Differentiating multiple system atrophy from Parkinson's disease: contribution of striatal and midbrain MRI volumetry and multi-tracer PET imaging

Association of dysphagia severity in multiple system atrophy with the specific binding ratio on dopamine transporter SPECT

OBJECTIVE

Dysphagia in multiple system atrophy (MSA) is life-threatening and is caused by parkinsonism with cerebellar ataxia as a contributing factor. The present study investigated the relationship between dysphagia severity in MSA and the specific binding ratio (SBR) on dopamine transporter (DaT) SPECT using the Hyodo score, a qualitative scale for use with fiberoptic endoscopic evaluation of swallowing (FEES).

METHODS

Hyodo score's ability to predict aspiration during a FEES examination of 88 patients with MSA was first tested. Then the clinical characteristics, Hyodo score, and SBR of patients with either predominant parkinsonism (MSA-P; n = 11) or cerebellar ataxia (MSA-C; n = 25) who underwent FEES and DaT SPECT simultaneously were compared.

RESULTS

Logistic regression demonstrated that the Hyodo score was a significant predictive factor of aspiration (p = 0.003). The MSA-P group had a significantly higher Hyodo score (p = 0.026) and lower SBR (p = 0.011) than the MSA-C group while neither group demonstrated any significant difference in disease duration at the FEES examination. Linear regression demonstrated a significant, inverse correlation between the Hyodo score and SBR in the MSA-P (p = 0.044; r = -0.616) and MSA-C (p = 0.044; r = -0.406) groups. When the effect of SBR was removed by analysis of covariance, no significant difference in the Hyodo score remained between the groups.

CONCLUSIONS

Our results suggested an association between presynaptic changes in nigrostriatal dopaminergic neurons and dysphagia severity in MSA which largely contributes to the difference in dysphagia severity between MSA-P and MSA-C.

PET brain imaging in neurological disorders

Brain disorders are a series of conditions with damage or loss of neurons, such as Parkinson's disease (PD), Alzheimer's disease (AD), or drug dependence. These individuals have gradual deterioration of cognitive, motor, and other central nervous system functions affected. This degenerative trajectory is intricately associated with dysregulations in neurotransmitter systems. Positron Emission Tomography (PET) imaging, employing radiopharmaceuticals and molecular imaging techniques, emerges as a crucial tool for detecting brain biomarkers. It offers invaluable insights for early diagnosis and distinguishing brain disorders. This article comprehensively reviews the application and progress of conventional and novel PET imaging agents in diagnosing brain disorders. Furthermore, it conducts a thorough analysis on merits and limitations. The article also provides a forward-looking perspective in the future development directions of PET imaging agents for diagnosing brain disorders and proposes potential innovative strategies. It aims to furnish clinicians and researchers with an all-encompassing overview of the latest advancements and forthcoming trends in the utilization of PET imaging for diagnosing brain disorders.

Rates of change of pons and middle cerebellar peduncle diameters are diagnostic of multiple system atrophy of the cerebellar type

Definitive diagnosis of multiple system atrophy of the cerebellar type (MSA-C) is challenging. We hypothesized that rates of change of pons and middle cerebellar peduncle diameters on MRI would be unique to MSA-C and serve as diagnostic biomarkers. We defined the normative data for anterior-posterior pons and transverse middle cerebellar peduncle diameters on brain MRI in healthy controls, performed diameter-volume correlations and measured intra- and inter-rater reliability. We studied an Exploratory cohort (2002-2014) of 88 MSA-C and 78 other cerebellar ataxia patients, and a Validation cohort (2015-2021) of 49 MSA-C, 13 multiple system atrophy of the parkinsonian type (MSA-P), 99 other cerebellar ataxia patients and 314 non-ataxia patients. We measured anterior-posterior pons and middle cerebellar peduncle diameters on baseline and subsequent MRIs, and correlated results with Brief Ataxia Rating Scale scores. We assessed midbrain:pons and middle cerebellar peduncle:pons ratios over time. The normative anterior-posterior pons diameter was 23.6 ± 1.6 mm, and middle cerebellar peduncle diameter 16.4 ± 1.4 mm. Pons diameter correlated with volume, r = 0.94, P < 0.0001. The anterior-posterior pons and middle cerebellar peduncle measures were smaller at first scan in MSA-C compared to all other ataxias; anterior-posterior pons diameter: Exploratory, 19.3 ± 2.6 mm versus 20.7 ± 2.6 mm, Validation, 19.9 ± 2.1 mm versus 21.1 ± 2.1 mm; middle cerebellar peduncle transverse diameter, Exploratory, 12.0 ± 2.6 mm versus 14.3 ±2.1 mm, Validation, 13.6 ± 2.1 mm versus 15.1 ± 1.8 mm, all P < 0.001. The anterior-posterior pons and middle cerebellar peduncle rates of change were faster in MSA-C than in all other ataxias; anterior-posterior pons diameter rates of change: Exploratory, -0.87 ± 0.04 mm/year versus -0.09 ± 0.02 mm/year, Validation, -0.89 ± 0.48 mm/year versus -0.10 ± 0.21 mm/year; middle cerebellar peduncle transverse diameter rates of change: Exploratory, -0.84 ± 0.05 mm/year versus -0.08 ± 0.02 mm/year, Validation, -0.94 ± 0.64 mm/year versus -0.11 ± 0.27 mm/year, all values P < 0.0001. Anterior-posterior pons and middle cerebellar peduncle diameters were indistinguishable between Possible, Probable and Definite MSA-C. The rate of anterior-posterior pons atrophy was linear, correlating with ataxia severity. Using a lower threshold anterior-posterior pons diameter decrease of -0.4 mm/year to balance sensitivity and specificity, area under the curve analysis discriminating MSA-C from other ataxias was 0.94, yielding sensitivity 0.92 and specificity 0.87. For the middle cerebellar peduncle, with threshold decline -0.5 mm/year, area under the curve was 0.90 yielding sensitivity 0.85 and specificity 0.79. The midbrain:pons ratio increased progressively in MSA-C, whereas the middle cerebellar peduncle:pons ratio was almost unchanged. Anterior-posterior pons and middle cerebellar peduncle diameters were smaller in MSA-C than in MSA-P, P < 0.001. We conclude from this 20-year longitudinal clinical and imaging study that anterior-posterior pons and middle cerebellar peduncle diameters are phenotypic imaging biomarkers of MSA-C. In the correct clinical context, an anterior-posterior pons and transverse middle cerebellar peduncle diameter decline of ∼0.8 mm/year is sufficient for and diagnostic of MSA-C.

Toward More Accessible Fully Automated 3D Volumetric MRI Decision Trees for the Differential Diagnosis of Multiple System Atrophy, Related Disorders, and Age-Matched Healthy Subjects

Differentiating multiple system atrophy (MSA) from related neurodegenerative movement disorders (NMD) is challenging. MRI is widely available and automated decision-tree analysis is simple, transparent, and resistant to overfitting. Using a retrospective cohort of heterogeneous clinical MRIs broadly sourced from a tertiary hospital system, we aimed to develop readily translatable and fully automated volumetric diagnostic decision-trees to facilitate early and accurate differential diagnosis of NMDs. 3DT1 MRI from 171 NMD patients (72 MSA, 49 PSP, 50 PD) and 171 matched healthy subjects were automatically segmented using Freesurfer6.0 with brainstem module. Decision trees employing substructure volumes and a novel volumetric pons-to-midbrain ratio (3D-PMR) were produced and tenfold cross-validation performed. The optimal tree separating NMD from healthy subjects selected cerebellar white matter, thalamus, putamen, striatum, and midbrain volumes as nodes. Its sensitivity was 84%, specificity 94%, accuracy 84%, and kappa 0.69 in cross-validation. The optimal tree restricted to NMD patients selected 3D-PMR, thalamus, superior cerebellar peduncle (SCP), midbrain, pons, and putamen as nodes. It yielded sensitivities/specificities of 94/84% for MSA, 72/96% for PSP, and 73/92% PD, with 79% accuracy and 0.62 kappa. There was correct classification of 16/17 MSA, 5/8 PSP, 6/8 PD autopsy-confirmed patients, and 6/8 MRIs that preceded motor symptom onset. Fully automated decision trees utilizing volumetric MRI data distinguished NMD patients from healthy subjects and MSA from other NMDs with promising accuracy, including autopsy-confirmed and pre-symptomatic subsets. Our open-source methodology is well-suited for widespread clinical translation. Assessment in even more heterogeneous retrospective and prospective cohorts is indicated.

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

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

Deep learning segmentation results in precise delineation of the putamen in multiple system atrophy

OBJECTIVES

The precise segmentation of atrophic structures remains challenging in neurodegenerative diseases. We determined the performance of a Deep Neural Patchwork (DNP) in comparison to established segmentation algorithms regarding the ability to delineate the putamen in multiple system atrophy (MSA), Parkinson's disease (PD), and healthy controls.

METHODS

We retrospectively included patients with MSA and PD as well as healthy controls. A DNP was trained on manual segmentations of the putamen as ground truth. For this, the cohort was randomly split into a training (N = 131) and test set (N = 120). The DNP's performance was compared with putaminal segmentations as derived by Automatic Anatomic Labelling, Freesurfer and Fastsurfer. For validation, we assessed the diagnostic accuracy of the resulting segmentations in the delineation of MSA vs. PD and healthy controls.

RESULTS

A total of 251 subjects (61 patients with MSA, 158 patients with PD, and 32 healthy controls; mean age of 61.5 ± 8.8 years) were included. Compared to the dice-coefficient of the DNP (0.96), we noted significantly weaker performance for AAL3 (0.72; p < .001), Freesurfer (0.82; p < .001), and Fastsurfer (0.84, p < .001). This was corroborated by the superior diagnostic performance of MSA vs. PD and HC of the DNP (AUC 0.93) versus the AUC of 0.88 for AAL3 (p = 0.02), 0.86 for Freesurfer (p = 0.048), and 0.85 for Fastsurfer (p = 0.04).

CONCLUSION

By utilization of a DNP, accurate segmentations of the putamen can be obtained even if substantial atrophy is present. This allows for more precise extraction of imaging parameters or shape features from the putamen in relevant patient cohorts.

CLINICAL RELEVANCE STATEMENT

Deep learning-based segmentation of the putamen was superior to currently available algorithms and is beneficial for the diagnosis of multiple system atrophy.

KEY POINTS

• A Deep Neural Patchwork precisely delineates the putamen and performs equal to human labeling in multiple system atrophy, even when pronounced putaminal volume loss is present. • The Deep Neural Patchwork-based segmentation was more capable to differentiate between multiple system atrophy and Parkinson's disease than the AAL3 atlas, Freesurfer, or Fastsurfer.

Constructing the detecting stage: Social processes and the diagnostic journey of early onset Parkinson's disease

Parkinson's is a neurodegenerative disorder that mostly affects people over the age of 65; however, a significant number (10%) of the diagnosed cases are under the age of 50 (Young Onset Parkinson's disease). It has multiple symptoms; it is difficult to diagnose and its progression cannot be intercepted. This article focuses on the experience of the diagnostic process as narrated by individuals who were under the age of 50 when diagnosed. Analysis of in-depth interviews with 17 individuals with Parkinson's disease inductively reveals a 'detecting stage', a period during which they visit different doctors, undertake many examinations and experience bodily symptoms with impacts on their mental health. The diagnostic process, which usually lasts over 2 years, often leads to misdiagnosis and or late diagnosis (a) on account of the individual's young age and social meanings attached to it, usually by doctors, whose discourse appears to be rather psychologised and (b) is perceived as disruptive to individuals' biographies. Due to the tendency to over-attribute bodily symptoms to psychological problems, accurate diagnosis is experienced as redemption.

Dopaminergic imaging in degenerative parkinsonisms, an established clinical diagnostic tool

Parkinson's disease (PD) and other neurodegenerative parkinsonisms are characterised by loss of striatal dopaminergic neurons. Dopamine functional deficits can be measured in vivo using positron emission tomography (PET) and single-photon emission computed tomography (SPECT) ligands assessing either presynaptic (e.g. dopamine synthesis and storage, transporter density) or postsynaptic terminals (i.e. D2 receptors availability). Nuclear medicine imaging thus helps the clinician to separate degenerative forms of parkinsonism with other neurological conditions, e.g. essential tremor or drug-induced parkinsonism. With the present study, we aimed at summarizing the current evidence about dopaminergic molecular imaging in the diagnostic evaluation of PD, atypical parkinsonian syndromes and dementia with Lewy bodies (DLB), as well as its potential to distinguish these conditions and to estimate disease progression. In fact, PET/SPECT methods are clinically validated and have been increasingly integrated into diagnostic guidelines (e.g. for PD and DLB). In addition, there is novel evidence on the classification properties of extrastriatal signal. Finally, dopamine imaging has an outstanding potential to detect neurodegeneration at the premotor stage, including REM-sleep behavior disorder and olfactory loss. Therefore, inclusion of subjects at an early stage for clinical trials can largely benefit from a validated in vivo biomarker such as presynaptic dopamine pathways PET/SPECT assessment.

Multimodal striatal neuromarkers in distinguishing parkinsonian variant of multiple system atrophy from idiopathic Parkinson's disease

AIMS

To develop an automatic method of classification for parkinsonian variant of multiple system atrophy (MSA-P) and Idiopathic Parkinson's disease (IPD) in early to moderately advanced stages based on multimodal striatal alterations and identify the striatal neuromarkers for distinction.

METHODS

77 IPD and 75 MSA-P patients underwent 3.0 T multimodal MRI comprising susceptibility-weighted imaging, resting-state functional magnetic resonance imaging, T1-weighted imaging, and diffusion tensor imaging. Iron-radiomic features, volumes, functional and diffusion scalars of bilateral 10 striatal subregions were calculated and provided to the support vector machine for classification RESULTS: A combination of iron-radiomic features, function, diffusion, and volumetric measures optimally distinguished IPD and MSA-P in the testing dataset (accuracy 0.911 and area under the receiver operating characteristic curves [AUC] 0.927). The diagnostic performance further improved when incorporating clinical variables into the multimodal model (accuracy 0.934 and AUC 0.953). The most crucial factor for classification was the functional activity of the left dorsolateral putamen.

CONCLUSION

The machine learning algorithm applied to multimodal striatal dysfunction depicted dorsal striatum and supervening prefrontal lobe and cerebellar dysfunction through the frontostriatal and cerebello-striatal connections and facilitated accurate classification between IPD and MSA-P. The dorsolateral putamen was the most valuable neuromarker for the classification.

Structural and functional abnormalities in Parkinson's disease based on voxel-based morphometry and resting-state functional magnetic resonance imaging

OBJECTIVE

To explore differences in gray matter volume (GMV) and white matter volume (WMV) between patients with Parkinson's disease (PD) and healthy controls, and to examine whether the structural abnormalities correlate with functional abnormalities.

METHODS

T1-weighted magnetic resonance imaging and resting-state functional magnetic resonance imaging (fMRI) were performed on 180 patients with PD and 58 age- and sex-matched healthy controls. We used voxel-based morphometry (VBM) to compare GMV and WMV between groups, and resting-state fMRI to compare amplitudes of low-frequency fluctuations (ALFF) in the structurally abnormal brain regions.

RESULTS

Structural neuroimaging showed smaller whole-brain GMV, but not WMV, in patients. Furthermore, VBM revealed smaller GMV in the right superior temporal gyrus (STG) and left frontotemporal space in patients, after correction for multiple comparisons. Patients also showed significantly higher ALFF in the right STG. GMV in the right STG and left frontotemporal space in patients correlated negatively with age and scores on Part III of the Movement Disorder Society Unified Parkinson's Disease Rating Scale, but not with PD duration.

CONCLUSIONS

Structural atrophy in the frontotemporal lobe may be a useful imaging biomarker in PD, such as for detecting disease progression. Furthermore, this structural atrophy appears to correlate with enhanced spontaneous brain activity. This study associates particular structural and functional abnormalities with PD neuropathology.

The Legacy of the TTASAAN Report - Premature Conclusions and Forgotten Promises About SPECT Neuroimaging: A Review of Policy and Practice Part II

Brain perfusion single photon emission computed tomography (SPECT) scans were initially developed in 1970s. A key radiopharmaceutical, hexamethylpropyleneamine oxime (HMPAO), was not stabilized until 1993 and most early SPECT scans were performed on single-head gamma cameras. These early scans were of inferior quality. In 1996, the Therapeutics and Technology Assessment Subcommittee of the American Academy of Neurology (TTASAAN) issued a report regarding the use of SPECT in the evaluation of neurological disorders. This two-part series explores the policies and procedures related to perfusion SPECT functional neuroimaging. In Part I, the comparison between the quality of the SPECT scans and the depth of the data for key neurological and psychiatric indications at the time of the TTASAAN report vs. the intervening 25 years were presented. In Part II, the technical aspects of perfusion SPECT neuroimaging and image processing will be explored. The role of color scales will be reviewed and the process of interpreting a SPECT scan will be presented. Interpretation of a functional brain scans requires not only anatomical knowledge, but also technical understanding on correctly performing a scan, regardless of the scanning modality. Awareness of technical limitations allows the clinician to properly interpret a functional brain scan. With this foundation, four scenarios in which perfusion SPECT neuroimaging, together with other imaging modalities and testing, lead to a narrowing of the differential diagnoses and better treatment. Lastly, recommendations for the revision of current policies and practices are made.

Sex-different interrelationships of rs945270, cerebral gray matter volumes, and attention deficit hyperactivity disorder: a region-wide study across brain

Previous genome-wide association studies (GWAS) reported that the allele C of rs945270 of the kinectin 1 gene (KTN1) most significantly increased the gray matter volume (GMV) of the putamen and modestly regulated the risk for attention deficit hyperactivity disorder (ADHD). On the other hand, ADHD is known to be associated with a reduction in subcortical and cortical GMVs. Here, we examined the interrelationships of the GMVs, rs945270 alleles, and ADHD symptom scores in the same cohort of children. With data of rs945270 genotypes, GMVs of 118 brain regions, and ADHD symptom scores of 3372 boys and 3129 girls of the Adolescent Brain Cognition Development project, we employed linear regression analyses to examine the pairwise correlations adjusted for the third of the three traits and other relevant covariates, and examine their mediation effects. We found that the major allele C of rs945270 modestly increased risk for ADHD in males only when controlling for the confounding effects of the GMV of any one of the 118 cerebral regions (0.026 ≤ p ≤ 0.059: Top two: left and right putamen). This allele also significantly increased putamen GMV in males alone (left p = 2.8 × 10-5, and right p = 9.4 × 10-5; α = 2.1 × 10-4) and modestly increased other subcortical and cortical GMVs in both sexes (α < p < 0.05), whether or not adjusted for ADHD symptom scores. Both subcortical and cortical GMVs were significantly or suggestively reduced in ADHD when adjusted for rs945270 alleles, each more significantly in females (3.6 × 10-7 ≤ p < α; Top two: left pallidum and putamen) and males (3.5 × 10-6 ≤ p < α), respectively. Finally, the left and right putamen GMVs reduced 14.0% and 11.7% of the risk effects of allele C on ADHD, and allele C strengthened 4.5% (left) and 12.2% (right) of the protective effects of putamen GMVs on ADHD risk, respectively. We concluded that the rs945270-GMVs-ADHD relationships were sex-different. In males, the major allele C of rs945270 increased risk for ADHD, which was compromised by putamen GMVs; this allele also but only significantly increased putamen GMVs that then significantly protected against ADHD risk. In females, the top two GMVs significantly decreasing ADHD risk were left pallidum and putamen GMVs. Basal ganglia the left putamen in particular play the most critical role in the pathogenesis of ADHD.

Basal ganglia atrophy-associated causal structural network degeneration in Parkinson's disease

Parkinson's disease (PD) is a progressive neurodegenerative disease characterized by both motor and non-motor symptoms. A convergent pathophysiological hallmark of PD is an early selective vulnerability within the basal ganglia circuit. However, the causal interactions between basal ganglia atrophy and progressive structural network alterations in PD remain unaddressed. Here, we adopted voxel-based morphometry method to measure gray matter (GM) volume for each participant (n = 84 PD patients and n = 70 matched healthy controls). Patients were first divided into three stages according to the Hoehn and Yahr (H&Y) and the Part III of Unified Parkinson's Disease Rating Scale scores respectively to analyze the stage-specific GM atrophy patterns. Then, the modulation of early caudate atrophy over other brain structures was evaluated using the whole-brain voxel-wise and region-of-interest-wise causal structural covariance network approaches. We found that GM atrophy progressively expands from the basal ganglia to the angular gyrus, temporal areas, and eventually spreads through the subcortical-cortical networks as PD progresses. Notably, we identified a shared caudate-associated degeneration network including the basal ganglia, thalamus, cerebellum, sensorimotor cortex, and cortical association areas with the PD progressive factors. These findings suggest that the early structural vulnerability of basal ganglia in PD may play a pivotal role in the modulation of motor and non-motor circuits at the structural level. Our work provides evidence for a novel mechanism of network degeneration that underlies the pathology of PD and may have potential clinical applications in the development of early predictors of PD onset and progress.

Screening of Parkinson's Disease Using Geometric Features Extracted from Spiral Drawings

Conventional means of Parkinson’s Disease (PD) screening rely on qualitative tests typically administered by trained neurologists. Tablet technologies that enable data collection during handwriting and drawing tasks may provide low-cost, portable, and instantaneous quantitative methods for high-throughput PD screening. However, past efforts to use data from tablet-based drawing processes to distinguish between PD and control populations have demonstrated only moderate classification ability. Focusing on digitized drawings of Archimedean spirals, the present study utilized data from the open-access ParkinsonHW dataset to improve existing PD drawing diagnostic pipelines. Random forest classifiers were constructed using previously documented features and highly-predictive, newly-proposed features that leverage the many unique mathematical characteristics of the Archimedean spiral. This approach yielded an AUC of 0.999 on the particular dataset we tested on, and more importantly identified interpretable features with good promise for generalization across diverse patient cohorts. It demonstrated the potency of mathematical relationships inherent to the drawing shape and the usefulness of sparse feature sets and simple models, which further enhance interpretability, in the face of limited sample size. The results of this study also inform suggestions for future drawing task design and data analytics (feature extraction, shape selection, task diversity, drawing templates, and data sharing).

Subcortical grey matter changes associated with motor symptoms evaluated by the Unified Parkinson's disease Rating Scale (part III): A longitudinal study in Parkinson's disease

•

Decreased grey matter volume over time suggests a subcortical alteration in PD.

•

Decreased volume in the thalamus may be related to the decline in motor skills.

•

Increased volume in the pallidum may contribute to motor impairment.

•

Structural changes in line with the model of basal ganglia-thalamocortical circuits.

•

VBM and volumetry might capture complementary aspects of structural changes in PD.

Parkinson disease (PD) is characterized by motor deficits related to structural changes in the basal ganglia-thalamocortical circuits. However, it is still unclear the exact nature of the association between grey matter alterations and motor symptoms.

Therefore, the aim of our investigation was to identify the subcortical modifications associated with motor symptoms of PD over time - adopting voxel-based morphometry (VBM) and automated volumetry methods.

We selected fifty subjects with PD from the Parkinson’s Progression Markers Initiative (PPMI) database, who performed an MRI session at two time points: at baseline (i.e. at maximum 2 years after clinical diagnosis of PD) and after 48 months. Motor symptoms were assessed using the part III of the Unified Parkinson’s Disease Rating Scale at the two time points.

Our VBM and volumetric analyses showed a general atrophy in all subcortical regions when comparing baseline with 48 months. These findings confirmed previous observations indicating a subcortical alteration over time in PD. Furthermore, our findings supported the idea that a reduced volume in the thalamus and an increased volume in pallidum may be related to the decline in motor skills. These structural modifications are in accordance with the functional model of the basal ganglia-thalamocortical circuits controlling movements. Moreover, VBM and volumetry provided partially overlapping results, suggesting that these methods might capture complementary aspects of brain degeneration in PD.

Quantitative Rodent Brain Receptor Imaging

Positron emission tomography (PET) is a non-invasive imaging technology employed to describe metabolic, physiological, and biochemical processes in vivo. These include receptor availability, metabolic changes, neurotransmitter release, and alterations of gene expression in the brain. Since the introduction of dedicated small-animal PET systems along with the development of many novel PET imaging probes, the number of PET studies using rats and mice in basic biomedical research tremendously increased over the last decade. This article reviews challenges and advances of quantitative rodent brain imaging to make the readers aware of its physical limitations, as well as to inspire them for its potential applications in preclinical research. In the first section, we briefly discuss the limitations of small-animal PET systems in terms of spatial resolution and sensitivity and point to possible improvements in detector development. In addition, different acquisition and post-processing methods used in rodent PET studies are summarized. We further discuss factors influencing the test-retest variability in small-animal PET studies, e.g., different receptor quantification methodologies which have been mainly translated from human to rodent receptor studies to determine the binding potential and changes of receptor availability and radioligand affinity. We further review different kinetic modeling approaches to obtain quantitative binding data in rodents and PET studies focusing on the quantification of endogenous neurotransmitter release using pharmacological interventions. While several studies have focused on the dopamine system due to the availability of several PET tracers which are sensitive to dopamine release, other neurotransmitter systems have become more and more into focus and are described in this review, as well. We further provide an overview of latest genome engineering technologies, including the CRISPR/Cas9 and DREADD systems that may advance our understanding of brain disorders and function and how imaging has been successfully applied to animal models of human brain disorders. Finally, we review the strengths and opportunities of simultaneous PET/magnetic resonance imaging systems to study drug-receptor interactions and challenges for the translation of PET results from bench to bedside.

Voxel-Based Meta-Analysis of Gray Matter Abnormalities in Multiple System Atrophy

Purpose: This study aimed to identify consistent gray matter volume (GMV) changes in the two subtypes of multiple system atrophy (MSA), including parkinsonism subtype (MSA-P), and cerebellar subtype (MSA-C), by conducting a voxel-wise meta-analysis of whole brain voxel-based morphometry (VBM) studies.

Method: VBM studies comparing MSA-P or MSA-C and healthy controls (HCs) were systematically searched in the PubMed, Embase, and Web of Science published from 1974 to 20 October 2020. A quantitative meta-analysis of VBM studies on MSA-P or MSA-C was performed using the effect size-based signed differential mapping (ES-SDM) method separately. A complementary analysis was conducted using the Seed-based d Mapping with Permutation of Subject Images (SDM-PSI) method, which allows a familywise error rate (FWE) correction for multiple comparisons of the results, for further validation of the results.

Results: Ten studies were included in the meta-analysis of MSA-P subtype, comprising 136 MSA-P patients and 211 HCs. Five studies were included in the meta-analysis of MSA-C subtype, comprising 89 MSA-C patients and 134 HCs. Cerebellum atrophy was detected in both MSA-P and MSA-C, whereas basal ganglia atrophy was only detected in MSA-P. Cerebral cortex atrophy was detected in both subtypes, with predominant impairment of the superior temporal gyrus, inferior frontal gyrus, temporal pole, insula, and amygdala in MSA-P and predominant impairment of the superior temporal gyrus, middle temporal gyrus, fusiform gyrus, and lingual gyrus in MSA-C. Most of these results survived the FWE correction in the complementary analysis, except for the bilateral amygdala and the left caudate nucleus in MSA-P, and the right superior temporal gyrus and the right middle temporal gyrus in MSA-C. These findings remained robust in the jackknife sensitivity analysis, and no significant heterogeneity was detected.

Conclusion: A different pattern of brain atrophy between MSA-P and MSA-C detected in the current study was in line with clinical manifestations and provided the evidence of the pathophysiology of the two subtypes of MSA.

Neuroimaging Advances in Parkinson's Disease and Atypical Parkinsonian Syndromes

Parkinson's disease (PD) and atypical Parkinsonian syndromes are progressive heterogeneous neurodegenerative diseases that share clinical characteristic of parkinsonism as a common feature, but are considered distinct clinicopathological disorders. Based on the predominant protein aggregates observed within the brain, these disorders are categorized as, (1) α-synucleinopathies, which include PD and other Lewy body spectrum disorders as well as multiple system atrophy, and (2) tauopathies, which comprise progressive supranuclear palsy and corticobasal degeneration. Although, great strides have been made in neurodegenerative disease research since the first medical description of PD in 1817 by James Parkinson, these disorders remain a major diagnostic and treatment challenge. A valid diagnosis at early disease stages is of paramount importance, as it can help accommodate differential prognostic and disease management approaches, enable the elucidation of reliable clinicopathological relationships ideally at prodromal stages, as well as facilitate the evaluation of novel therapeutics in clinical trials. However, the pursuit for early diagnosis in PD and atypical Parkinsonian syndromes is hindered by substantial clinical and pathological heterogeneity, which can influence disease presentation and progression. Therefore, reliable neuroimaging biomarkers are required in order to enhance diagnostic certainty and ensure more informed diagnostic decisions. In this article, an updated presentation of well-established and emerging neuroimaging biomarkers are reviewed from the following modalities: (1) structural magnetic resonance imaging (MRI), (2) diffusion-weighted and diffusion tensor MRI, (3) resting-state and task-based functional MRI, (4) proton magnetic resonance spectroscopy, (5) transcranial B-mode sonography for measuring substantia nigra and lentiform nucleus echogenicity, (6) single photon emission computed tomography for assessing the dopaminergic system and cerebral perfusion, and (7) positron emission tomography for quantifying nigrostriatal functions, glucose metabolism, amyloid, tau and α-synuclein molecular imaging, as well as neuroinflammation. Multiple biomarkers obtained from different neuroimaging modalities can provide distinct yet corroborative information on the underlying neurodegenerative processes. This integrative "multimodal approach" may prove superior to single modality-based methods. Indeed, owing to the international, multi-centered, collaborative research initiatives as well as refinements in neuroimaging technology that are currently underway, the upcoming decades will mark a pivotal and exciting era of further advancements in this field of neuroscience.

Nuclear Imaging in the Diagnosis of Clinically Uncertain Parkinsonian Syndromes

BACKGROUND

Parkinsonian syndromes are classified by etiology mainly on clinical grounds, that is, on the basis of the clinical manifestations and with the aid of conventional ancillary studies. In most cases, the clinical diagnosis is clear. In up to 30% of cases, however, the etiological classification remains uncertain after completion of the basic clinical diagnostic evaluation, and additional investigation with nuclear imaging may be indicated. In particular, cerebral single-photon emission computed tomography (SPECT) with dopamine transporter (DAT) ligands may be helpful. DAT-SPECT can be used to demonstrate or rule out nigrostriatal degeneration and thereby differentiate neurodegenerative parkinsonian syndromes from symptomatic parkinsonian syndromes and other differential diagnoses. Positron emission tomography (PET) with the glucose analogue [18F]fluorodeoxyglucose (FDG) can be used to identify disease-specific patterns of neuronal dysfunction/degeneration in order to differentiate the various neurodegenerative parkinsonian syndromes from one another.

METHODS

In this review, we summarize the current state of the evidence on DAT-SPECT and FDG-PET for the indications mentioned above on the basis of a selective review of the literature.

RESULTS

DAT-SPECT has been adequately validated as an in vivo marker for nigrostriatal degeneration. Studies using the clinical diagnosis of a movement disorders specialist over the course of the disease as a reference have shown that DAT- SPECT is 78-100% sensitive (median, 93%) and 70-100% specific (median, 89%) for the differentiation of neurodegenerative parkinsonian syndromes from symptomatic parkinsonism and other differential diagnoses in clinically unclear cases. DAT- SPECT scanning led to a change of diagnosis in 27-56% of patients (median, 43%) and to a change of treatment in 33-72% (median, 43%). FDG-PET enables the differentiation of atypical neurodegenerative parkinsonian syndromes from the idiopathic parkinsonian syndrome (i.e., Parkinson's disease proper) with high sensitivity and specificity (both approximately 90%), when the clinical diagnosis by a movement disorders specialist over the course of the disease is used as a reference.

CONCLUSION

DAT-SPECT has been well documented to be highly diagnostically accurate and to have a relevant influence on the diagnosis and treatment of patients with clinically uncertain parkinsonian or tremor syndrome. It has not yet been shown to improve patient-relevant endpoints such as mortality, morbidity, and health-related quality of life; proof of this will probably have to await the introduction of neuroprotective treatments. The current evidence for the high differential diagnostic accuracy of FDG-PET in neurodegenerative parkinsonian syndromes needs to be reinforced by prospective studies with neuropathological verification of the diagnosis.

KTN1 Variants Underlying Putamen Gray Matter Volumes and Parkinson's Disease

Background

Selective loss of dopaminergic neurons and diminished putamen gray matter volume (GMV) represents a central feature of Parkinson’s disease (PD). Recent studies have reported specific effects of kinectin 1 gene (KTN1) variants on the putamen GMV.

Objective

To examine the relationship of KTN1 variants, KTN1 mRNA expression in the putamen and substantia nigra pars compacta (SNc), putamen GMV, and PD.

Methods

We examined the associations between PD and a total of 1847 imputed KTN1 single nucleotide polymorphisms (SNPs) in one discovery sample [2,000 subjects with PD vs. 1,986 healthy controls (HC)], and confirmed the nominally significant associations (p < 0.05) in two replication samples (900 PD vs. 867 HC, and 940 PD vs. 801 HC, respectively). The regulatory effects of risk variants on the KTN1 mRNA expression in putamen and SNc and the putamen GMV were tested. We also quantified the expression levels of KTN1 mRNA in the putamen and/or SNc for comparison between PD and HC in five independent cohorts.

Results

Six replicable and two non-replicable KTN1-PD associations were identified (0.009 ≤ p ≤ 0.049). The major alleles of five SNPs, including rs12880292, rs8017172, rs17253792, rs945270, and rs4144657, significantly increased risk for PD (0.020 ≤ p ≤ 0.049) and putamen GMVs (19.08 ≤ β ≤ 60.38; 2.82 ≤ Z ≤ 15.03; 5.0 × 10^–51 ≤ p ≤ 0.018). The risk alleles of five SNPs, including rs8017172, rs17253792, rs945270, rs4144657, and rs1188184 also significantly increased the KTN1 mRNA expression in the putamen or SNc (0.021 ≤ p ≤ 0.046). The KTN1 mRNA was abundant in the putamen and/or SNc across five independent cohorts and differentially expressed in the SNc between PD and HC in one cohort (p = 0.047).

Conclusion

There was a consistent, significant, replicable, and robust positive relationship among the KTN1 variants, PD risk, KTN1 mRNA expression in putamen, and putamen volumes, and a modest relation between PD risk and KTN1 mRNA expression in SNc, suggesting that KTN1 may play a functional role in the development of PD.

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.

Gray Matter Atrophy in Parkinson's Disease and the Parkinsonian Variant of Multiple System Atrophy: A Combined ROI- and Voxel-Based Morphometric Study

OBJECTIVES

Parkinson's disease (PD) and the parkinsonian variant of multiple system atrophy (MSA-P) are distinct neurodegenerative disorders that share similar clinical features of parkinsonism. The morphological alterations of these diseases have yet to be understood. The purpose of this study was to evaluate gray matter atrophy in PD and MSA-P using regions of interest (ROI)-based measurements and voxel-based morphometry (VBM).

METHODS

We studied 41 patients with PD, 20 patients with MSA-P, and 39 controls matched for age, sex, and handedness using an improved T1-weighted sequence that eased gray matter segmentation. The gray matter volumes were measured using ROI and VBM.

RESULTS

ROI volumetric measurements showed significantly reduced bilateral putamen volumes in MSA-P patients compared with those in PD patients and controls (p<0.05), and the volumes of the bilateral caudate nucleus were significantly reduced in both MSA-P and PD patients compared with those in the controls (p<0.05). VBM analysis revealed multifocal cortical and subcortical atrophy in both MSA-P and PD patients, and the volumes of the cerebellum and temporal lobes were remarkably reduced in MSA-P patients compared with the volumes in PD patients (p<0.05).

CONCLUSIONS

Both PD and MSA-P are associated with gray matter atrophy, which mainly involves the bilateral putamen, caudate nucleus, cerebellum, and temporal lobes. ROI and VBM can be used to identify these morphological alterations, and VBM is more sensitive and repeatable and less time-consuming, which may have potential diagnostic value.

Semi-Automatic Signature-Based Segmentation Method for Quantification of Neuromelanin in Substantia Nigra

In Parkinson's disease (PD), there is a reduction of neuromelanin (NM) in the substantia nigra (SN). Manual quantification of the NM volume in the SN is unpractical and time-consuming; therefore, we aimed to quantify NM in the SN with a novel semi-automatic segmentation method. Twenty patients with PD and twelve healthy subjects (HC) were included in this study. T1-weighted spectral pre-saturation with inversion recovery (SPIR) images were acquired on a 3T scanner. Manual and semi-automatic atlas-free local statistics signature-based segmentations measured the surface and volume of SN, respectively. Midbrain volume (MV) was calculated to normalize the data. Receiver operating characteristic (ROC) analysis was performed to determine the sensitivity and specificity of both methods. PD patients had significantly lower SN mean surface (37.7 ± 8.0 vs. 56.9 ± 6.6 mm²) and volume (235.1 ± 45.4 vs. 382.9 ± 100.5 mm³) than HC. After normalization with MV, the difference remained significant. For surface, sensitivity and specificity were 91.7 and 95 percent, respectively. For volume, sensitivity and specificity were 91.7 and 90 percent, respectively. Manual and semi-automatic segmentation methods of the SN reliably distinguished between PD patients and HC. ROC analysis shows the high sensitivity and specificity of both methods.

Putamen gray matter volumes in neuropsychiatric and neurodegenerative disorders

Putamen is enriched with dopamine and associated with dopamine-related phenotypes including many neuropsychiatric and neurodegenerative disorders that manifest with motor impairment, impulsive behavior, and cognitive deficits. The gray matter volume of the putamen is age-dependent and genetically controlled. In most neuropsychiatric and neurodegenerative disorders, including Parkinson's spectrum disorders, Huntington's disease, dementia with Lewy bodies, Alzheimer's disease, multiple sclerosis, attention deficit hyperactivity disorder, developmental dyslexia, and major depression, the putamen volume is significantly reduced. On the other hand, in individuals with bipolar disorder, schizophrenia spectrum disorders, especially neuroleptics-medicated patients with schizophrenia, autism spectrum disorders, obsessive-compulsive spectrum disorders, and cocaine/amphetamine dependence, the putamen volume is significantly enlarged. Therefore, the putamen volume may serve as a structural neural marker for many neuropsychiatric and neurodegenerative disorders and a predictor of treatment outcomes in individuals afflicted with these conditions. We provided an overview of the genetic bases of putamen volume and explored potential mechanisms whereby altered putamen volume manifests in these neuropsychiatric and neurodegenerative conditions, with a specific focus on dopaminergic processes.

An update on advances in magnetic resonance imaging of multiple system atrophy

In this review, we describe how different neuroimaging tools have been used to identify novel MSA biomarkers, highlighting their advantages and limitations. First, we describe the main structural MRI changes frequently associated with MSA including the 'hot cross-bun' and 'putaminal rim' signs as well as putaminal, pontine, and middle cerebellar peduncle (MCP) atrophy. We discuss the sensitivity and specificity of different supra- and infratentorial changes in differentiating MSA from other disorders, highlighting those that can improve diagnostic accuracy, including the MCP width and MCP/superior cerebellar peduncle (SCP) ratio on T1-weighted imaging, raised putaminal diffusivity on diffusion-weighted imaging, and increased T2* signal in the putamen, striatum, and substantia nigra on susceptibility-weighted imaging. Second, we focus on recent advances in structural and functional MRI techniques including diffusion tensor imaging (DTI), resting-state functional MRI (fMRI), and arterial spin labelling (ASL) imaging. Finally, we discuss new approaches for MSA research such as multimodal neuroimaging strategies and how such markers may be applied in clinical trials to provide crucial data for accurately selecting patients and to act as secondary outcome measures.

Recent Advances in Biomarkers for Parkinson's Disease

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

Structural MRI in Idiopathic Parkinson's Disease

Among modern neuroimaging modalities, magnetic resonance imaging (MRI) is a widely available, non-invasive, and cost-effective method to detect structural and functional abnormalities related to neurodegenerative disorders. In the last decades, MRI have been widely implemented to support PD diagnosis as well as to provide further insights into motor and non-motor symptoms pathophysiology, complications and treatment-related effects. Different aspects of the brain morphology and function may be derived from a single scan, by applying different analytic approaches. Biomarkers of neurodegeneration as well as tissue microstructural changes may be extracted from structural MRI techniques. In this chapter, we analyze the role of structural imaging to differentiate PD patients from controls and to define neural substrates of motor and non-motor PD symptoms. Evidence collected in the premotor PD phase will be also critically discussed. White matter as well as gray matter integrity imaging studies has been reviewed, aiming to highlight points of strength and limits to their potential application in clinical settings.

99mTc-TRODAT-1 SPECT/CT imaging as a complementary biomarker in the diagnosis of parkinsonian syndromes

INTRODUCTION

Parkinson's disease (PD) and Parkinson plus syndromes (PPS) are neurodegenerative movement disorders caused by loss of dopamine in the basal ganglia. The diagnosis of both PD and PPS is complex as it is made solely on the basis of clinical features, with no established imaging modality to aid in the diagnosis. Technetium-99m-labeled tropane derivative (Tc-TRODAT-1) binds to the dopamine transporters present in the presynaptic membrane of the dopaminergic nerve terminal. The aim of this prospective study was to investigate the potential usefulness of Tc-TRODAT-1 imaging in the diagnosis of PD and PPS.

PATIENTS AND METHODS

Fifty-eight patients with a clinical diagnosis of idiopathic PD or PPS were recruited. The severity of the disease was assessed using the Hoehn and Yahr scale. Patients in stage I and II were considered as cases of Early PD. Twenty-five apparently healthy volunteers served as controls. Brain single-photon emission computed tomography/computed tomography in all the participants was performed 3-4 h after an injection of Tc-TRODAT-1. Specific uptake ratios (SURs) of striatum were calculated for both the left and right striatum, and the values were compared between PD, PPS, and healthy volunteers.

RESULTS

A significant lower uptake of tracer activity was found in either of the striatum in PD and PPS cases compared with the control group, which showed a symmetrical comma-shaped striatal uptake. This was also reflected in the SUR values, which were significantly higher in the control group in comparison with the PD and PPS patients (P<0.001). A significant difference was also found in the SUR values between the cases of early PD and control group (P<0.001).No significant difference was noted among the SUR values in different Hoehn and Yahr stages.

CONCLUSION

For clinical practice, both the visual analysis and the quantitative parameters of Tc-TRODAT-1 single-photon emission computed tomography/computed tomography showed usefulness in distinguishing cases of PD and PPS from the healthy individuals.

Diagnosis of multiple system atrophy

Multiple system atrophy (MSA) may be difficult to distinguish clinically from other disorders, particularly in the early stages of the disease. An autonomic-only presentation can be indistinguishable from pure autonomic failure. Patients presenting with parkinsonism may be misdiagnosed as having Parkinson disease. Patients presenting with the cerebellar phenotype of MSA can mimic other adult-onset ataxias due to alcohol, chemotherapeutic agents, lead, lithium, and toluene, or vitamin E deficiency, as well as paraneoplastic, autoimmune, or genetic ataxias. A careful medical history and meticulous neurological examination remain the cornerstone for the accurate diagnosis of MSA. Ancillary investigations are helpful to support the diagnosis, rule out potential mimics, and define therapeutic strategies. This review summarizes diagnostic investigations useful in the differential diagnosis of patients with suspected MSA. Currently used techniques include structural and functional brain imaging, cardiac sympathetic imaging, cardiovascular autonomic testing, olfactory testing, sleep study, urological evaluation, and dysphagia and cognitive assessments. Despite advances in the diagnostic tools for MSA in recent years and the availability of consensus criteria for clinical diagnosis, the diagnostic accuracy of MSA remains sub-optimal. As other diagnostic tools emerge, including skin biopsy, retinal biomarkers, blood and cerebrospinal fluid biomarkers, and advanced genetic testing, a more accurate and earlier recognition of MSA should be possible, even in the prodromal stages. This has important implications as misdiagnosis can result in inappropriate treatment, patient and family distress, and erroneous eligibility for clinical trials of disease-modifying drugs.

The Bulbocavernosus Reflex in the Differential Diagnosis of Multiple System Atrophy with Predominant Parkinsonism and Parkinson's Disease

Multiple system atrophy with predominant parkinsonism (MSA-P) is a degenerative disorder that presents with autonomic dysfunction, atypical parkinsonism, and ataxia. Parkinson's disease (PD) is an age-related neurological disorder of the central nervous system. Differentiation between MSA-P and PD is important because treatments, complications, and prognoses differ. The bulbocavernosus reflex (BCR) tests the afferent and efferent signals of the pudendal nerve as well as the sacral cord. In this study, we investigated differences in BCR parameters between MSA-P and PD patients. Thirty-eight MSA-P patients and 32 PD patients were selected to participate in our electrophysiological investigations. The Keypoint EMG/EP system was used to induce the BCR, and latencies and amplitudes were recorded for systematic statistical analyses. Area under the curve of the receiver operating characteristic was used to assess the specificity and sensitivity of the BCR parameters. A BCR was elicited in 76.32% of MSA-P patients and 93.75% of PD patients. The BCR latencies of the MSA-P group were longer than those of the PD group (p < 0.001). In addition, the MSA-P group had a lower BCR amplitude compared to the PD and control groups (p < 0.001). We discovered the difference between MSA-P and PD through BCR latencies and amplitudes. Compared to PD patients, MSA-P patients have longer latencies and lower amplitudes. Therefore, the BCR may be used to discriminate between MSA-P and PD in some cases.

White matter alterations in Parkinson's disease with normal cognition precede grey matter atrophy

INTRODUCTION

While progressive MRI brain changes characterize advanced Parkinson's disease (PD), little has been discovered about structural alterations in the earliest phase of the disease, i.e. in patients with motor symptoms and with normal cognition. Our study aimed to detect grey matter (GM) and white matter (WM) changes in PD patients without cognitive impairment.

METHODS

Twenty PD patients and twenty-one healthy controls (HC) were tested for attention, executive function, working memory, and visuospatial and language domains. High-resolution T1-weighted and 60 directional diffusion-weighted 3T MRI images were acquired. The cortical, deep GM and WM volumes and density, as well as the diffusion properties of WM, were calculated. Analyses were repeated on data flipped to the side of the disease origin.

RESULTS

PD patients did not show any significant differences from HC in cognitive functioning or in brain volumes. Decreased GM intensity was found in the left superior parietal lobe in the right (p<0.02) and left (p<0.01) flipped data. The analysis of original, un-flipped data demonstrated elevated axial diffusivity (p<0.01) in the superior and anterior corona radiata, internal capsule, and external capsule in the left hemisphere of PD relative to HC, while higher mean and radial diffusivity were discovered in the right (p<0.02 and p<0.03, respectively) and left (p<0.02 and p<0.02, respectively) in the fronto-temporal WM utilizing flipped data.

CONCLUSIONS

PD patients without cognitive impairment and GM atrophy demonstrated widespread alterations of WM microstructure. Thus, WM impairment in PD might be a sensitive sign preceding the neuronal loss in associated GM regions.

Differentiation of neurodegenerative parkinsonian syndromes by volumetric magnetic resonance imaging analysis and support vector machine classification

BACKGROUND

Clinical differentiation of parkinsonian syndromes is still challenging.

OBJECTIVES

A fully automated method for quantitative MRI analysis using atlas-based volumetry combined with support vector machine classification was evaluated for differentiation of parkinsonian syndromes in a multicenter study.

METHODS

Atlas-based volumetry was performed on MRI data of healthy controls (n = 73) and patients with PD (204), PSP with Richardson's syndrome phenotype (106), MSA of the cerebellar type (21), and MSA of the Parkinsonian type (60), acquired on different scanners. Volumetric results were used as input for support vector machine classification of single subjects with leave-one-out cross-validation.

RESULTS

The largest atrophy compared to controls was found for PSP with Richardson's syndrome phenotype patients in midbrain (-15%), midsagittal midbrain tegmentum plane (-20%), and superior cerebellar peduncles (-13%), for MSA of the cerebellar type in pons (-33%), cerebellum (-23%), and middle cerebellar peduncles (-36%), and for MSA of the parkinsonian type in the putamen (-23%). The majority of binary support vector machine classifications between the groups resulted in balanced accuracies of >80%. With MSA of the cerebellar and parkinsonian type combined in one group, support vector machine classification of PD, PSP and MSA achieved sensitivities of 79% to 87% and specificities of 87% to 96%. Extraction of weighting factors confirmed that midbrain, basal ganglia, and cerebellar peduncles had the largest relevance for classification.

CONCLUSIONS

Brain volumetry combined with support vector machine classification allowed for reliable automated differentiation of parkinsonian syndromes on single-patient level even for MRI acquired on different scanners. © 2016 International Parkinson and Movement Disorder Society.

MicroRNA-101 Modulates Autophagy and Oligodendroglial Alpha-Synuclein Accumulation in Multiple System Atrophy

Synucleinopathies, neurodegenerative disorders with alpha-synuclein (α-syn) accumulation, are the second leading cause of neurodegeneration in the elderly, however no effective disease-modifying alternatives exist for these diseases. Multiple system atrophy (MSA) is a fatal synucleinopathy characterized by the accumulation of toxic aggregates of α-syn within oligodendroglial cells, leading to demyelination and neurodegeneration, and the reduction of this accumulation might halt the fast progression of MSA. In this sense, the involvement of microRNAs (miRNAs) in synucleinopathies is yet poorly understood, and the potential of manipulating miRNA levels as a therapeutic tool is underexplored. In this study, we analyzed the levels of miRNAs that regulate the expression of autophagy genes in MSA cases, and investigated the mechanistic correlates of miRNA dysregulation in in vitro models of synucleinopathy. We found that microRNA-101 (miR-101) was significantly increased in the striatum of MSA patients, together with a reduction in the expression of its predicted target gene RAB5A. Overexpression of miR-101 in oligodendroglial cell cultures resulted in a significant increase in α-syn accumulation, along with autophagy deficits. Opposite results were observed upon expression of an antisense construct targeting miR-101. Stereotaxic delivery of a lentiviral construct expressing anti-miR-101 into the striatum of the MBP-α-syn transgenic (tg) mouse model of MSA resulted in reduced oligodendroglial α-syn accumulation and improved autophagy. These results suggest that miRNA dysregulation contributes to MSA pathology, with miR-101 alterations potentially mediating autophagy impairments. Therefore, therapies targeting miR-101 may represent promising approaches for MSA and related neuropathologies with autophagy dysfunction.

Neuroimaging in Dementia

Although the diagnosis of dementia still is primarily based on clinical criteria, neuroimaging is playing an increasingly important role. This is in large part due to advances in techniques that can assist with discriminating between different syndromes. Magnetic resonance imaging remains at the core of differential diagnosis, with specific patterns of cortical and subcortical changes having diagnostic significance. Recent developments in molecular PET imaging techniques have opened the door for not only antemortem but early, even preclinical, diagnosis of underlying pathology. This is vital, as treatment trials are underway for pharmacological agents with specific molecular targets, and numerous failed trials suggest that earlier treatment is needed. This article provides an overview of classic neuroimaging findings as well as new and cutting-edge research techniques that assist with clinical diagnosis of a range of dementia syndromes, with an emphasis on studies using pathologically proven cases.

Altered Functional and Causal Connectivity of Cerebello-Cortical Circuits between Multiple System Atrophy (Parkinsonian Type) and Parkinson's Disease

Lesions of the cerebellum lead to motor and non-motor deficits by influencing cerebral cortex activity via cerebello-cortical circuits. It remains unknown whether the cerebello-cortical “disconnection” underlies motor and non-motor impairments both in the parkinsonian variant of multiple system atrophy (MSA-P) and Parkinson’s disease (PD). In this study, we investigated both the functional and effective connectivity of the cerebello-cortical circuits from resting-state functional magnetic resonance imaging (rs-fMRI) data of three groups (26 MSA-P patients, 31 PD patients, and 30 controls). Correlation analysis was performed between the causal connectivity and clinical scores. PD patients showed a weakened cerebellar dentate nucleus (DN) functional coupling in the posterior cingulate cortex (PCC) and inferior parietal lobe compared with MSA-P or controls. MSA-P patients exhibited significantly enhanced effective connectivity from the DN to PCC compared with PD patients or controls, as well as declined causal connectivity from the left precentral gyrus to right DN compared with the controls, and this value is significantly correlated with the motor symptom scores. Our findings demonstrated a crucial role for the cerebello-cortical networks in both MSA-P and PD patients in addition to striatal-thalamo-cortical (STC) networks and indicated that different patterns of cerebello-cortical loop degeneration are involved in the development of the diseases.

Stereological Evaluation of the Brains in Patients with Parkinson’s disease Compared to Controls

Parkinson’s disease (PD) is a chronic and progressive neurological disorder. A tetrad of bradykinesia, rigidity, tremor and postural instability are the core features of the disease. The aim of this study was to evaluate stereological changes in the brain of patients with PD and compare them with that of healthy controls. This case-control study was conducted on 29 patients with PD and 12 controls (C) in Zahedan, Iran. All subjects enrolled into the study through the convenience sampling method. MRI images of the brains of two groups in frontal and sagittal axis with consecutive 5mm distance slices were captured. Parameters including total volume (V) and volume density (Vv) of different parts of the brain were estimated based on Cavalries’ point counting stereological method. To analyze the data, descriptive statistics, Mann-Whitney U-Test applied for comparing the PD and C groups were used. Significance level was set at p<0.05. Our study showed that the volume of the brain and total volume and volume density (Vv) of cerebral hemispheres, cerebellum, ventricles, hippocampus, pons, mid brain and superior cerebellar peduncles in the PD group did not indicate significant difference from the control group. Total volume of brain stem in PD group wasn’t significantly different from the control group. The volume density of brain stem (p= 0.012) and total volume and volume density of middle cerebellar peduncle (p< 0.0001) in PD group were significantly larger than the control group. This study shows that PD stereological parameters related to volume and volume density of middle cerebellar peduncle and volume density of brain stem were significantly larger in patients compared to the controls. Therefore, stereological parameters can be used for early diagnosis and probably for follow-up in patients with PD.

Multivariate Analysis of 18F-DMFP PET Data to Assist the Diagnosis of Parkinsonism

An early and differential diagnosis of parkinsonian syndromes still remains a challenge mainly due to the similarity of their symptoms during the onset of the disease. Recently, ¹⁸F-Desmethoxyfallypride (DMFP) has been suggested to increase the diagnostic precision as it is an effective radioligand that allows us to analyze post-synaptic dopamine D_2/3 receptors. Nevertheless, the analysis of these data is still poorly covered and its use limited. In order to address this challenge, this paper shows a novel model to automatically distinguish idiopathic parkinsonism from non-idiopathic variants using DMFP data. The proposed method is based on a multiple kernel support vector machine and uses the linear version of this classifier to identify some regions of interest: the olfactory bulb, thalamus, and supplementary motor area. We evaluated the proposed model for both, the binary separation of idiopathic and non-idiopathic parkinsonism and the multigroup separation of parkinsonian variants. These systems achieved accuracy rates higher than 70%, outperforming DaTSCAN neuroimages for this purpose. In addition, a system that combined DaTSCAN and DMFP data was assessed.

Imaging biomarkers in Parkinson's disease and Parkinsonian syndromes: current and emerging concepts

Two centuries ago in 1817, James Parkinson provided the first medical description of Parkinson’s disease, later refined by Jean-Martin Charcot in the mid-to-late 19th century to include the atypical parkinsonian variants (also termed, Parkinson-plus syndromes). Today, Parkinson’s disease represents the second most common neurodegenerative disorder with an estimated global prevalence of over 10 million. Conversely, atypical parkinsonian syndromes encompass a group of relatively heterogeneous disorders that may share some clinical features with Parkinson’s disease, but are uncommon distinct clinicopathological diseases. Decades of scientific advancements have vastly improved our understanding of these disorders, including improvements in in vivo imaging for biomarker identification. Multimodal imaging for the visualization of structural and functional brain changes is especially important, as it allows a ‘window’ into the underlying pathophysiological abnormalities. In this article, we first present an overview of the cardinal clinical and neuropathological features of, 1) synucleinopathies: Parkinson’s disease and other Lewy body spectrum disorders, as well as multiple system atrophy, and 2) tauopathies: progressive supranuclear palsy, and corticobasal degeneration. A comprehensive presentation of well-established and emerging imaging biomarkers for each disorder are then discussed. Biomarkers for the following imaging modalities are reviewed: 1) structural magnetic resonance imaging (MRI) using T1, T2, and susceptibility-weighted sequences for volumetric and voxel-based morphometric analyses, as well as MRI derived visual signatures, 2) diffusion tensor MRI for the assessment of white matter tract injury and microstructural integrity, 3) proton magnetic resonance spectroscopy for quantifying proton-containing brain metabolites, 4) single photon emission computed tomography for the evaluation of nigrostriatal integrity (as assessed by presynaptic dopamine transporters and postsynaptic dopamine D2 receptors), and cerebral perfusion, 5) positron emission tomography for gauging nigrostriatal functions, glucose metabolism, amyloid and tau molecular imaging, as well as neuroinflammation, 6) myocardial scintigraphy for dysautonomia, and 7) transcranial sonography for measuring substantia nigra and lentiform nucleus echogenicity. Imaging biomarkers, using the ‘multimodal approach’, may aid in making early, accurate and objective diagnostic decisions, highlight neuroanatomical and pathophysiological mechanisms, as well as assist in evaluating disease progression and therapeutic responses to drugs in clinical trials.

A systematic review of lessons learned from PET molecular imaging research in atypical parkinsonism

PURPOSE

To systematically review the previous studies and current status of positron emission tomography (PET) molecular imaging research in atypical parkinsonism.

METHODS

MEDLINE, ISI Web of Science, Cochrane Library, and Scopus electronic databases were searched for articles published until 29th March 2016 and included brain PET studies in progressive supranuclear palsy (PSP), multiple system atrophy (MSA), and corticobasal syndrome (CBS). Only articles published in English and in peer-reviewed journals were included in this review. Case-reports, reviews, and non-human studies were excluded.

RESULTS

Seventy-seven PET studies investigating the dopaminergic system, glucose metabolism, microglial activation, hyperphosphorilated tau, opioid receptors, the cholinergic system, and GABA_A receptors in PSP, MSA, and CBS patients were included in this review. Disease-specific patterns of reduced glucose metabolism have shown higher accuracy than dopaminergic imaging techniques to distinguish between parkinsonian syndromes. Microglial activation has been found in all forms of atypical parkinsonism and reflects the known distribution of neuropathologic changes in these disorders. Opioid receptors are decreased in the striatum of PSP and MSA patients. Subcortical cholinergic dysfunction was more severe in MSA and PSP than Parkinson's disease patients although no significant changes in cortical cholinergic receptors were seen in PSP with cognitive impairment. GABA_A receptors were decreased in metabolically affected cortical and subcortical regions in PSP patients.

CONCLUSIONS

PET molecular imaging has provided valuable insight for understanding the mechanisms underlying atypical parkinsonism. Changes at a molecular level occur early in the course of these neurodegenerative diseases and PET imaging provides the means to aid differential diagnosis, monitor disease progression, identify of novel targets for pharmacotherapy, and monitor response to new treatments.