Cochrane, and Google Scholar was performed based on MeSH keywords and suitable synonyms. Two researchers (TM and JPI) independently and separately performed the literature search. Our search strategy in each database was done by the following terms: ((Parkinson [Title/Abstract]) AND ((“Parkinsonian syndromes ”[Mesh]) OR Parkinsonism [Title/Abstract])) AND ((PET [Title/Abstract]) OR “SPECT”[Mesh]) OR ((Functional imaging, Transcranial sonography [Title/Abstract]) OR “Magnetic resonance spectroscopy ”[Mesh]). Database search had no limitation in time, and our last update of search was in February 2021. To have a comprehensive search and to find possible relevant articles, a manual search was conducted on the reference list of the articles and limited to those published in English.

Conclusion

Early diagnosis of PD could be vital for early management and adequate neuroprotection. Recent neuroimaging modalities such as SPECT and PET imaging using radiolabeled tracers, MRI, and CT are used to discover the disease. By the modalities, it is possible to early diagnose dopaminergic degeneration and also to differentiate PD from others parkinsonian syndromes, to monitor the natural progression of the disease and the effect of neuroprotective treatments on the progression. In this regard, functional imaging techniques have provided critical insights and roles on PD.

Brain perfusion magnetic resonance imaging using pseudocontinuous arterial spin labeling in 314 dogs and cats

Background

Arterial spin labeling (ASL) is a noninvasive brain perfusion magnetic resonance imaging (MRI) technique that has not been assessed in clinical veterinary medicine.

Hypothesis/Objectives

To test the feasibility of ASL using a 1.5 Tesla scanner and provide recommendations for optimal quantification of cerebral blood flow (CBF) in dogs and cats.

Animals

Three hundred fourteen prospectively selected client‐owned dogs and cats.

Methods

Each animal underwent brain MRI including morphological sequences and ≥1 ASL sequences using different sites of blood labeling and postlabeling delays (PLD). Calculated ASL success rates were compared. The CBF was quantified in animals that had morphologically normal brain MRI results and parameters of ASL optimization were investigated.

Results

Arterial spin labeling was easily implemented with an overall success rate of 95% in animals with normal brain MRI. Technical recommendations included (a) positioning of the imaging slab at the foramen magnum and (b) selected PLD of 1025 ms in cats and dogs <7 kg, 1525 ms in dogs 7 to 38 kg, and 2025 ms in dogs >38 kg. In 37 dogs, median optimal CBF in the cortex and thalamic nuclei were 114 and 95 mL/100 g/min, respectively. In 28 cats, median CBF in the cortex and thalamic nuclei were 113 and 114 mL/100 g/min, respectively.

Conclusions and Clinical Importance

Our survey of brain perfusion ASL‐MRI demonstrated the feasibility of ASL at 1.5 Tesla, suggested technical recommendations and provided CBF values that should be helpful in the characterization of various brain diseases in dogs and cats.

Impaired cerebral microcirculation in isolated REM sleep behaviour disorder

During the prodromal period of Parkinson's disease and other α-synucleinopathy-related parkinsonisms, neurodegeneration is thought to progressively affect deep brain nuclei, such as the locus coeruleus, caudal raphe nucleus, substantia nigra, and the forebrain nucleus basalis of Meynert. Besides their involvement in the regulation of mood, sleep, behaviour, and memory functions, these nuclei also innervate parenchymal arterioles and capillaries throughout the cortex, possibly to ensure that oxygen supplies are adjusted according to the needs of neural activity. The aim of this study was to examine whether patients with isolated REM sleep behaviour disorder, a parasomnia considered to be a prodromal phenotype of α-synucleinopathies, reveal microvascular flow disturbances consistent with disrupted central blood flow control. We applied dynamic susceptibility contrast MRI to characterize the microscopic distribution of cerebral blood flow in the cortex of 20 polysomnographic-confirmed patients with isolated REM sleep behaviour disorder (17 males, age range: 54-77 years) and 25 healthy matched controls (25 males, age range: 58-76 years). Patients and controls were cognitively tested by Montreal Cognitive Assessment and Mini Mental State Examination. Results revealed profound hypoperfusion and microvascular flow disturbances throughout the cortex in patients compared to controls. In patients, the microvascular flow disturbances were seen in cortical areas associated with language comprehension, visual processing and recognition and were associated with impaired cognitive performance. We conclude that cortical blood flow abnormalities, possibly related to impaired neurogenic control, are present in patients with isolated REM sleep behaviour disorder and associated with cognitive dysfunction. We hypothesize that pharmacological restoration of perivascular neurotransmitter levels could help maintain cognitive function in patients with this prodromal phenotype of parkinsonism.

Neuroimaging evaluation of deep brain stimulation in the treatment of representative neurodegenerative and neuropsychiatric disorders

Brain stimulation technology has become a viable modality of reversible interventions in the effective treatment of many neurological and psychiatric disorders. It is aimed to restore brain dysfunction by the targeted delivery of specific electronic signal within or outside the brain to modulate neural activity on local and circuit levels. Development of therapeutic approaches with brain stimulation goes in tandem with the use of neuroimaging methodology in every step of the way. Indeed, multimodality neuroimaging tools have played important roles in target identification, neurosurgical planning, placement of stimulators and post-operative confirmation. They have also been indispensable in pre-treatment screen to identify potential responders and in post-treatment to assess the modulation of brain circuitry in relation to clinical outcome measures. Studies in patients to date have elucidated novel neurobiological mechanisms underlying the neuropathogenesis, action of stimulations, brain responses and therapeutic efficacy. In this article, we review some applications of deep brain stimulation for the treatment of several diseases in the field of neurology and psychiatry. We highlight how the synergistic combination of brain stimulation and neuroimaging technology is posed to accelerate the development of symptomatic therapies and bring revolutionary advances in the domain of bioelectronic medicine.

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.

Feasibility of pharmacokinetic parametric PET images in scaled subprofile modelling using principal component analysis

Scaled subprofile model using principal component analysis (SSM/PCA) is a multivariate analysis technique used, mainly in [¹⁸F]-2-fluoro-2-deoxy-d-glucose (FDG) PET studies, for the generation of disease-specific metabolic patterns (DP) that may aid with the classification of subjects with neurological disorders, like Alzheimer’s disease (AD). The aim of this study was to explore the feasibility of using quantitative parametric images for this type of analysis, with dynamic [¹¹C]-labelled Pittsburgh Compound B (PIB) PET data as an example. Therefore, 15 AD patients and 15 healthy control subjects were included in an SSM/PCA analysis to generate four AD-DPs using relative cerebral blood flow (R₁), binding potential (BP_ND) and SUVR images derived from dynamic PIB and static FDG-PET studies. Furthermore, 49 new subjects with a variety of neurodegenerative cognitive disorders were tested against these DPs. The AD-DP was characterized by a reduction in the frontal, parietal, and temporal lobes voxel values for R₁ and SUVR-FDG DPs; and by a general increase of values in cortical areas for BP_ND and SUVR-PIB DPs. In conclusion, the results suggest that the combination of parametric images derived from a single dynamic scan might be a good alternative for subject classification instead of using 2 independent PET studies.

Multimodal Evaluation of Neurovascular Functionality in Early Parkinson's Disease

Parkinson's disease (PD) is a multisystem neurological condition affecting different neurotransmitter pathways characterized by aberrant functional connectivity (FC) and perfusion alteration. Since the FC, measuring neuronal activity, and cerebral blood flow (CBF) are closely related through the neurovascular coupling (NVC) mechanism, we aim to assess whether FC changes found in PD mirror perfusion ones. A multimodal MRI study was implemented by acquiring resting state functional MRI (rsfMRI) and arterial spin labeling (ASL) datasets on a group of 26 early PD (66.8 ± 8 years, 22 males, median [interquartile range] Hoehn and Yahr = 1.5 [1]) and 18 age- and sex-matched healthy controls (HCs). In addition, a T1-weighted MPRAGE was also acquired in the same scan session. After a standard preprocessing, resting state networks (RSNs) and CBF maps were extracted from rsfMRI and ASL dataset, respectively. Then, by means of a dual regression algorithm performed on RSNs, a cluster of FC differences between groups was obtained and used to mask CBF maps in the subsequent voxel-wise group comparison. Furthermore, a gray matter (GM) volumetric assessment was performed within the FC cluster in order to exclude tissue atrophy as a source of functional changes. Reduced FC for a PD patient with respect to HC group was found within a sensory-motor network (SMN, p_FWE = 0.01) and visual networks (VNs, primary p_FWE = 0.022 and lateral p_FWE = 0.01). The latter was accompanied by a decreased CBF (primary p_FWE = 0.037, lateral p_FWE = 0.014 VNs), while no GM atrophy was detected instead. The FC alteration found in the SMN of PD might be likely due to a dopaminergic denervation of the striatal pathways causing a functional disconnection. On the other hand, the changes in connectivity depicted in VNs might be related to an altered non-dopaminergic system, since perfusion was also reduced, revealing a compromised NVC. Finally, the absence of GM volume loss might imply that functional changes may potentially anticipate neurodegeneration. In this framework, FC and CBF might be proposed as early functional biomarkers providing meaningful insights in evaluating both disease progression and therapeutic/rehabilitation treatment outcome.

Cerebral blood flow in dystonia due to pantothenate kinase-associated neurodegeneration

BACKGROUND AND PURPOSE

The aim of this study was to look for deviations of cerebral perfusion in patients suffering from pantothenate kinase-associated neurodegeneration, where the globus pallidus is affected by severe accumulation of iron.

MATERIAL AND METHODS

Under resting conditions, cerebral blood flow was measured by the magnetic resonance imaging technique of arterial spin labelling in cortical areas and basal ganglia in eight pantothenate kinase-associated neurodegeneration patients and 14 healthy age-matched control subjects and correlated to T2* time of these areas and - in patients - to clinical parameters.

RESULTS

Despite highly significant differences of T2* time of the globus pallidus (20 vs 39 ms, p < 0.001), perfusion values of this nucleus were nearly identical in both groups (32 ± 3.3 vs 31 ± 4.0 ml/min/100 g) as well as in total brain gray matter (both 62 ± 6.7 resp. ±10.3 ml/min/100 g), putamen (41 ± 5.4 vs 40 ± 6.1 ml/min/100 g), in selected cortical regions, and the cerebellum. Correlations between perfusion and T2* time to clinical data did not reach significance (p > 0.05).

CONCLUSION

The absence of any obvious deviations of perfusion in the group of patients during a resting condition does not support the view that (non-functional) vascular pathology is a major pathogenic factor in pantothenate kinase-associated neurodegeneration in the younger age group. The findings underline the value of the arterial spin technique to measure cerebral blood flow in areas of disturbed susceptibility.

Arterial spin labeling detects perfusion patterns related to motor symptoms in Parkinson's disease

INTRODUCTION

Imaging neurovascular disturbances in Parkinson's disease (PD) is an excellent measure of disease severity. Indeed, a disease-specific regional pattern of abnormal metabolism has been identified using positron emission tomography. Only a handful of studies, however, have applied perfusion MRI to detect this disease pattern. Our goal was to replicate the evaluation of a PD-related perfusion pattern using scaled subprofile modeling/principal component analysis (SSM-PCA).

METHODS

We applied arterial spin labeling (ASL) MRI for this purpose. Uniquely, we assessed this pattern separately in PD individuals ON and OFF dopamine medications. We further compared the existence of these patterns and their strength in each individual with their Movement Disorder Society-Unified Parkinson's Disease Rating Scale motor (MDS-UPDRS) scores, cholinergic tone as indexed by short-term afferent inhibition (SAI), and other neuropsychiatric tests.

RESULTS

We observed a PD-related perfusion pattern that was similar to previous studies. The patterns were observed in both ON and OFF states but only the pattern in the OFF condition could significantly (AUC=0.72) differentiate between PD and healthy subjects. In the ON condition, PD subjects were similar to controls from a CBF standpoint (AUC=0.45). The OFF pattern prominently included the posterior cingulate, precentral region, precuneus, and the subcallosal cortex. Individual principal components from the ON and OFF states were strongly associated with MDS-UPDRS scores, SAI amplitude and latency.

CONCLUSION

Using ASL, our study identified patterns of abnormal perfusion in PD and were associated with disease symptoms.

Brain Glucose Metabolism Heterogeneity in Idiopathic REM Sleep Behavior Disorder and in Parkinson's Disease

BACKGROUND/OBJECTIVE

Idiopathic REM sleep behavior disorder (iRBD) often precedes Parkinson's disease (PD) and other alpha-synucleinopathies. The aim of the study is to investigate brain glucose metabolism of patients with RBD and PD by means of a multidimensional scaling approach, using18F-FDG-PET as a biomarker of synaptic function.

METHODS

Thirty-six iRBD patients (64.1±6.5 y, 32 M), 72 PD patients, and 79 controls (65.6±9.4 y, 53 M) underwent brain 18F-FDG-PET. PD patients were divided according to the absence (PD, 32 subjects; 68.4±8.5 y, 15 M) or presence (PDRBD, 40 subjects; 71.8±6.6 y, 29 M) of RBD. 18F-FDG-PET scans were used to independently discriminate subjects belonging to four categories: controls (RBD no, PD no), iRBD (RBD yes, PD no), PD (RBD no, PD yes) and PDRBD (RBD yes, PD yes).

RESULTS

The discriminant analysis was moderately accurate in identifying the correct category. This is because the model mostly confounds iRBD and PD, thus the intermediate classes. Indeed, iRBD, PD and PDRBD were progressively located at increasing distance from controls and are ordered along a single dimension (principal coordinate analysis) indicating the presence of a single flux of variation encompassing both RBD and PD conditions.

CONCLUSION

Data-driven approach to brain 18F-FDG-PET showed only moderate discrimination between iRBD and PD patients, highlighting brain glucose metabolism heterogeneity among such patients. iRBD should be considered as a marker of an ongoing condition that may be picked-up in different stages across patients and thus express different brain imaging features and likely different clinical trajectories.

Subcortical atrophy and perfusion patterns in Parkinson disease and multiple system atrophy

BACKGROUND

The clinical differentiation between Parkinson disease (PD) and multiple system atrophy (MSA) is difficult.

OBJECTIVES

Arterial spin labeling (ASL) is an advanced MRI technique that obviates the use of an exogenous contrast agent for the estimation of cerebral perfusion. We explored the value of ASL in combination with structural MRI for the differentiation between PD and MSA.

METHODS

Ninety-four subjects (30 PD, 30 MSA and 34 healthy controls) performed a morphometric and ASL-MRI to measure volume and perfusion values within basal ganglia and cerebellum. A region-of-interest analysis was performed to test for structural atrophy and regional blood flow differences between groups.

RESULTS

MSA patients showed higher subcortical atrophy than both PD patients and HC, while no differences were observed between the latter. MSA and PD showed lower volume-corrected perfusion values than HC in several cerebellar areas (Crus I, Crus II, right VIIb, right VIIIa, right VIIIb), right caudate and both thalami. MSA and PD patients displayed similar perfusion values in all aforementioned areas, but the right cerebellar area VIIIb (lower in MSA) and right caudate and both thalami (lower in PD). Similar results were obtained when comparing PD and MSA patients with the parkinsonian variant.

CONCLUSIONS

A perfusion reduction was equally observed in both MSA and PD patients in cerebellar areas that are putatively linked to cognitive (i.e., executive) rather than motor functions. The observed hypo-perfusion could not be explained by atrophy, suggesting the involvement of the cerebellum in the pathophysiology of both MSA and PD.

Direct prospective comparison of 18F-FDG PET and arterial spin labelling MR using simultaneous PET/MR in patients referred for diagnosis of dementia

PURPOSE

¹⁸F-FDG PET is routinely used as an imaging marker in the early and differential diagnosis of dementing disorders and has incremental value over the clinical neurological and neuropsychological evaluation. Perfusion MR imaging by means of arterial spin labelling (ASL) is an alternative modality to indirectly measure neuronal functioning and could be used as complement measurement in a single MR session in the workup of dementia. Using simultaneous PET-MR, we performed a direct head-to-head comparison between enhanced multiplane tagging ASL (eASL) and ¹⁸F-FDG PET in a true clinical context of subjects referred for suspicion of neurodegenerative dementia.

METHODS

Twenty-seven patients underwent a 20-min ¹⁸F-FDG PET/MR and simultaneously acquired eASL on a GE Signa PET/MR. Data were compared with 30 screened age- and gender-matched healthy controls. Both integral eASL and ¹⁸F-FDG datasets were analysed visually by two readers unaware of the final clinical diagnosis, either in normal/abnormal classes, or full differential diagnosis (normal, Alzheimer type dementia [AD], dementia with Lewy Bodies [LBD], frontotemporal dementia [FTD] or other). Reader confidence was assessed with a rating scale (range 1-4). Data were also analysed semiquantitatively by VOI and voxel-based analyses.

RESULTS

The ground truth diagnosis for the patient group resulted in 14 patients with a neurodegenerative cognitive disorder (AD, FTD, LBD) and 13 patients with no arguments for an underlying neurodegenerative cause. Visual analysis resulted in equal specificity (0.70) for differentiating normal and abnormal cases between the two modalities, but in a higher sensitivity (0.93), confidence rating (0.64) and interobserver agreement for ¹⁸F-FDG PET compared with eASL. The same was true for assigning a specific differential diagnosis (sensitivity: and 0.39 for ¹⁸F-FDG PET and eASL, respectively). Semiquantitative analyses revealed prototypical patterns for AD and FTD, with both higher volumes of abnormality and intensity differences on ¹⁸F-FDG PET.

CONCLUSION

In a direct head-to-head comparison on a simultaneous GE Signa PET/MR, ¹⁸F-FDG PET performed better compared with ASL in terms of sensitivity and reader confidence, as well as volume and intensity of abnormalities. However, using pure semiquantitative analysis, similar diagnostic accuracy between the two modalities was obtained. Therefore, ASL may still serve as complement to neuroreceptor or protein deposition PET studies when a single simultaneous investigation is warranted.

Cortical hypoperfusion in patients with idiopathic rapid eye movement sleep behavior disorder detected with arterial spin-labeled perfusion MRI

BACKGROUND

Idiopathic rapid eye movement sleep behavior disorder (iRBD) is an important risk factor for α-synucleinopathy.

OBJECTIVE

We investigated alterations in the cerebral blood flow (CBF) based on arterial spin-labeled (ASL) imaging in patients with iRBD to determine brain perfusion changes associated with the disorder.

METHODS

Fifteen patients with iRBD and twenty age-gender-matched healthy controls were enrolled. Cortical perfusions were compared between the two groups after the ASL data was co-registered to the high-resolution T1-weighted images.

RESULTS

No significant differences were detected between the groups in regard to age, gender, education, or UPDRS-III score. The iRBD group showed a lower MMSE score than the healthy controls (27.07 ± 2.25 vs. 28.55 ± 1.23, p < 0.05). Compared with the healthy controls, the iRBD group showed significantly decreased CBF values in the right inferior frontal gyrus, right middle frontal gyrus, and right insula (p < 0.05 corrected).

CONCLUSION

The cortical hypoperfusion areas in patients with iRBD were similar to the patterns in patients with α -synucleinopathies. ASL perfusion MRI is a potential approach to find biomarkers in preclinical stages of α -synucleinopathies.

Use of radiomic features and support vector machine to distinguish Parkinson's disease cases from normal controls

Background

Parkinson's disease (PD) is an irreversible neurodegenerative disease. The diagnosis of PD based on neuroimaging is usually with low-level or deep learning features, which results in difficulties in achieving precision classification or interpreting the clinical significance. Herein, we aimed to extract high-order features by using radiomics approach and achieve acceptable diagnosis accuracy in PD.

Methods

In this retrospective multicohort study, we collected ¹⁸F-fluorodeoxyglucose positron emission tomography (¹⁸F-FDG PET) images and clinical scale [the Unified Parkinson's Disease Rating Scale (UPDRS) and Hoehn & Yahr scale (H&Y)] from two cohorts. One cohort from Huashan Hospital had 91 normal controls (NC) and 91 PD patients (UPDRS: 22.7±11.7, H&Y: 1.8±0.8), and the other cohort from Wuxi 904 Hospital had 26 NC and 22 PD patients (UPDRS: 20.9±11.6, H&Y: 1.7±0.9). The Huashan cohort was used as the training and test sets by 5-fold cross-validation and the Wuxi cohort was used as another separate test set. After identifying regions of interests (ROIs) based on the atlas-based method, radiomic features were extracted and selected by using autocorrelation and fisher score algorithm. A support vector machine (SVM) was trained to classify PD and NC based on selected radiomic features. In the comparative experiment, we compared our method with the traditional voxel values method. To guarantee the robustness, above processes were repeated in 500 times.

Results

Twenty-six brain ROIs were identified. Six thousand one hundred and ten radiomic features were extracted in total. Among them 30 features were remained after feature selection. The accuracies of the proposed method achieved 90.97%±4.66% and 88.08%±5.27% in Huashan and Wuxi test sets, respectively.

Conclusions

This study showed that radiomic features and SVM could be used to distinguish between PD and NC based on 18F-FDG PET images.

Abnormal pattern of brain glucose metabolism in Parkinson's disease: replication in three European cohorts

Rationale

In Parkinson’s disease (PD), spatial covariance analysis of ¹⁸F-FDG PET data has consistently revealed a characteristic PD-related brain pattern (PDRP). By quantifying PDRP expression on a scan-by-scan basis, this technique allows objective assessment of disease activity in individual subjects. We provide a further validation of the PDRP by applying spatial covariance analysis to PD cohorts from the Netherlands (NL), Italy (IT), and Spain (SP).

Methods

The PDRP_NL was previously identified (17 controls, 19 PD) and its expression was determined in 19 healthy controls and 20 PD patients from the Netherlands. The PDRP_IT was identified in 20 controls and 20 “de-novo” PD patients from an Italian cohort. A further 24 controls and 18 “de-novo” Italian patients were used for validation. The PDRP_SP was identified in 19 controls and 19 PD patients from a Spanish cohort with late-stage PD. Thirty Spanish PD patients were used for validation. Patterns of the three centers were visually compared and then cross-validated. Furthermore, PDRP expression was determined in 8 patients with multiple system atrophy.

Results

A PDRP could be identified in each cohort. Each PDRP was characterized by relative hypermetabolism in the thalamus, putamen/pallidum, pons, cerebellum, and motor cortex. These changes co-varied with variable degrees of hypometabolism in posterior parietal, occipital, and frontal cortices. Frontal hypometabolism was less pronounced in “de-novo” PD subjects (Italian cohort). Occipital hypometabolism was more pronounced in late-stage PD subjects (Spanish cohort). PDRP_IT, PDRP_NL, and PDRP_SP were significantly expressed in PD patients compared with controls in validation cohorts from the same center (P < 0.0001), and maintained significance on cross-validation (P < 0.005). PDRP expression was absent in MSA.

Conclusion

The PDRP is a reproducible disease characteristic across PD populations and scanning platforms globally. Further study is needed to identify the topography of specific PD subtypes, and to identify and correct for center-specific effects.

Electronic supplementary material

The online version of this article (10.1007/s00259-019-04570-7) contains supplementary material, which is available to authorized users.

Scaled Subprofile Modeling and Convolutional Neural Networks for the Identification of Parkinson’s Disease in 3D Nuclear Imaging Data

Over the last years convolutional neural networks (CNNs) have shown remarkable results in different image classification tasks, including medical imaging. One area that has been less explored with CNNs is Positron Emission Tomography (PET). Fluorodeoxyglucose Positron Emission Tomography (FDG-PET) is a PET technique employed to obtain a representation of brain metabolic function. In this study we employed 3D CNNs in FDG-PET brain images with the purpose of discriminating patients diagnosed with Parkinson’s disease (PD) from controls. We employed Scaled Subprofile Modeling using Principal Component Analysis as a preprocessing step to focus on specific brain regions and limit the number of voxels that are used as input for the CNNs, thereby increasing the signal-to-noise ratio in our data. We performed hyperparameter optimization on three CNN architectures to estimate the classification accuracy of the networks on new data. The best performance that we obtained was [Formula: see text] and area under the receiver operating characteristic curve [Formula: see text] on the test set. We believe that, with larger datasets, PD patients could be reliably distinguished from controls by FDG-PET scans alone and that this technique could be applied to more clinically challenging tasks, like the differential diagnosis of neurological disorders with similar symptoms, such as PD, Progressive Supranuclear Palsy (PSP) and Multiple System Atrophy (MSA).

The Metabolic Activity of Caudate and Prefrontal Cortex Negatively Correlates with the Severity of Idiopathic Parkinson's Disease

Positron emission tomography (PET) scan with tracer [¹⁸F]-fluorodeoxy-glucose (¹⁸F-FDG) is widely used to measure the glucose metabolism in neurodegenerative disease such as Idiopathic Parkinson’s disease (IPD). Previous studies using ¹⁸F-FDG PET mainly focused on the motor or non-motor symptoms but not the severity of IPD. In this study, we aimed to determine the metabolic patterns of ¹⁸F-FDG in different stages of IPD defined by Hoehn and Yahr rating scale (H-Y rating scale) and to identify regions in the brain that play critical roles in disease progression. Fifty IPD patients were included in this study. They were 29 men and 21 women (mean±SD, age 57.7±11.1 years, disease duration 4.0±3.8 years, H-Y 2.2±1.1). Twenty healthy individuals were included as normal controls. Following ¹⁸F-FDG PET scan, image analysis was performed using Statistical Parametric Mapping (SPM) and Resting-State fMRI Data Analysis Toolkit (REST). The metabolic feature of IPD and regions-of-interests (ROIs) were determined. Correlation analysis between ROIs and H-Y stage was performed. SPM analysis demonstrated a significant hypometabolic activity in bilateral putamen, caudate and anterior cingulate as well as left parietal lobe, prefrontal cortex in IPD patients. In contrast, hypermetabolism was observed in the cerebellum and vermis. There was a negative correlation (p=0.007, r=-0.412) between H-Y stage and caudate metabolic activity. Moreover, the prefrontal area also showed a negative correlation with H-Y (P=0.033, r=-0.334). Thus, the uptake of FDG in caudate and prefrontal cortex can potentially be used as a surrogate marker to evaluate the severity of IPD.

Factors affecting the harmonization of disease-related metabolic brain pattern expression quantification in [18F]FDG-PET (PETMETPAT)

Introduction

The implementation of spatial-covariance [¹⁸F]fluorodeoxyglucose positron emission tomography–based disease-related metabolic brain patterns as biomarkers has been hampered by intercenter imaging differences. Within the scope of the JPND-PETMETPAT working group, we illustrate the impact of these differences on Parkinson's disease–related pattern (PDRP) expression scores.

Methods

Five healthy controls, 5 patients with idiopathic rapid eye movement sleep behavior disorder, and 5 patients with Parkinson's disease were scanned on one positron emission tomography/computed tomography system with multiple image reconstructions. In addition, one Hoffman 3D Brain Phantom was scanned on several positron emission tomography/computed tomography systems using various reconstructions. Effects of image contrast on PDRP scores were also examined.

Results

Human and phantom raw PDRP scores were systematically influenced by scanner and reconstruction effects. PDRP scores correlated inversely to image contrast. A Gaussian spatial filter reduced contrast while decreasing intercenter score differences.

Discussion

Image contrast should be considered in harmonization efforts. A Gaussian filter may reduce noise and intercenter effects without sacrificing sensitivity. Phantom measurements will be important for correcting PDRP score offsets.

Combined native magnetic resonance angiography, flow-quantifying, and perfusion-imaging for impending second-stroke assessment

This special report introduces native flow quantitative imaging for evaluating stroke risk. Moreover, the advantage of combining three imaging techniques [magnetic resonance angiography (MRA), phase-contrast (PC) flow imaging, and arterial spin-labeling imaging] is shown to be beneficial for responding to ischemia and preserving viable neurons. These quantitative imaging techniques provide authoritative information for diagnosing impending stroke and selecting appropriate treatment.

Advances in the Research of Risk Factors and Prodromal Biomarkers of Parkinson's Disease

Parkinson's disease (PD) is the second most common neurodegenerative disease in the world. With the advent of an aging population and improving life expectancy worldwide, the number of PD patients is expected to increase, which may lead to an urgent need for effective preventive and diagnostic strategies for PD. Although there is increasing research regarding the pathogenesis of PD, there is limited knowledge regarding the prevention of PD. Moreover, the diagnosis of PD depends on clinical criteria, which require the occurrence of bradykinesia and at least one symptom of rest tremor or rigidity. However, converging evidence from clinical, genetic, neuropathological, and imaging studies suggests the initiation of PD-specific pathology prior to the initial presentation of these classical motor clinical features by years or decades. This latent stage of neurodegeneration in PD is a particularly important stage for effective neuroprotective therapies, which might retard the progression or prevent the onset of PD. Therefore, the exploration of risk factors and premotor biomarkers is not only crucial to the early diagnosis of PD but is also helpful in the development of effective neuroprotection and health care strategies for appropriate populations at risk for PD. In this review, we searched and summarized ∼249 researches and 31 reviews focusing on the risk factors and prodromal biomarkers of PD and published in MEDLINE.

Precuneus Dysfunction in Parkinson's Disease With Mild Cognitive Impairment

Background: Mild cognitive impairment (MCI) frequently occurs in Parkinson’s disease (PD). Neurovascular changes interact with neurodegenerative processes in PD. However, the deficits of cerebral blood flow (CBF) perfusion and the associated functional connectivity (FC) in PD patients with MCI (PD-MCI) remain unclear.

Purpose: This study aimed to explore the specific neurovascular perfusion alterations in PD-MCI compared to PD with normal cognition (PD-NC) and healthy controls (HCs), and to further examine the resultant whole brain FC changes in the abnormal perfusion regions.

Methods: Relative CBF (rCBF) was calculated using arterial spin labeling (ASL) in 54 patients with PD (27 patients with PD-NC and 27 patients with PD-MCI) and 25 HCs matched for age and gender ratio, who also underwent the structural MRI, resting-state functional MRI (rs-fMRI) and neuropsychological examinations. The gray matter (GM) changes in PD patients were analyzed using voxel-based morphometry (VBM). The alterations in rCBF perfusion and FC among groups were then analyzed respectively. Additionally, correlations between these alterations and neuropsychological performances were further examined.

Results: Compared to HC, left caudate atrophy was detected in patients with PD. In comparison to both PD-NC and HC, patients with PD-MCI specifically exhibited hypoperfusion in the parietal memory network (PMN) in the precuneus (PCu) and decreased PCu-FC in the right striatum. Moreover, PCu perfusion and PCu-FC strengths in the right striatum were positively associated with memory performance in PD-MCI.

Conclusions: These findings suggest that the posterior PMN dysfunction underlies memory deficits in PD-MCI.

FDG PET Parkinson's disease-related pattern as a biomarker for clinical trials in early stage disease

Background

The development of therapeutic interventions for Parkinson disease (PD) is challenged by disease complexity and subjectivity of symptom evaluation. A Parkinson's Disease Related Pattern (PDRP) of glucose metabolism via fluorodeoxyglucose positron emission tomography (FDG-PET) has been reported to correlate with motor symptom scores and may aid the detection of disease-modifying therapeutic effects.

Objectives

We sought to independently evaluate the potential utility of the PDRP as a biomarker for clinical trials of early-stage PD.

Methods

Two machine learning approaches (Scaled Subprofile Model (SSM) and NPAIRS with Canonical Variates Analysis) were performed on FDG-PET scans from 17 healthy controls (HC) and 23 PD patients. The approaches were compared regarding discrimination of HC from PD and relationship to motor symptoms.

Results

Both classifiers discriminated HC from PD (p < 0.01, p < 0.03), and classifier scores for age- and gender- matched HC and PD correlated with Hoehn & Yahr stage (R² = 0.24, p < 0.015) and UPDRS (R² = 0.23, p < 0.018). Metabolic patterns were highly similar, with hypometabolism in parieto-occipital and prefrontal regions and hypermetabolism in cerebellum, pons, thalamus, paracentral gyrus, and lentiform nucleus relative to whole brain, consistent with the PDRP. An additional classifier was developed using only PD subjects, resulting in scores that correlated with UPDRS (R² = 0.25, p < 0.02) and Hoehn & Yahr stage (R² = 0.16, p < 0.06).

Conclusions

Two independent analyses performed in a cohort of mild PD patients replicated key features of the PDRP, confirming that FDG-PET and multivariate classification can provide an objective, sensitive biomarker of disease stage with the potential to detect treatment effects on PD progression.

•

The Parkinson's disease-related pattern (PDRP) of glucose metabolic effects is demonstrated in an independent cohort of early stage PD patients.

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The PDRP pattern of metabolic changes is robust to variations in image processing and choice of classification model.

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Age-related metabolic changes show partial overlap with the PDRP, suggesting that age-adjustment is an important consideration.

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The PDRP correlates with motor function as defined by Hoehn & Yahr stage and UPDRS score.

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An additional data driven metabolic classifier highlights pattern aspects associated with early stage motor decline.