Adaptive and pathological connectivity responses in Parkinson's disease brain networks.

Functional imaging has been used extensively to identify and validate disease-specific networks as biomarkers in neurodegenerative disorders. It is not known, however, whether the connectivity patterns in these networks differ with disease progression compared to the beneficial adaptations that may also occur over time. To distinguish the 2 responses, we focused on assortativity, the tendency for network connections to link nodes with similar properties. High assortativity is associated with unstable, inefficient flow through the network. Low assortativity, by contrast, involves more diverse connections that are also more robust and efficient. We found that in Parkinson's disease (PD), network assortativity increased over time. Assoratitivty was high in clinically aggressive genetic variants but was low for genes associated with slow progression. Dopaminergic treatment increased assortativity despite improving motor symptoms, but subthalamic gene therapy, which remodels PD networks, reduced this measure compared to sham surgery. Stereotyped changes in connectivity patterns underlie disease progression and treatment responses in PD networks.

Stereotyped Relationship Between Motor and Cognitive Metabolic Networks in Parkinson's Disease.

BACKGROUND: Idiopathic Parkinson's disease (iPD) is associated with two distinct brain networks, PD-related pattern (PDRP) and PD-related cognitive pattern (PDCP), which correlate respectively with motor and cognitive symptoms. The relationship between the two networks in individual patients is unclear. OBJECTIVE: To determine whether a consistent relationship exists between these networks, we measured the difference between PDRP and PDCP expression, termed delta, on an individual basis in independent populations of patients with iPD (n = 356), patients with idiopathic REM sleep behavioral disorder (iRBD) (n = 21), patients with genotypic PD (gPD) carrying GBA1 variants (n = 12) or the LRRK2-G2019S mutation (n = 14), patients with atypical parkinsonian syndromes (n = 238), and healthy control subjects (n = 95) from the United States, Slovenia, India, and South Korea. METHODS: We used [18 F]-fluorodeoxyglucose positron emission tomography and resting-state fMRI to quantify delta and to compare the measure across samples; changes in delta over time were likewise assessed in longitudinal patient samples. Lastly, we evaluated delta in prodromal individuals with iRBD and subjects with gPD. RESULTS: Delta was abnormally elevated in each of the four iPD samples (P < 0.05), as well as in the at-risk iRBD group (P < 0.05), with increasing values over time (P < 0.001). PDRP predominance was also present in gPD, with higher values in patients with GBA1 variants compared with the less aggressive LRRK2-G2019S mutation (P = 0.005). This trend was not observed in patients with atypical parkinsonian syndromes, who were accurately discriminated from iPD based on PDRP expression and delta (area under the curve = 0.85; P < 0.0001). CONCLUSIONS: PDRP predominance, quantified by delta, assays the spread of dysfunction from motor to cognitive networks in patients with PD. Delta may therefore aid in differential diagnosis and in tracking disease progression in individual patients. © 2022 International Parkinson and Movement Disorder Society.

Neuropathological correlation supports automated image-based differential diagnosis in parkinsonism.

PURPOSE: Up to 25% of patients diagnosed as idiopathic Parkinson's disease (IPD) have an atypical parkinsonian syndrome (APS). We had previously validated an automated image-based algorithm to discriminate between IPD, multiple system atrophy (MSA), and progressive supranuclear palsy (PSP). While the algorithm was accurate with respect to the final clinical diagnosis after long-term expert follow-up, its relationship to the initial referral diagnosis and to the neuropathological gold standard is not known. METHODS: Patients with an uncertain diagnosis of parkinsonism were referred for 18F-fluorodeoxyglucose (FDG) PET to classify patients as IPD or as APS based on the automated algorithm. Patients were followed by a movement disorder specialist and subsequently underwent neuropathological examination. The image-based classification was compared to the neuropathological diagnosis in 15 patients with parkinsonism. RESULTS: At the time of referral to PET, the clinical impression was only 66.7% accurate. The algorithm correctly identified 80% of the cases as IPD or APS (p = 0.02) and 87.5% of the APS cases as MSA or PSP (p = 0.03). The final clinical diagnosis was 93.3% accurate (p < 0.001), but needed several years of expert follow-up. CONCLUSION: The image-based classifications agreed well with autopsy and can help to improve diagnostic accuracy during the period of clinical uncertainty.

LRRK2 and GBA Variants Exert Distinct Influences on Parkinson's Disease-Specific Metabolic Networks.

The natural history of idiopathic Parkinson's disease (PD) varies considerably across patients. While PD is generally sporadic, there are known genetic influences: the two most common, mutations in the LRRK2 or GBA1 gene, are associated with slower and more aggressive progression, respectively. Here, we applied graph theory to metabolic brain imaging to understand the effects of genotype on the organization of previously established PD-specific networks. We found that closely matched PD patient groups with the LRRK2-G2019S mutation (PD-LRRK2) or GBA1 variants (PD-GBA) expressed the same disease networks as sporadic disease (sPD), but PD-LRRK2 and PD-GBA patients exhibited abnormal increases in network connectivity that were not present in sPD. Using a community detection strategy, we found that the location and modular distribution of these connections differed strikingly across genotypes. In PD-LRRK2, connections were gained within the network core, with the formation of distinct functional pathways linking the cerebellum and putamen. In PD-GBA, by contrast, the majority of functional connections were formed outside the core, involving corticocortical pathways at the network periphery. Strategically localized connections within the core in PD-LRRK2 may maintain PD network activity at lower levels than in PD-GBA, resulting in a less aggressive clinical course.

Metabolic resting-state brain networks in health and disease.

The delineation of resting state networks (RSNs) in the human brain relies on the analysis of temporal fluctuations in functional MRI signal, representing a small fraction of total neuronal activity. Here, we used metabolic PET, which maps nonfluctuating signals related to total activity, to identify and validate reproducible RSN topographies in healthy and disease populations. In healthy subjects, the dominant (first component) metabolic RSN was topographically similar to the default mode network (DMN). In contrast, in Parkinson's disease (PD), this RSN was subordinated to an independent disease-related pattern. Network functionality was assessed by quantifying metabolic RSN expression in cerebral blood flow PET scans acquired at rest and during task performance. Consistent task-related deactivation of the "DMN-like" dominant metabolic RSN was observed in healthy subjects and early PD patients; in contrast, the subordinate RSNs were activated during task performance. Network deactivation was reduced in advanced PD; this abnormality was partially corrected by dopaminergic therapy. Time-course comparisons of DMN loss in longitudinal resting metabolic scans from PD and Alzheimer's disease subjects illustrated that significant reductions appeared later for PD, in parallel with the development of cognitive dysfunction. In contrast, in Alzheimer's disease significant reductions in network expression were already present at diagnosis, progressing over time. Metabolic imaging can directly provide useful information regarding the resting organization of the brain in health and disease.

Abnormal metabolic brain network associated with Parkinson's disease: replication on a new European sample.

PURPOSE: The purpose of this study was to identify the specific metabolic brain pattern characteristic for Parkinson's disease (PD): Parkinson's disease-related pattern (PDRP), using network analysis of [18F]-fluorodeoxyglucose positron emission tomography (FDG-PET) brain images in a cohort of Slovenian PD patients. METHODS: Twenty PD patients (age 70.1 ± 7.8 years, Movement Disorder Society Unified Parkinson's Disease Motor Rating Scale (MDS-UPDRS-III) 38.3 ± 12.2; disease duration 4.3 ± 4.1 years) and 20 age-matched normal controls (NCs) underwent FDG-PET brain imaging. An automatic voxel-based scaled subprofile model/principal component analysis (SSM/PCA) was applied to these scans for PDRP-Slovenia identification. RESULTS: The pattern was characterized by relative hypermetabolism in pallidum, putamen, thalamus, brain stem, and cerebellum associated with hypometabolism in sensorimotor cortex, posterior parietal, occipital, and frontal cortices. The expression of PDRP-Slovenia discriminated PD patients from NCs (p < 0.0001) and correlated positively with patients' clinical score (MDS-UPDRS-III, p = 0.03). Additionally, its topography agrees well with the original PDRP (p < 0.001) identified in American cohort of PD patients. We validated the PDRP-Slovenia expression on additional FDG-PET scans of 20 PD patients, 20 NCs, and 25 patients with atypical parkinsonism (AP). We confirmed that the expression of PDRP-Slovenia manifests good diagnostic accuracy with specificity and sensitivity of 85-90% at optimal pattern expression cutoff for discrimination of PD patients and NCs and is not expressed in AP. CONCLUSION: PDRP-Slovenia proves to be a robust and reproducible functional imaging biomarker independent of patient population. It accurately differentiates PD patients from NCs and AP and correlates well with the clinical measure of PD progression.

Parkinson's disease: increased motor network activity in the absence of movement.

We used a network approach to assess systems-level abnormalities in motor activation in humans with Parkinson's disease (PD). This was done by measuring the expression of the normal movement-related activation pattern (NMRP), a previously validated activation network deployed by healthy subjects during motor performance. In this study, NMRP expression was prospectively quantified in (15)O-water PET scans from a PD patient cohort comprised of a longitudinal early-stage group (n = 12) scanned at baseline and at two or three follow-up visits two years apart, and a moderately advanced group scanned on and off treatment with either subthalamic nucleus deep brain stimulation (n = 14) or intravenous levodopa infusion (n = 14). For each subject and condition, we measured NMRP expression during both movement and rest. Resting expression of the abnormal PD-related metabolic covariance pattern was likewise determined in the same subjects. NMRP expression was abnormally elevated (p < 0.001) in PD patients scanned in the nonmovement rest state. By contrast, network activity measured during movement did not differ from normal (p = 0.34). In the longitudinal cohort, abnormal increases in resting NMRP expression were evident at the earliest clinical stages (p < 0.05), which progressed significantly over time (p = 0.003). Analogous network changes were present at baseline in the treatment cohort (p = 0.001). These abnormalities improved with subthalamic nucleus stimulation (p < 0.005) but not levodopa (p = 0.25). In both cohorts, the changes in NMRP expression that were observed did not correlate with concurrent PD-related metabolic covariance pattern measurements (p > 0.22). Thus, the resting state in PD is characterized by changes in the activity of normal as well as pathological brain networks.

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.

Longitudinal Network Changes and Phenoconversion Risk in Isolated REM Sleep Behavior Disorder.

Isolated rapid eye movement sleep behavior disorder (iRBD) is a prodromal syndrome for Parkinson's disease (PD) and related α-synucleinopathies. We conducted a longitudinal imaging study of network changes in iRBD and their relationship to phenoconversion. Expression levels for the PD-related motor and cognitive networks (PDRP and PDCP) were measured at baseline, 2 and 4 years, along with dopamine transporter (DAT) binding. PDRP and PDCP expression increased over time, with higher values in the former network. While abnormal functional connections were identified initially within the PDRP, others bridging the two networks appeared later. A model based on the rates of PDRP progression and putamen dopamine loss predicted phenoconversion within 1.2 years in individuals with iRBD. In aggregate, the data suggest that maladaptive reorganization of brain networks takes place in iRBD years before phenoconversion. Network expression and DAT binding measures can be used together to assess phenoconversion risk in these individuals.

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

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

Improved sequence learning with subthalamic nucleus deep brain stimulation: evidence for treatment-specific network modulation.

We used a network approach to study the effects of anti-parkinsonian treatment on motor sequence learning in humans. Eight Parkinson's disease (PD) patients with bilateral subthalamic nucleus (STN) deep brain stimulation underwent H(2)(15)O positron emission tomography (PET) imaging to measure regional cerebral blood flow (rCBF) while they performed kinematically matched sequence learning and movement tasks at baseline and during stimulation. Network analysis revealed a significant learning-related spatial covariance pattern characterized by consistent increases in subject expression during stimulation (p = 0.008, permutation test). The network was associated with increased activity in the lateral cerebellum, dorsal premotor cortex, and parahippocampal gyrus, with covarying reductions in the supplementary motor area (SMA) and orbitofrontal cortex. Stimulation-mediated increases in network activity correlated with concurrent improvement in learning performance (p < 0.02). To determine whether similar changes occurred during dopaminergic pharmacotherapy, we studied the subjects during an intravenous levodopa infusion titrated to achieve a motor response equivalent to stimulation. Despite consistent improvement in motor ratings during infusion, levodopa did not alter learning performance or network activity. Analysis of learning-related rCBF in network regions revealed improvement in baseline abnormalities with STN stimulation but not levodopa. These effects were most pronounced in the SMA. In this region, a consistent rCBF response to stimulation was observed across subjects and trials (p = 0.01), although the levodopa response was not significant. These findings link the cognitive treatment response in PD to changes in the activity of a specific cerebello-premotor cortical network. Selective modulation of overactive SMA-STN projection pathways may underlie the improvement in learning found with stimulation.

Parkinson's disease cognitive network correlates with caudate dopamine.

Prior evidence has suggested a link between caudate dopaminergic functioning and cognition in Parkinson's disease (PD). In this dual tracer study we analyzed the relationship between nigrostriatal dopaminergic dysfunction and the expression of the previously validated PD cognition-related metabolic pattern (PDCP). In this study, 17 non-demented PD patients underwent positron emission tomography (PET) imaging with [(18)F]-fluorodeoxyglucose to measure PDCP expression, and [(18)F]-fluoropropyl-ß-CIT (FPCIT) to measure dopamine transporter (DAT) binding. Automated voxel-by-voxel searches of the FPCIT PET volumes were performed to identify regions in which DAT binding significantly correlated with PDCP expression values. The findings were validated using prespecified anatomical regions-of-interest (ROIs). Voxel-wise interrogation of the FPCIT PET scans revealed a single significant cluster in which DAT binding correlated with PDCP expression (p<0.05, corrected). This cluster was localized to the left caudate nucleus; an analogous correlation (r=-0.63, p<0.01) was also present in the "mirror" region of the right hemisphere. These findings were confirmed by the presence of a significant correlation (r=-0.67, p<0.005) between PDCP expression and DAT binding in caudate ROIs, which survived adjustment for age, disease duration, and clinical severity ratings. Correlation between caudate DAT binding and subject expression of the PD motor-related metabolic pattern was not significant (p>0.21). In summary, this study demonstrates a significant relationship between loss of dopaminergic input to the caudate nucleus and the expression of a cognition-related disease network in unmedicated PD patients. These baseline measures likely function in concert to determine the cognitive effects of dopaminergic therapy in PD.

Longitudinal studies of cerebral glucose metabolism in late-life depression and normal aging.

OBJECTIVE: Late-life depression (LLD) has a substantial public health impact and is both a risk factor for and a prodrome of dementia. Positron emission tomography (PET) studies of cerebral glucose metabolism have demonstrated sensitivity in evaluating neural circuitry involved in depression, aging, incipient cognitive decline, and dementia. The present study evaluated the long term effects of a course of antidepressant treatment on glucose metabolism in LLD patients. METHODS: Nine LLD patients and seven non-depressed control subjects underwent clinical and cognitive evaluations as well as brain magnetic resonance imaging and PET studies of cerebral glucose metabolism at baseline, after 8 weeks of treatment with citalopram for a major depressive episode (patients only), and at an approximately 2-year follow-up. RESULTS: The majority of LLD patients were remitted at follow-up (7/9). Neither patients nor controls showed significant cognitive decline. The patients showed greater increases in glucose metabolism than the controls in regions associated with mood symptoms (anterior cingulate and insula). Both groups showed decreases in metabolism in posterior association cortices implicated in dementia. CONCLUSIONS: Longitudinal changes in cerebral glucose metabolism are observed in controls and in LLD patients without significant cognitive decline that are more extensive than the decreases in brain volume. Longer duration follow-up studies and the integration of other molecular imaging methods will have implications for understanding the clinical and neurobiological significance of these metabolic changes.

Differential diagnosis of parkinsonian syndromes using F-18 fluorodeoxyglucose positron emission tomography.

INTRODUCTION: Positron emission tomography (PET) imaging with F-18 fluorodeoxyglucose (FDG) has been used to identify characteristic patterns of regional glucose metabolism in patients with idiopathic Parkinson's disease (IPD) and the atypical parkinsonian syndromes of progressive supranuclear palsy (PSP), multiple system atrophy (MSA), and corticobasal syndrome (CBS). We undertook this study to assess the utility of fluorodeoxyglucose-PET in the differential diagnosis of individual patients with clinical parkinsonism. "Visual" and "computer-supported" reading of the fluorodeoxyglucose-PET scans were used for image interpretation and compared with each other. METHODS: One hundred thirty-six parkinsonian patients were referred from movement disorder clinics in specialty neurology centers for the fluorodeoxyglucose-PET study. Imaging-based diagnosis was obtained by visual assessment of individual scans by a PET physician blinded to the clinical diagnosis and also by computer-assisted interpretation using statistical parametric mapping (SPM) analysis. The results were compared with a 2-year follow-up clinical assessment made by a movement disorder specialist. RESULTS: Concordance of visual evaluation of fluorodeoxyglucose-PET with clinical diagnosis was achieved in 91.7% of patients scanned, 97.6% IPD, 80% MSA, 76.6% PSP, and 100% CBS. Blinded computer assessment using SPM was concordant with the clinical diagnosis in 91% of cases evaluated (90.4% IPD, 80% MSA, 93.3% PSP, and 100% CBS). CONCLUSIONS: Fluorodeoxyglucose-PET performed at the time of initial referral for parkinsonism is useful for the differential diagnosis of IPD, PSP, MSA, and CBS. Computer-assisted methods can be used for objective evaluation especially when expert readers are not available.

Effect of the identification group size and image resolution on the diagnostic performance of metabolic Alzheimer's disease-related pattern.

BACKGROUND: Alzheimer's disease-related pattern (ADRP) is a metabolic brain biomarker of Alzheimer's disease (AD). While ADRP is being introduced into research, the effect of the size of the identification cohort and the effect of the resolution of identification and validation images on ADRP's performance need to be clarified. METHODS: 240 2-[18F]fluoro-2-deoxy-D-glucose positron emission tomography images [120 AD/120 cognitive normals (CN)] were selected from the Alzheimer's disease neuroimaging initiative database. A total of 200 images (100 AD/100 CN) were used to identify different versions of ADRP using a scaled subprofile model/principal component analysis. For this purpose, five identification groups were randomly selected 25 times. The identification groups differed in the number of images (20 AD/20 CN, 30 AD/30 CN, 40 AD/40 CN, 60 AD/60 CN, and 80 AD/80 CN) and image resolutions (6, 8, 10, 12, 15 and 20 mm). A total of 750 ADRPs were identified and validated through the area under the curve (AUC) values on the remaining 20 AD/20 CN with six different image resolutions. RESULTS: ADRP's performance for the differentiation between AD patients and CN demonstrated only a marginal average AUC increase, when the number of subjects in the identification group increases (AUC increase for about 0.03 from 20 AD/20 CN to 80 AD/80 CN). However, the average of the lowest five AUC values increased with the increasing number of participants (AUC increase for about 0.07 from 20 AD/20 CN to 30 AD/30 CN and for an additional 0.02 from 30 AD/30 CN to 40 AD/40 CN). The resolution of the identification images affects ADRP's diagnostic performance only marginally in the range from 8 to 15 mm. ADRP's performance stayed optimal even when applied to validation images of resolution differing from the identification images. CONCLUSIONS: While small (20 AD/20 CN images) identification cohorts may be adequate in a favorable selection of cases, larger cohorts (at least 30 AD/30 CN images) shall be preferred to overcome possible/random biological differences and improve ADRP's diagnostic performance. ADRP's performance stays stable even when applied to the validation images with a resolution different than the resolution of the identification ones.

Abnormal network topographies and changes in global activity: absence of a causal relationship.

Changes in regional brain activity can be observed following global normalization procedures to reduce variability in the data. In particular, spurious regional differences may appear when scans from patients with low global activity are compared to those from healthy subjects. It has thus been suggested that the consistent increases in subcortical activity that characterize the abnormal Parkinson's disease-related metabolic covariance pattern (PDRP) are artifacts of global normalization, and that similar topographies can be identified in scans from healthy subjects with varying global activity. To address this issue, we examined the effects of experimental reductions in global metabolic activity on PDRP expression. Ten healthy subjects underwent ¹8F-fluorodeoxyglucose PET in wakefulness and following sleep induction. In all subjects, the global metabolic rate (GMR) declined with sleep (mean -34%, range: -17 to -56%), exceeding the test-retest differences of the measure (p<0.001). By contrast, sleep-wake differences in PDRP expression did not differ from test-retest differences, and did not correlate (R²=0.04) with concurrent declines in global metabolic activity. Indeed, despite significant GMR reductions in sleep, PDRP values remained within the normal range. Likewise, voxel weights on the principal component patterns resulting from combined analysis of the sleep and wake scans did not correlate (R²<0.07) with the corresponding regional loadings on the PDRP topography. In aggregate, the data demonstrate that abnormal PDRP expression is not induced by reductions in global activity. Moreover, significant declines in GMR are not associated with the appearance of PDRP-like spatial topographies.

Serotonin modulation of cerebral glucose metabolism: sex and age effects.

The serotonin system is implicated in a variety of psychiatric disorders whose clinical presentation and response to treatment differ between males and females, as well as with aging. However, human neurobiological studies are limited. Sex differences in the cerebral metabolic response to an increase in serotonin concentrations were measured, as well as the effect of aging, in men compared to women. Thirty-three normal healthy individuals (14 men/19 women, age range 20-79 years) underwent two resting positron emission tomography studies with the radiotracer [18F]-2-deoxy-2-fluoro-D-glucose ([(18)F]-FDG) after placebo and selective serotonin reuptake inhibitor (SSRI, citalopram) infusions on two separate days. Results indicated that women demonstrated widespread areas of increased cortical glucose metabolism with fewer areas of decrease in metabolism in response to citalopram. Men, in contrast, demonstrated several regions of decreased cortical metabolism, but no regions of increased metabolism. Age was associated with greater increases in women and greater decreases in men in most brain regions. These results support prior studies indicating that serotonin function differs in men and women across the lifespan. Future studies aimed at characterizing the influences of age and sex on the serotonin system in patients with psychiatric disorders are needed to elucidate the relationship between sex and age differences in brain chemistry and associated differences in symptom presentation and treatment response.

Impaired sequence learning in dystonia mutation carriers: a genotypic effect.

Abnormalities in motor sequence learning have been observed in non-manifesting carriers of the DYT1 dystonia mutation. Indeed, motor sequence learning deficits in these subjects have been associated with increased cerebellar activation during task performance. In the current study, we determined whether similar changes are also present in clinically manifesting DYT1 carriers as well as in carriers of other primary dystonia mutations such as DYT6. Additionally, we determined whether sequence learning performance and associated brain activation in these subjects correlate with previously described genotype-related abnormalities of cerebellar pathway integrity and striatal D2 dopamine receptor binding. Nineteen DYT1 carriers (10 non-manifesting DYT1: 51.5±15.1 years; nine manifesting DYT1: 46.1±15.1 years) and 12 healthy control subjects (42.8±15.3 years) were scanned with H2(15)O positron emission tomography while performing controlled sequence learning and reference tasks. Eleven DYT6 carriers (four non-manifesting DYT6: 38.0±22.1; seven manifesting DYT6: 35.3±14.2 years) were evaluated during task performance without concurrent imaging. DYT1 and DYT6 carriers also underwent diffusion tensor magnetic resonance imaging for the assessment of tract integrity and 11C-raclopride positron emission tomography to measure caudate/putamen D2 receptor binding. These imaging measures were correlated with sequence learning performance and associated activation responses. Sequence learning deficits of similar magnitude were observed in manifesting and non-manifesting DYT1 carriers. In contrast, learning deficits were not detected in DYT6 carriers, irrespective of clinical penetrance. Affected DYT1 carriers exhibited significant increases in sequence learning-related activation in the left lateral cerebellar cortex and in the right premotor and inferior parietal regions. Increases in premotor cortical activation observed in the mutation carriers correlated with reductions in cerebellar pathway integrity measured using magnetic resonance diffusion tensor imaging and probabilistic tractography. Additionally, the cerebellar tract changes correlated with reductions in dentate nucleus activation recorded during task performance. Sequence learning performance and task-related activation responses did not correlate with striatal D2 receptor binding. In summary, we found that sequence learning deficits and concomitant increases in cerebellar activation are specific features of the DYT1 genotype. The close relationship between reduced cerebellar pathway integrity and increased learning-related activation of the premotor cortex is compatible with the view of DYT1 dystonia as a neurodevelopmental circuit disorder.

Prospective F-18 FDOPA PET Imaging Study in Human PD.

Purpose: We present the findings of our final prospective study submitted to the U.S. Food and Drug Administration (FDA) for New Drug Application (NDA) approval for the use of 3,4-dihydroxy-6-[18F]fluoro-l-phenylalanine (F-18 FDOPA) positron emission tomography (PET) imaging for Parkinson's disease (PD). The primary aim was to determine the sensitivity, specificity, and predictive values of F-18 FDOPA PET in parkinsonian patients with respect to clinical standard-of-truth (SOT). Secondary outcomes included the inter-rater reliability, and correlation of quantitative measures for PET with dopaminergic status. Methods: In 68 parkinsonian subjects, F-18 FDOPA PET scan from 80 to 100 min was acquired following a CT scan. Scan images were presented to one expert in F-18 FDOPA image interpretation and two physicians with prior experience in I-123 FPCIT single-photon emission computed tomography image interpretation. Fifty-six subjects completed the study with a follow-up for SOT determination. Image readers were blind to the clinical/quantitative data; SOT clinician was blind to the image data. Results: For 47 of the 56 patients, SOT was in agreement with the PET scan results. For nine patients, SOT suggested dopaminergic deficit, whereas the imaging showed normal uptake. The specificity and positive predictive values are 91% and 92%, respectively, suggesting high probability that those who test positive by the PET scan truly have dopaminergic degeneration. The sensitivity was 73%. Inter-rater agreement was 0.6-0.8 between the different readers. Conclusion: Our prospective study demonstrates high specificity and moderate sensitivity of F-18 FDOPA PET for PD. We received NDA approval in October 2019. Supplementary Information: The online version contains supplementary material available at 10.1007/s13139-022-00748-4.

Abnormalities in metabolic network activity precede the onset of motor symptoms in Parkinson's disease.

Imaging studies show that Parkinson's disease (PD) alters the activity of motor- and cognition-related metabolic brain networks. However, it is not known whether the network changes appear at or before symptom onset. In this study, we examined 15 hemiparkinsonian patients who underwent serial metabolic imaging with [(18)F]-fluorodeoxyglucose (FDG) PET at baseline and again 2.1 +/- 0.6 (mean +/- SD) and 3.9 +/- 0.7 years later. We assessed longitudinal changes in network activity in each cerebral hemisphere, focusing specifically on the "presymptomatic" hemisphere--ipsilateral to the initially involved body side. At the network level, the activity of the PD motor-related pattern (PDRP) increased symmetrically in both hemispheres over time (p < 0.001), with significant bilateral elevations at each of the three time points. Hemispheric expression of the PD cognition-related pattern likewise increased symmetrically (p < 0.001), although significant elevations were not evident on either side until 4 years. At the regional level, putamen metabolism contralateral to the initially affected body side was elevated at all three time points, without longitudinal change. In contrast, in the initially presymptomatic hemisphere, putamen metabolic activity increased steadily over time, reaching abnormal levels only at 4 years. Metabolic activity in the contralateral precuneus fell to subnormal levels by the final time point. These findings suggest that abnormal PDRP activity antecedes the appearance of motor signs by approximately 2 years. The timing and laterality of symptom onset relates to focal asymmetric metabolic changes at the putamenal node of this network.