Quantification of Parkinson's disease-related network expression with ECD SPECT

99mTc-HMPAO SPECT Perfusion Signatures Associated With Clinical Progression in Patients With Isolated REM Sleep Behavior Disorder

BACKGROUND AND OBJECTIVES

Idiopathic/isolated REM sleep behavior disorder (iRBD) is associated with dementia with Lewy bodies and Parkinson disease. Despite evidence of abnormal cerebral perfusion in iRBD, there is currently no pattern that can predict whether an individual will develop dementia with Lewy bodies or Parkinson disease. The objective was to identify a perfusion signature associated with conversion to dementia with Lewy bodies in iRBD.

METHODS

Patients with iRBD underwent video-polysomnography, neurologic and neuropsychological assessments, and baseline 99mTc-HMPAO SPECT to assess relative cerebral blood flow. Partial least squares correlation was used to identify latent variables that maximized covariance between 27 clinical features and relative gray matter perfusion. Patient-specific scores on the latent variables were used to test the association with conversion to dementia with Lewy bodies compared with that with Parkinson disease. The signature's expression was also assessed in 24 patients with iRBD who underwent a second perfusion scan, 22 healthy controls, and 19 individuals with Parkinson disease.

RESULTS

Of the 137 participants, 93 underwent SPECT processing, namely 52 patients with iRBD (67.9 years, 73% men), 19 patients with Parkinson disease (67.3 years, 37% men), and 22 controls (67.0 years, 73% men). Of the 47 patients with iRBD followed up longitudinally (4.5 years), 12 (26%) developed a manifest synucleinopathy (4 dementia with Lewy bodies and 8 Parkinson disease). Analysis revealed 2 latent variables between relative blood flow and clinical features: the first was associated with a broad set of features that included motor, cognitive, and perceptual variables, age, and sex; the second was mostly associated with cognitive features and RBD duration. When brought back into the patient's space, the expression of the first variable was associated with conversion to a manifest synucleinopathy, whereas the second was associated with conversion to dementia with Lewy bodies. The expression of the patterns changed over time and was associated with worse motor features.

DISCUSSION

This study identified a brain perfusion signature associated with cognitive impairment in iRBD and transition to dementia with Lewy bodies. This signature, which can be derived from individual scans, has the potential to be developed into a biomarker that predicts dementia with Lewy bodies in at-risk individuals.

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.

Regional cortical hypoperfusion and atrophy correlate with striatal dopaminergic loss in Parkinson's disease: a study using arterial spin labeling MR perfusion

PURPOSE

To investigate the relationship of the striatal dopamine transporter density to changes in the gray matter (GM) volume and cerebral perfusion in patients with Parkinson's disease (PD).

METHODS

We evaluated the regional cerebral blood flow (CBF) and GM volume, concurrently measured using arterial spin labeling and T1-weighted magnetic resonance imaging, respectively, as well as the striatal specific binding ratio (SBR) in ¹²³I-N-ω-fluoropropyl-2β-carboxymethoxy-3β-(4-iodophenyl)nortropane (¹²³I-FP-CIT) single-photon emission computed tomography in 30 non-demented patients with PD (15 men and 15 women; mean age, 67.2 ± 8.8 years; mean Hoehn-Yahr stage, 2.2 ± 0.9). Voxel-wise regression analyses using statistical parametric mapping (SPM) were performed to explore the brain regions that showed correlations of the striatal SBR to the GM volume and CBF, respectively, with a height threshold of p < 0.0005 at the voxel level and p < 0.05 family-wise error-corrected at the cluster level.

RESULTS

SPM analysis showed a significant positive correlation between the SBR and GM volume in the inferior frontal gyrus (IFG). Whereas, a positive correlation between the SBR and CBF was widely found in the frontotemporal and parietotemporal regions, including the IFG. Notably, the opercular part of the IFG showed significant correlations in both SPM analyses of the GM volume (r² = 0.90, p < 0.0001) and CBF (r² = 0.88, p < 0.0001).

CONCLUSION

The voxel-wise analyses revealed the brain regions, mainly the IFG, that showed hypoperfusion and atrophy related to dopaminergic loss, which suggests that the progression of dopaminergic neurodegeneration leads to regional cortical dysfunction in PD.

Implementation of functional imaging using 11C-methionine PET-CT co-registered with MRI for advanced surgical planning and decision making in prolactinoma surgery

PURPOSE

To report the first experience of our multidisciplinary team with functional imaging using ¹¹C-methionine positron emission tomography-computed tomography (¹¹C-methionine PET-CT) co-registered with MRI (Met-PET/MRI^CR) in clinical decision making and surgical planning of patients with difficult to treat prolactinoma.

METHODS

In eighteen patients with prolactinoma, referred to our tertiary referral centre because of intolerance or resistance for dopamine agonists (DA), Met-PET/MRI^CR was used to aid decision-making regarding therapy.

RESULTS

Met-PET/MRI^CR was positive in 94% of the patients. MRI and Met-PET/MRI^CR findings were completely concordant in five patients, partially concordant in nine patients, and non-concordant in four patients. In five patients Met-PET/MRI^CR identified lesion(s) that were retrospectively also visible on MRI. Met-PET/MRI^CR was false negative in one patient, with a cystic adenoma on conventional MRI. Thirteen patients underwent transsphenoidal surgery, with nine achieving full biochemical remission, two clinical improvement and near normalized prolactin levels, and one patient clinical improvement with significant tumour reduction. Hence, nearly all patients (94%) were considered to have a positive outcome. Permanent complication rate was low. Three patients continued DA, two patients have a wait and scan policy.

CONCLUSION

Met-PET/MRI^CR can provide additional information to guide multidisciplinary preoperative and intraoperative decision making in selected cases of prolactinoma. This approach resulted in a high remission rate with a low rate of complications in our expert centre.

The role of neuroimaging in Parkinson's disease

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

Spatial Covariance of Cholinergic Muscarinic M1 /M4 Receptors in Parkinson's Disease

BACKGROUND

Parkinson's disease (PD) is associated with cholinergic dysfunction, although the role of M1 and M4 receptors remains unclear.

OBJECTIVE

To investigate spatial covariance patterns of cholinergic muscarinic M₁ /M₄ receptors in PD and their relationship with cognition and motor symptoms.

METHODS

Some 19 PD and 24 older adult controls underwent ¹²³ I-iodo-quinuclidinyl-benzilate (QNB) (M₁ /M₄ receptor) and ^99m Tc-exametazime (perfusion) single-photon emission computed tomography (SPECT) scanning. We implemented voxel principal components analysis, producing a series of images representing patterns of intercorrelated voxels across individuals. Linear regression analyses derived specific M₁ /M₄ spatial covariance patterns associated with PD.

RESULTS

A cholinergic M₁ /M₄ pattern that converged onto key hubs of the default, auditory-visual, salience, and sensorimotor networks fully discriminated PD patients from controls (F_1,41  = 135.4, P < 0.001). In PD, we derived M₁ /M₄ patterns that correlated with global cognition (r = -0.62, P = 0.008) and motor severity (r = 0.53, P = 0.02). Both patterns emerged with a shared topography implicating the basal forebrain as well as visual, frontal executive, and salience circuits. Further, we found a M₁ /M₄ pattern that predicted global cognitive decline (r = 0.46, P = 0.04) comprising relative decreased binding within default and frontal executive networks.

CONCLUSIONS

Cholinergic muscarinic M₁ /M₄ modulation within key brain networks were apparent in PD. Cognition and motor severity were associated with a similar topography, inferring both phenotypes possibly rely on related cholinergic mechanisms. Relative decreased M₁ /M₄ binding within default and frontal executive networks could be an indicator of future cognitive decline. © 2021 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

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.

Differences in cerebellar perfusion between Parkinson's disease and multiple system atrophy

INTRODUCTION

Objective biomarkers are required for differential diagnosis of Parkinson's disease (PD), multiple system atrophy (MSA) and progressive supranuclear palsy (PSP).

OBJECTIVE

We aimed to determine if cerebellar blood flow, measured using N-isopropyl-[¹²³I] p-iodoamphetamine single photon emission computed tomography (¹²³I -IMP-SPECT), was useful for differentiating between PD, MSA and PSP.

METHODS

Twenty-four patients with PD, seventeen patients with MSA with predominant parkinsonian features (MSA-P), sixteenth patients with MSA with predominant cerebellar ataxia (MSA-C) and eight patients with PSP were enrolled. Twenty-seven normal controls' data were used for the calculation of z score. All patients underwent ¹²³I -IMP-SPECT, and data were analyzed using a three-dimensional-stereotactic surface projection program.

RESULTS

Cerebellar perfusion in MSA-P (MSA-P vs PD, P = .002; MSA-P vs PSP, P < .001) and MSA-C (MSA-C vs PD, P < .001; MSA-C vs PSP, P < .001) were significantly decreased compared with PD or PSP. There was no significant difference in perfusion between PD and PSP groups (P = .061). The area under the receiver operating characteristic curve for cerebellar perfusion between MSA-P and PD was 0.858.

CONCLUSION

Our findings revealed that cerebellar perfusion by ¹²³I-IMP-SPECT was useful for differentiating between PD and MSA-P.

The application of positron emission tomography (PET) imaging in CNS drug development

As drug discovery and development in Neuroscience push beyond symptom management to disease modification, neuroimaging becomes a key area of translational research that enables measurements of the presence of drugs and downstream physiological consequences of drug action within the living brain. As such, neuroimaging can be used to help optimize decision-making processes throughout the various phases of drug development. Positron Emission Tomography (PET) is a functional imaging technique that allows the quantification and visualization of biochemical processes, by monitoring the time dependent distribution of molecules labelled with short-lived positron-emitting isotopes. This review focuses on the application of PET to support CNS drug development, particularly in the early clinical phases, by allowing us to measure tissue exposure, target engagement, and pharmacological activity. We will also discuss the application of PET imaging as tools to image the pathological hallmarks of disease and evaluate the potential disease-modifying effect of candidate drugs in slowing disease progression.

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.

A New Metabolic Network Correlated with Olfactory and Executive Dysfunctions in Idiopathic Rapid Eye Movement Sleep Behavior Disorder

Background and Purpose

To identify a metabolic network reflecting neurodegeneration in patients with idiopathic rapid eye movement (REM) sleep behavior disorder (iRBD).

Methods

We recruited a prospective cohort comprising patients with de novo Parkinson's disease (PD) with probable REM sleep behavior disorder (PDRBD, n=21), polysomnography-confirmed iRBD patients (n=28), and age-matched healthy controls (HC) (n=24). PDRBD-related spatial covariance pattern (PDRBD-RP) were determined from ¹⁸F-fluorodeoxyglucose PET images of the PDRBD group and validated by reproduction in a separate PD cohort with polysomnography-confirmed REM sleep behavior disorder (n=11). We also confirmed via ¹⁸F-N-3-fluoropropyl-2β-carboxymethoxy-3β-(4-iodophenyl)-nortropane PET that none of our iRBD patients had any loss of dopamine transporters (DATs) suggestive of PD. Differences in the PDRBD-RP across groups were compared, and the clinical significance of these metabolic patterns in iRBD patients was further evaluated based on relationships with olfactory and cognitive functions, and striatal DAT densities.

Results

The PDRBD-RP reflected the previously reported PD-related covariance pattern and additionally showed relative metabolic increases in the hippocampus and premotor cortex. The PDRBD-RP gradually increased from the HC to iRBD patients and to the de novo and validation PDRBD groups. In iRBD patients, the PDRBD-RP was negatively correlated with olfactory and frontal executive functions (age-controlled p<0.01 for both), and tended to be negatively correlated with the striatal DAT density, although this was insignificant after age adjustment. During the mean follow-up period of 3.5 years, 5 of 11 iRBD patients with PDRBD-RP elevation had developed Lewy body diseases, whereas those without PDRBD-RP elevation had not.

Conclusions

Our results suggest that PDRBD-RP is an effective biomarker for monitoring the progression to neurodegenerative disease in iRBD patients.

What a neurologist should know about PET and SPECT functional imaging for parkinsonism: A practical perspective

The diagnosis of a parkinsonian syndrome based on clinical criteria remains sometimes difficult, especially at disease onset. Brain or heart molecular imaging techniques (SPECT or PET) can provide a major help to improve and speed up diagnosis, influencing treatment strategies. Presynaptic dopaminergic imaging using either [¹⁸F]-Dopa PET or ¹²³I -2β-Carbomethoxy-3β-(4-Iodophenyl)- N-(3-Fluoropropyl) Nortropane ([¹²³I]-Ioflupane)SPECT demonstrates or rules out the presence of a dopaminergic degenerative process. This allows to distinguish Parkinson's disease, Parkinson "plus" syndromes and dementia with Lewy bodies (reduced radiotracers binding) from essential tremor, psychogenic, post-neuroleptic or vascular parkinsonisms, dopa-responsive dystonia and Alzheimer's disease (normal radiotracers binding). For differential diagnosis between Parkinson's disease and Parkinson "plus" syndromes, brain molecular imaging with [¹⁸F]-Fluorodeoxyglucose ([¹⁸F]-FDG) PET or ^99mTc-HMPAO SPECT can provide useful information, whereas [¹⁸F]-Dopa PET or [¹²³I]-Ioflupane does not separate these entities. Finally, sympathetic cardiac [¹²³I]-Metaiodobenzylguanidine ([¹²³I]-MIBG) scintigraphy or SPECT can help distinguishing Parkinson's disease and dementia with Lew bodies (decreased binding) from multiple system atrophy and progressive supranuclear palsy (normal binding). New radiotracers notably those targeting the pathological process itself such as Tau aggregates are under development and may provide interesting informations to delineate the different Parkinson "plus" syndromes.

Validation of abnormal glucose metabolism associated with Parkinson's disease in Chinese participants based on 18F-fluorodeoxyglucose positron emission tomography imaging

PURPOSE

We previously identified disease-related cerebral metabolic characteristics associated with Parkinson's disease (PD) in the Chinese population using 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET)/computed tomography (CT) imaging. The present study aims to assess data reproducibility and robustness of the metabolic activity characteristics across independent cohorts.

PATIENTS AND METHODS

Forty-eight patients with PD and 48 healthy controls from Chongqing district, in addition to 33 patients with PD and 33 healthy controls from Shanghai district were recruited. Each subject underwent brain 18F-FDG PET/CT imaging in a resting state. Based on the brain images, differences between the groups and PD-related cerebral metabolic activities were graphically and quantitatively evaluated.

RESULTS

Both PD patient cohorts exhibited analogous cerebral patterns characterized by metabolic increase in the putamen, globus pallidus, thalamus, pons, sensorimotor cortex and cerebellum, along with metabolic decrease in parieto-occipital areas. Additionally, the metabolic pattern was highly indicative of the disease, with a significant elevation in PD patients compared with healthy controls (p<0.001) in both the derivation (Shanghai) and validation (Chongqing) cohorts.

CONCLUSION

This dual-center study demonstrated the high comparability and reproducibility of PD-related cerebral metabolic activity patterns across independent Chinese cohorts and may serve as an objective diagnostic marker for the disease.

Differential diagnosis of parkinsonian syndromes using dopamine transporter and perfusion SPECT

OBJECTIVE

We aimed to assess whether a combined analysis of dopamine transporter (DAT)- and perfusion-SPECT images (or either) could: (1) distinguish atypical parkinsonian syndromes (APS) from Lewy body diseases (LBD; majority Parkinson disease [PD]), and (2) differentiate among APS subgroups (progressive supranuclear palsy [PSP], corticobasal syndrome [CBS], and multiple system atrophy [MSA]).

METHODS

We recruited consecutive patients with neurodegenerative parkinsonian syndromes (LBD, n = 46; APS, n = 33). Individual [¹²³I]FP-CIT- and [¹²³I]iodoamphetamine-SPECT images were coregistered onto anatomical MRI segmented into brain regions. Striatal DAT activity and regional perfusion were extracted from each brain region for each patient and submitted to logistic regression analyses. Stepwise procedures were used to select predictors that should be included in the models to distinguish APS from LBD, and differentiate among the APS subgroups. Receiver-operating characteristic (ROC) analyses were performed to measure diagnostic power. Leave-one-out cross-validation (LOOCV) was performed to evaluate the diagnostic accuracy.

RESULTS

The model to discriminate APS from LBD showed that the area under the ROC curve (AUC) was 0.923, while the total diagnostic accuracy (TDA) was 86.1% in LOOCV. In the model to distinguish PSP, CBS, and MSA from LBD, the AUC/TDA values were 0.978/94.6%, 0.978/87.0%, and 0.880/80.3%, respectively. In the model to differentiate between CBS and MSA, MSA and PSP, and PSP and CBS, the AUC/TDA values were 0.967/91.3%, 0.920/88.0%, 0.875/77.8%, respectively.

CONCLUSION

An image-based automated classification using striatal DAT activity and regional perfusion patterns provided a good performance in the differential diagnosis of neurodegenerative parkinsonian syndromes without clinical information.

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.

Cholinergic and perfusion brain networks in Parkinson disease dementia

Objective:

To investigate muscarinic M1/M4 cholinergic networks in Parkinson disease dementia (PDD) and their association with changes in Mini-Mental State Examination (MMSE) after 12 weeks of treatment with donepezil.

Methods:

Forty-nine participants (25 PDD and 24 elderly controls) underwent ¹²³I-QNB and ^99mTc-exametazime SPECT scanning. We implemented voxel principal components (PC) analysis, producing a series of PC images of patterns of interrelated voxels across individuals. Linear regression analyses derived specific M1/M4 and perfusion spatial covariance patterns (SCPs).

Results:

We found an M1/M4 SCP of relative decreased binding in basal forebrain, temporal, striatum, insula, and anterior cingulate (F_1,47 = 31.9, p < 0.001) in cholinesterase inhibitor–naive patients with PDD, implicating limbic-paralimbic and salience cholinergic networks. The corresponding regional cerebral blood flow SCP showed relative decreased uptake in temporoparietal and prefrontal areas (F_1,47 = 177.5, p < 0.001) and nodes of the frontoparietal and default mode networks (DMN). The M1/M4 pattern that correlated with an improvement in MMSE (r = 0.58, p = 0.005) revealed relatively preserved/increased pre/medial/orbitofrontal, parietal, and posterior cingulate areas coinciding with the DMN and frontoparietal networks.

Conclusion:

Dysfunctional limbic-paralimbic and salience cholinergic networks were associated with PDD. Established cholinergic maintenance of the DMN and frontoparietal networks may be prerequisite for cognitive remediation following cholinergic treatment in this condition.

Neuroimaging of Parkinson's disease: Expanding views

Advances in molecular and structural and functional neuroimaging are rapidly expanding the complexity of neurobiological understanding of Parkinson's disease (PD). This review article begins with an introduction to PD neurobiology as a foundation for interpreting neuroimaging findings that may further lead to more integrated and comprehensive understanding of PD. Diverse areas of PD neuroimaging are then reviewed and summarized, including positron emission tomography, single photon emission computed tomography, magnetic resonance spectroscopy and imaging, transcranial sonography, magnetoencephalography, and multimodal imaging, with focus on human studies published over the last five years. These included studies on differential diagnosis, co-morbidity, genetic and prodromal PD, and treatments from L-DOPA to brain stimulation approaches, transplantation and gene therapies. Overall, neuroimaging has shown that PD is a neurodegenerative disorder involving many neurotransmitters, brain regions, structural and functional connections, and neurocognitive systems. A broad neurobiological understanding of PD will be essential for translational efforts to develop better treatments and preventive strategies. Many questions remain and we conclude with some suggestions for future directions of neuroimaging of PD.

Parkinson's disease-related spatial covariance pattern identified with resting-state functional MRI

In this study, we sought to identify a disease-related spatial covariance pattern of spontaneous neural activity in Parkinson's disease using resting-state functional magnetic resonance imaging (MRI). Time-series data were acquired in 58 patients with early to moderate stage Parkinson's disease and 54 healthy controls, and analyzed by Scaled Subprofile Model Principal Component Analysis toolbox. A split-sample analysis was also performed in a derivation sample of 28 patients and 28 control subjects and validated in a prospective testing sample of 30 patients and 26 control subjects. The topographic pattern of neural activity in Parkinson's disease was characterized by decreased activity in the striatum, supplementary motor area, middle frontal gyrus, and occipital cortex, and increased activity in the thalamus, cerebellum, precuneus, superior parietal lobule, and temporal cortex. Pattern expression was elevated in the patients compared with the controls, with a high accuracy (90%) to discriminate the patients from the controls. The split-sample analysis produced a similar pattern but with a lower accuracy for group discrimination in both the derivation (80%) and the validation (73%) samples. Our results showed that resting-state functional MRI can be potentially useful for identification of Parkinson's disease-related spatial covariance patterns, and for differentiation of Parkinson's disease patients from healthy controls at an individual level.

Dysfunctional putamen modulation during bimanual finger-to-thumb movement in patients with Parkinson's disease

Parkinson's disease (PD) is a neurodegenerative disorder affecting middle-aged and elderly people. PD can be viewed as “circuit disorder,” indicating that large scale cortico-subcortical pathways were involved in its pathophysiology. The brain network in an experimental context is emerging as an important biomarker in disease diagnosis and prognosis prediction. This context-dependent network for PD and the underling functional mechanism remains unclear. In this paper, the brain network profiles in 11 PD patients without dementia were studied and compared with 12 healthy controls. The functional magnetic resonance imaging (fMRI) data were acquired when the subjects were performing a pseudorandomized unimanual or bimanual finger-to-thumb movement task. The activation was detected and the network profiles were analyzed by psychophysiological interaction (PPI) toolbox. For the controls and PD patients, the motor areas including the primary motor and premotor areas, supplementary motor area, the cerebellum and parts of the frontal, temporal and parietal gyrus were activated. The right putamen exhibited significant control > PD activation and weaker activity during the bimanual movement relative to the unimanual movement in the control group. The decreased putamen modulation on some nucleus in basal ganglia, such as putamen, thalamus and caudate, and some cortical areas, such as cingulate, parietal, angular, frontal, temporal and occipital gyrus was detected in the bimanual movement condition relative to the unimanual movement condition. Between-group PPI difference was detected in cingulate gyrus, angular gyrus and precuneus (control > PD) and inferior frontal gyrus (PD > control). The deficient putamen activation and its enhanced connectivity with the frontal gyrus could be a correlate of impaired basal ganglia inhibition and frontal gyrus compensation to maintain the task performance during the motor programs of PD patients.

Regional covariance of muscarinic acetylcholine receptors in Alzheimer's disease using (R, R) [(123)I]-QNB SPECT

Alzheimer's disease (AD) is characterised by deficits in cholinergic neurotransmission and subsequent receptor changes. We investigated (123)I-iodo-quinuclidinyl-benzilate (QNB) SPECT images using spatial covariance analysis (SCA), to detect an M1/M4 receptor spatial covariance pattern (SCP) that distinguished AD from controls. Furthermore, a corresponding regional cerebral blood flow (rCBF) SCP was also derived. Thirty-nine subjects (15 AD and 24 healthy elderly controls) underwent (123)I-QNB and (99m)Tc-exametazime SPECT. Voxel SCA was simultaneously applied to the set of smoothed/registered scans, generating a series of eigenimages representing common intercorrelated voxels across subjects. Linear regression identified individual M1/M4 and rCBF SCPs that discriminated AD from controls. The M1/M4 SCP showed concomitant decreased uptake in medial temporal, inferior frontal, basal forebrain and cingulate relative to concomitant increased uptake in frontal poles, occipital, pre-post central and precuneus/superior parietal regions (F1,37 = 85.7, p < 0.001). A largely different perfusion SCP was obtained showing concomitant decreased rCBF in medial and superior temporal, precuneus, inferior parietal and cingulate relative to concomitant increased rCBF in cerebellum, pre-post central, putamen, fusiform and brain stem/midbrain regions (F1,37 = 77.5, p < 0.001). The M1/M4 SCP expression correlated with the duration of cognitive symptoms (r = 0.90, p < 0.001), whereas the rCBF SCP expression negatively correlated with MMSE, CAMCOG and CAMCOGmemory (r ≥ |0.63|, p ≤ 0.006). (123)I-QNB SPECT revealed an M1/M4 basocortical covariance pattern, distinct from rCBF, reflecting the duration of disease rather than current clinical symptoms. This approach could be more sensitive than univariate methods in characterising the cholinergic/rCBF changes that underpin the clinical phenotype of AD.

Update on SPECT and PET in parkinsonism - part 1: imaging for differential diagnosis

PURPOSE OF REVIEW

To give an update on recent findings concerning the use of single-photon emission computed tomography (SPECT) and positron emission tomography (PET) for differential diagnosis and prognosis of neurodegenerative parkinsonism and related disorders.

RECENT FINDINGS

Several studies confirmed the very high diagnostic accuracy and clinical impact of imaging nigrostriatal function (most notably with [I]FP-CIT-SPECT) for diagnosing neurodegenerative parkinsonism and dementia with Lewy bodies. Accurate differential diagnosis of neurodegenerative parkinsonism can be achieved by imaging disease-specific patterns of cerebral glucose metabolism with [18F]fluorodeoxyglucose-PET, which surpasses the diagnostic accuracy of other currently available radionuclide imaging techniques.

SUMMARY

SPECT and PET are established methods for the differential diagnosis of parkinsonism with significant therapeutic and prognostic impact. Given the limited accuracy of the clinical diagnosis as the reference standard, future studies with post-mortem verification are needed for validation of diagnostic imaging pattern, particularly in tauopathies.

Brain network markers of abnormal cerebral glucose metabolism and blood flow in Parkinson's disease

Neuroimaging of cerebral glucose metabolism and blood flow is ideally suited to assay widely-distributed brain circuits as a result of local molecular events and behavioral modulation in the central nervous system. With the progress in novel analytical methodology, this endeavor has succeeded in unraveling the mechanisms underlying a wide spectrum of neurodegenerative diseases. In particular, statistical brain mapping studies have made significant strides in describing the pathophysiology of Parkinson's disease (PD) and related disorders by providing signature biomarkers to determine the systemic abnormalities in brain function and evaluate disease progression, therapeutic responses, and clinical correlates in patients. In this article, we review the relevant clinical applications in patients in relation to healthy volunteers with a focus on the generation of unique spatial covariance patterns associated with the motor and cognitive symptoms underlying PD. These characteristic biomarkers can be potentially used not only to improve patient recruitment but also to predict outcomes in clinical trials.

Abnormal metabolic network activity in REM sleep behavior disorder

OBJECTIVE

To determine whether the Parkinson disease-related covariance pattern (PDRP) expression is abnormally increased in idiopathic REM sleep behavior disorder (RBD) and whether increased baseline activity is associated with greater individual risk of subsequent phenoconversion.

METHODS

For this cohort study, we recruited 2 groups of RBD and control subjects. Cohort 1 comprised 10 subjects with RBD (63.5 ± 9.4 years old) and 10 healthy volunteers (62.7 ± 8.6 years old) who underwent resting-state metabolic brain imaging with (18)F-fluorodeoxyglucose PET. Cohort 2 comprised 17 subjects with RBD (68.9 ± 4.8 years old) and 17 healthy volunteers (66.6 ± 6.0 years old) who underwent resting brain perfusion imaging with ethylcysteinate dimer SPECT. The latter group was followed clinically for 4.6 ± 2.5 years by investigators blinded to the imaging results. PDRP expression was measured in both RBD groups and compared with corresponding control values.

RESULTS

PDRP expression was elevated in both groups of subjects with RBD (cohort 1: p < 0.04; cohort 2: p < 0.005). Of the 17 subjects with long-term follow-up, 8 were diagnosed with Parkinson disease or dementia with Lewy bodies; the others did not phenoconvert. For individual subjects with RBD, final phenoconversion status was predicted using a logistical regression model based on PDRP expression and subject age at the time of imaging (r(2) = 0.64, p < 0.0001).

CONCLUSIONS

Latent network abnormalities in subjects with idiopathic RBD are associated with a greater likelihood of subsequent phenoconversion to a progressive neurodegenerative syndrome.

Covariant perfusion patterns provide clues to the origin of cognitive fluctuations and attentional dysfunction in dementia with Lewy bodies

BACKGROUND

Fluctuating cognition (FC), particularly in attention, is a core and defining symptom in dementia with Lewy bodies (DLB) but is seen much less frequently in Alzheimer's dementia (AD). However, its neurobiological origin is poorly understood. The aim of our study was therefore to characterize perfusion patterns in DLB patients that are associated with the severity and frequency of FC as measured both clinically and using objective neuropsychological assessments.

METHODS

Spatial covariance analyses were applied to data derived from single photon emission computed tomography (SPECT) HMPAO brain imaging in 19 DLB and 23 AD patients. Patients underwent clinical assessment of their FC and cognitive function as well as objective testing of their attention.

RESULTS

Covariant perfusion principal components (PCs) were not associated with either FC or cognitive or attentional measures in AD. However, in DLB patients, the second PC (defined as DLB-cognitive motor pattern, DLB-PCI2) which was characterized by bilateral relative increases in cerebellum, basal ganglia, and supplementary motor areas and widespread bilateral decreases in parietal regions, positively correlated with poorer cognitive function, increased FC and worse attentional function measured both clinically and neurophysiologically (p < 0.05) as well as with the severity of bradykinesia (p = 0.04).

CONCLUSIONS

FC in DLB appears distinct from those seen in AD, and likely to be driven by internal neurobiological perturbations in brain circuitry as evidenced using spatial covariance analyses of cerebral perfusion. FC and certain aspects of attentional dysfunction in DLB may, in part, depend upon both distributed motor and non-motor networks.

Functional reorganization of motor and limbic circuits after exercise training in a rat model of bilateral parkinsonism

Exercise training is widely used for neurorehabilitation of Parkinson's disease (PD). However, little is known about the functional reorganization of the injured brain after long-term aerobic exercise. We examined the effects of 4 weeks of forced running wheel exercise in a rat model of dopaminergic deafferentation (bilateral, dorsal striatal 6-hydroxydopamine lesions). One week after training, cerebral perfusion was mapped during treadmill walking or at rest using [(14)C]-iodoantipyrine autoradiography. Regional cerebral blood flow-related tissue radioactivity (rCBF) was analyzed in three-dimensionally reconstructed brains by statistical parametric mapping. In non-exercised rats, lesions resulted in persistent motor deficits. Compared to sham-lesioned rats, lesioned rats showed altered functional brain activation during walking, including: 1. hypoactivation of the striatum and motor cortex; 2. hyperactivation of non-lesioned areas in the basal ganglia-thalamocortical circuit; 3. functional recruitment of the red nucleus, superior colliculus and somatosensory cortex; 4. hyperactivation of the ventrolateral thalamus, cerebellar vermis and deep nuclei, suggesting recruitment of the cerebellar-thalamocortical circuit; 5. hyperactivation of limbic areas (amygdala, hippocampus, ventral striatum, septum, raphe, insula). These findings show remarkable similarities to imaging findings reported in PD patients. Exercise progressively improved motor deficits in lesioned rats, while increasing activation in dorsal striatum and rostral secondary motor cortex, attenuating a hyperemia of the zona incerta and eliciting a functional reorganization of regions participating in the cerebellar-thalamocortical circuit. Both lesions and exercise increased activation in mesolimbic areas (amygdala, hippocampus, ventral striatum, laterodorsal tegmental n., ventral pallidum), as well as in related paralimbic regions (septum, raphe, insula). Exercise, but not lesioning, resulted in decreases in rCBF in the medial prefrontal cortex (cingulate, prelimbic, infralimbic). Our results in this PD rat model uniquely highlight the breadth of functional reorganizations in motor and limbic circuits following lesion and long-term, aerobic exercise, and provide a framework for understanding the neural substrates underlying exercise-based neurorehabilitation.

Identification of disease-related spatial covariance patterns using neuroimaging data

The scaled subprofile model (SSM)(1-4) is a multivariate PCA-based algorithm that identifies major sources of variation in patient and control group brain image data while rejecting lesser components (Figure 1). Applied directly to voxel-by-voxel covariance data of steady-state multimodality images, an entire group image set can be reduced to a few significant linearly independent covariance patterns and corresponding subject scores. Each pattern, termed a group invariant subprofile (GIS), is an orthogonal principal component that represents a spatially distributed network of functionally interrelated brain regions. Large global mean scalar effects that can obscure smaller network-specific contributions are removed by the inherent logarithmic conversion and mean centering of the data(2,5,6). Subjects express each of these patterns to a variable degree represented by a simple scalar score that can correlate with independent clinical or psychometric descriptors(7,8). Using logistic regression analysis of subject scores (i.e. pattern expression values), linear coefficients can be derived to combine multiple principal components into single disease-related spatial covariance patterns, i.e. composite networks with improved discrimination of patients from healthy control subjects(5,6). Cross-validation within the derivation set can be performed using bootstrap resampling techniques(9). Forward validation is easily confirmed by direct score evaluation of the derived patterns in prospective datasets(10). Once validated, disease-related patterns can be used to score individual patients with respect to a fixed reference sample, often the set of healthy subjects that was used (with the disease group) in the original pattern derivation(11). These standardized values can in turn be used to assist in differential diagnosis(12,13) and to assess disease progression and treatment effects at the network level(7,14-16). We present an example of the application of this methodology to FDG PET data of Parkinson's Disease patients and normal controls using our in-house software to derive a characteristic covariance pattern biomarker of disease.

Functional neuroimaging in Parkinson's disease

The use of functional imaging in neurodegenerative diseases has increased in recent years, with applications in research into the underlying pathophysiology, aiding in diagnosis, or evaluating new treatments. In Parkinson's disease (PD), these imaging methods have expanded our understanding of the disease beyond dopaminergic deficits. Moreover, functional imaging methods have described alterations in functional networks relating not only to the motor symptoms, but also to many nonmotor features of PD, such as cognitive dysfunction. From a clinical viewpoint, functional imaging methods can assist in monitoring disease progression, such as in the context of clinical trials, and holds the potential to aid in early diagnosis of PD and differentiation from other parkinsonian disorders.

Characterization of disease-related covariance topographies with SSMPCA toolbox: effects of spatial normalization and PET scanners

To generate imaging biomarkers from disease-specific brain networks, we have implemented a general toolbox to rapidly perform scaled subprofile modeling (SSM) based on principal component analysis (PCA) on brain images of patients and normals. This SSMPCA toolbox can define spatial covariance patterns whose expression in individual subjects can discriminate patients from controls or predict behavioral measures. The technique may depend on differences in spatial normalization algorithms and brain imaging systems. We have evaluated the reproducibility of characteristic metabolic patterns generated by SSMPCA in patients with Parkinson's disease (PD). We used [(18) F]fluorodeoxyglucose PET scans from patients with PD and normal controls. Motor-related (PDRP) and cognition-related (PDCP) metabolic patterns were derived from images spatially normalized using four versions of SPM software (spm99, spm2, spm5, and spm8). Differences between these patterns and subject scores were compared across multiple independent groups of patients and control subjects. These patterns and subject scores were highly reproducible with different normalization programs in terms of disease discrimination and cognitive correlation. Subject scores were also comparable in patients with PD imaged across multiple PET scanners. Our findings confirm a very high degree of consistency among brain networks and their clinical correlates in PD using images normalized in four different SPM platforms. SSMPCA toolbox can be used reliably for generating disease-specific imaging biomarkers despite the continued evolution of image preprocessing software in the neuroimaging community. Network expressions can be quantified in individual patients independent of different physical characteristics of PET cameras.

Multivariate spatial covariance analysis of 99mTc-exametazime SPECT images in dementia with Lewy bodies and Alzheimer's disease: utility in differential diagnosis

We examined (99m)Tc-exametazime brain blood flow single-photon emission computed tomography (SPECT) images using a spatial covariance analysis (SCA) approach to assess its diagnostic value in distinguishing dementia with Lewy bodies (DLB) from Alzheimer's disease (AD). Voxel SCA was simultaneously applied to a set of preprocessed images (AD, n=40; DLB, n=26), generating a series of eigenimages representing common intercorrelated voxels in AD and DLB. Linear regression derived a spatial covariance pattern (SCP) that discriminated DLB from AD. To investigate the diagnostic value of the model SCP, the SCP was validated by applying it to a second, independent, AD and DLB cohort (AD, n=34; DLB, n=29). Mean SCP expressions differed between AD and DLB (F(1,64)=36.2, P<0.001) with good diagnostic accuracy (receiver operating characteristic (ROC) curve area 0.87, sensitivity 81%, specificity 88%). Forward application of the model SCP to the independent cohort revealed similar differences between groups (F(1,61)=38.4, P<0.001), also with good diagnostic accuracy (ROC 0.86, sensitivity 80%, specificity 80%). Multivariate analysis of blood flow SPECT data appears to be robust and shows good diagnostic accuracy in two independent cohorts for distinguishing DLB from AD.

Metabolic brain networks in translational neurology: concepts and applications

Over the past 2 decades, functional imaging techniques have become commonplace in the study of brain disease. Nevertheless, very few validated analytical methods have been developed specifically to identify and measure systems-level abnormalities in living patients. Network approaches are particularly relevant for translational research in the neurodegenerative disorders, which often involve stereotyped abnormalities in brain organization. In recent years, spatial covariance mapping, a multivariate analytical tool applied mainly to metabolic images acquired in the resting state, has provided a useful means of objectively assessing brain disorders at the network level. By quantifying network activity in individual subjects on a scan-by-scan basis, this technique makes it possible to objectively assess disease progression and the response to treatment on a system-wide basis. To illustrate the utility of network imaging in neurological research, we review recent applications of this approach in the study of Parkinson disease and related movement disorders. Novel uses of the technique are discussed, including the prediction of cognitive responses to dopaminergic therapy, evaluation of the effects of placebo treatment on network activity, assessment of preclinical disease progression, and the use of automated pattern-based algorithms to enhance diagnostic accuracy.

Milestones in neuroimaging

In the last 25 years there have been enormous advances in brain imaging. In addition to utility in diagnosis, these have led to novel insights into the pathogenesis of basal ganglia disease and the role of dopamine and the basal ganglia in normal health. The authors review highlights of this work, with a focus on advances in Parkinson's disease, the dystonias, Huntington's disease, and the role of dopamine in cognition and reward signaling. Emerging areas for future development include studies of functional connectivity, the analysis of default mode networks, studies of novel neurochemical pathways, methods to study disease pathogenesis, and the application of imaging techniques to investigate animal models of disease.

Functional neuroimaging in Parkinson's disease

INTRODUCTION

Functional neuroimaging techniques have greatly contributed to improving our understanding of Parkinson's disease (PD) neurodegeneration and related compensatory mechanisms.

AREAS COVERED

In this paper, the authors analyze the role of functional neuroimaging as a diagnostic tool in PD and review functional neuroimaging studies on PD progression and compensatory adaptations. Through this, the article provides the reader with sensible approaches for the use of functional neuroimaging in the diagnosis of PD. The reader is also provided with knowledge on the time course of nigrostriatal dopamine dysfunction in PD as well as an overview of the potential beneficial and deleterious effects of increased dopamine turnover. Finally, the reader is provided with a critical discussion of the differential effects of levodopa and dopamine agonists on presynaptic dopamine markers and the implications for the interpretation of clinical trials.

EXPERT OPINION

Functional neuroimaging probably plays a limited role in the diagnosis of PD. Parkinson's disease pathology leads to an exponential decline in nigrostriatal dopamine function and a compensatory increase in dopamine turnover, which may help delay symptom onset. On the negative side, increased dopamine turnover contributes to the development of treatment-related motor complications. Presynaptic markers of dopamine function are subject to regulatory changes, compromising the direct interpretation of neuroimaging results in trials of neuroprotective therapies for PD.

Parkinson's disease tremor-related metabolic network: characterization, progression, and treatment effects

The circuit changes that mediate parkinsonian tremor, while likely differing from those underlying akinesia and rigidity, are not precisely known. In this study, to identify a specific metabolic brain network associated with this disease manifestation, we used FDG PET to scan nine tremor dominant Parkinson's disease (PD) patients at baseline and during ventral intermediate (Vim) thalamic nucleus deep brain stimulation (DBS). Ordinal trends canonical variates analysis (OrT/CVA) was performed on the within-subject scan data to detect a significant spatial covariance pattern with consistent changes in subject expression during stimulation-mediated tremor suppression. The metabolic pattern was characterized by covarying increases in the activity of the cerebellum/dentate nucleus and primary motor cortex, and, to a less degree, the caudate/putamen. Vim stimulation resulted in consistent reductions in pattern expression (p<0.005, permutation test). In the absence of stimulation, pattern expression values (subject scores) correlated significantly (r=0.85, p<0.02) with concurrent accelerometric measurements of tremor amplitude. To validate this spatial covariance pattern as an objective network biomarker of PD tremor, we prospectively quantified its expression on an individual subject basis in independent PD populations. The resulting subject scores for this PD tremor-related pattern (PDTP) were found to exhibit: (1) excellent test-retest reproducibility (p<0.0001); (2) significant correlation with independent clinical ratings of tremor (r=0.54, p<0.001) but not akinesia-rigidity; and (3) significant elevations (p<0.02) in tremor dominant relative to atremulous PD patients. Following validation, we assessed the natural history of PDTP expression in early stage patients scanned longitudinally with FDG PET over a 4-year interval. Significant increases in PDTP expression (p<0.01) were evident in this cohort over time; rate of progression, however, was slower than for the PD-related akinesia/rigidity pattern (PDRP). We also determined whether PDTP expression is modulated by interventions specifically directed at parkinsonian tremor. While Vim DBS was associated with changes in PDTP (p<0.001) but not PDRP expression, subthalamic nucleus (STN) DBS reduced the activity of both networks (p<0.05). PDTP expression was suppressed more by Vim than by STN stimulation (p<0.05). These findings suggest that parkinsonian tremor is mediated by a distinct metabolic network involving primarily cerebello-thalamo-cortical pathways. Indeed, effective treatment of this symptom is associated with significant reduction in PDTP expression. Quantification of treatment-mediated changes in both PDTP and PDRP scores can provide an objective means of evaluating the differential effects of novel antiparkinsonian interventions on the different motor features of the disorder.

Scaled subprofile modeling of resting state imaging data in Parkinson's disease: methodological issues

Consistent functional brain abnormalities in Parkinson's disease (PD) are difficult to pinpoint because differences from the normal state are often subtle. In this regard, the application of multivariate methods of analysis has been successful but not devoid of misinterpretation and controversy. The Scaled Subprofile Model (SSM), a principal components analysis (PCA)-based spatial covariance method, has yielded critical information regarding the characteristic abnormalities of functional brain organization that underlie PD and other neurodegenerative disorders. However, the relevance of disease-related spatial covariance patterns (metabolic brain networks) and the most effective methods for their derivation has been a subject of debate. We address these issues here and discuss the inherent advantages of proper application as well as the effects of the misapplication of this methodology. We show that ratio pre-normalization using the mean global metabolic rate (GMR) or regional values from a "reference" brain region (e.g. cerebellum) that may be required in univariate analytical approaches is obviated in SSM. We discuss deviations of the methodology that may yield erroneous or confounding factors.

Cortical hypometabolism and hypoperfusion in Parkinson's disease is extensive: probably even at early disease stages

Recent cerebral blood flow (CBF) and glucose consumption (CMRglc) studies of Parkinson's disease (PD) revealed conflicting results. Using simulated data, we previously demonstrated that the often-reported subcortical hypermetabolism in PD could be explained as an artifact of biased global mean (GM) normalization, and that low-magnitude, extensive cortical hypometabolism is best detected by alternative data-driven normalization methods. Thus, we hypothesized that PD is characterized by extensive cortical hypometabolism but no concurrent widespread subcortical hypermetabolism and tested it on three independent samples of PD patients. We compared SPECT CBF images of 32 early-stage and 33 late-stage PD patients with that of 60 matched controls. We also compared PET FDG images from 23 late-stage PD patients with that of 13 controls. Three different normalization methods were compared: (1) GM normalization, (2) cerebellum normalization, (3) reference cluster normalization (Yakushev et al.). We employed standard voxel-based statistics (fMRIstat) and principal component analysis (SSM). Additionally, we performed a meta-analysis of all quantitative CBF and CMRglc studies in the literature to investigate whether the global mean (GM) values in PD are decreased. Voxel-based analysis with GM normalization and the SSM method performed similarly, i.e., both detected decreases in small cortical clusters and concomitant increases in extensive subcortical regions. Cerebellum normalization revealed more widespread cortical decreases but no subcortical increase. In all comparisons, the Yakushev method detected nearly identical patterns of very extensive cortical hypometabolism. Lastly, the meta-analyses demonstrated that global CBF and CMRglc values are decreased in PD. Based on the results, we conclude that PD most likely has widespread cortical hypometabolism, even at early disease stages. In contrast, extensive subcortical hypermetabolism is probably not a feature of PD.

Covariance 99mTc-exametazime SPECT patterns in Alzheimer's disease and dementia with Lewy bodies: utility in differential diagnosis

(99m)Tc-exametazime single photon emission computed tomography (SPECT) scans of 36 patients with Alzheimer's disease (AD) and 30 with dementia with Lewy bodies (DLB) underwent region of interest (ROI) and principal component analysis (PCA). Principal component analysis was performed on the entire ROI data set. Principal components (PCs) were obtained, representing common intercorrelated regions in AD and DLB. Topographic expression that signified the extent to which a participant expressed the topographic covariance pattern was derived and used as a discriminatory variable. Principal components were identified, accounting for 77% of total data variance. Significant (PC x group) interaction was observed (P < .001). Topographic expression was significantly higher in DLB than AD (F(1,64) = 21.6, P < .001), and differentiated DLB from AD with sensitivity 73% specificity 72%. Calculating the topographic expression in an independent data set of 48 patients with AD and 23 with DLB gave sensitivity = 70%, specificity = 67%. Principal component analysis captures additional sources of variance and if perfusion SPECT is the only scan available, this procedure may offer extra information.

Parkinson's disease spatial covariance pattern: noninvasive quantification with perfusion MRI

Parkinson's disease (PD) is associated with elevated expression of a specific disease-related spatial covariance pattern (PDRP) in radiotracer scans of cerebral blood flow and metabolism. In this study, we scanned nine early-stage patients with PD and nine healthy controls using continuous arterial spin labeling (CASL) perfusion magnetic resonance imaging (pMRI). Parkinson's disease-related metabolic pattern expression in CASL pMRI scans was compared with the corresponding (18)F-fluorodeoxyglucose positron emission tomography values. The PDRP expression was abnormally elevated (P<0.01) in patients scanned with either modality, and the two values were highly intercorrelated (P<0.0001). Perfusion MRI methods can be used for accurate quantification of disease-related covariance patterns.

Metabolic networks for assessment of therapy and diagnosis in Parkinson's disease

Neuroimaging and modern computational techniques like spatial covariance analysis have contributed greatly to the understanding of neural system abnormalities in neurodegenerative disorders such as Parkinson's disease (PD). The application of network analysis to metabolic PET data obtained from patients with PD has led to the identification and validation of two distinct spatial covariance patterns associated with the motor and cognitive manifestations of the disease. Quantifying the activity of these patterns in individual subjects has provided an objective tool for the assessment of treatment efficacy and differential diagnosis. We have found that activity of the PD motor-related network is modulated by antiparkinsonian treatments such as dopaminergic therapy, deep brain stimulation (DBS), and subthalamic nucleus (STN) gene therapy. By contrast, the cognitive-related network is not altered by these interventions for PD motor symptoms. This pattern may however change in response to therapies targeting the cognitive symptoms of this disorder. Recent work has focused on the identification of specific network biomarkers for atypical parkinsonian conditions such as multiple system atrophy (MSA) and progressive supranuclear palsy (PSP). These disease-related patterns can potentially be used in an automated imaging-based algorithm to classify patients with these disorders.

Imaging the nigrostriatal system to monitor disease progression and treatment-induced complications

Radiotracer imaging (RTI) techniques such as positron emission tomography (PET) allow the in vivo assessment of nigrostriatal DA function in Parkinson's disease and have provided valuable insights into the mechanisms of nigrostriatal degeneration and the consequent compensatory changes. Moreover, functional imaging serves as an excellent tool in the assessment of the progression of PD and the evolution of treatment-related complications. However, various studies have shown discordance between clinical progression of PD and nigrostriatal degeneration estimated by PET or SPECT, and no RTI technique can be reliably used as a biomarker for progression of PD. Presynaptic dopaminergic imaging has consistently demonstrated an anterior-posterior gradient of dopaminergic dysfunction predominantly affecting the putamen, with side-to-side asymmetry in tracer binding. Dopaminergic hypofunction in the striatum follows a negative exponential pattern with the fastest rate of decline in early disease. Evaluation of central pharmacokinetics of levodopa action by PET has demonstrated the role of increased synaptic dopamine turnover and downregulation of the dopamine transporter in the pathophysiology of levodopa-induced dyskinesias. In PD with behavioral complications such as impulse control disorders, increased levels of dopamine release have been observed in the ventral striatum during performance of a positive reward task, as well as loss of deactivation in orbitofrontal cortex in response to negative reward prediction errors. This suggests that there is a pathologically heightened "reward" response in the ventral striatum together with loss of the capacity to respond to negative outcomes. Overall, functional imaging with PET is an excellent tool for understanding the disease and its complications; however, caution must be applied in interpretation of the results.

FDG PET in the Evaluation of Parkinson's Disease

Network analysis of (18)F-fluorodeoxyglucose (FDG) positron emission tomography (PET) is an innovative approach for the study of in movement disorders, such as Parkinson's disease (PD). Spatial covariance analysis of imaging data acquired from PD patients has revealed characteristic regional patterns associated with the motor and cognitive features of disease. Quantification of pattern expression in individual patients can be used for diagnosis, assessment of disease severity, and evaluation of novel medical and surgical therapies. Identification of disease-specific patterns in other parkinsonian syndromes, such as multiple system atrophy and progressive supranuclear palsy, has improved diagnostic accuracy in patients with difficult to diagnose parkinsonism. Further developments of these techniques are likely to enhance the role of functional imaging in investigating underlying abnormalities and potential new therapies in these neurodegenerative diseases.

Metabolic brain networks in neurodegenerative disorders: a functional imaging approach

Network analysis of functional brain imaging data is an innovative approach to study circuit abnormalities in neurodegenerative diseases. In Parkinson's disease, spatial covariance analysis of resting-state metabolic images has identified specific regional patterns associated with motor and cognitive symptoms. With functional imaging, these metabolic networks have recently been used to measure system-related progression and to evaluate novel treatment strategies. Network analysis is also being used to characterize specific functional biomarkers for Huntington's disease and Alzheimer's disease. These networks have been particularly helpful in uncovering compensatory mechanisms in genetically at-risk individuals. Ongoing developments in network applications are likely to enhance the role of functional imaging in the investigation of neurodegenerative disorders.