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.

Predictive Factors of Cloxacillin Susceptibility in Primary Bacterial Spinal Infection.

STUDY DESIGN: Prognostic study. OBJECTIVES: The objective of this study is to identify predictive factors for cloxacillin susceptibility in spinal infections. METHODS: A retrospective analysis was conducted using data from January 1, 1997, to December 31, 2021. The study included patients presenting with back pain and either a positive bacterial culture from the spine or radiological evidence of spinal infection (spondylodiscitis and/or epidural abscess) along with positive bacterial blood culture. RESULTS: Among 171 patients (127 males, 44 females), 53.2% had Staphylococcus isolates, with 40.4% showing cloxacillin resistance. Lower globulin levels (<33.5 g/L), recent hospitalization within 90 days, and residence in an old age home predicted gram-positive bacteria with cloxacillin resistance and gram-negative bacteria as causative organisms (P<.05). The 30-day and 1-year all-cause mortality rates were 0% and 8.2%, respectively. Higher red cell distribution width (RDW >16.1%) and Charlson comorbidity index (CCI) scores predicted 1-year all-cause mortality (P<.05). Intensive care unit admission was required for 9.9% of patients. CONCLUSIONS: This study identified predictive factors for spinal infection by gram-positive bacteria with cloxacillin resistance and gram-negative bacteria. Patients with lower globulin levels (<33.5 g/L), recent hospitalization within 90 days, or residency in an old age home upon admission should avoid standalone cloxacillin therapy and consider antibiotics with gram-negative coverage. Higher RDW (>16.1%) and CCI scores were associated with increased 1-year all-cause mortality. These findings contribute to treatment decision-making and improving patient outcomes in spinal infections.

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.

SinoNasal Microbiota Transfer to treat recalcitrant chronic rhinosinusitis: A case series.

KEY POINTS: SinoNasal Microbiota Transfer (SNMT) was safe with immediate benefit in all recipients, with sustained improvement in two of three recipients for up to 180 days. The addition of antimicrobial photodynamic therapy worsened chronic rhinosinusitis. These promising SNMT results warrant further study of safety and efficacy.

HSP90 inhibition suppresses tumor glycolytic flux to potentiate the therapeutic efficacy of radiotherapy for head and neck cancer.

Glycolytic metabolism may account for antitumor immunity failure. Pyruvate kinase M2 (PKM2) and platelet phosphofructokinase (PFKP), two key enzymes involved in the glycolytic pathway, are hyperactivated in head and neck squamous cell carcinoma (HNSCC). Using ganetespib as a drug model for heat shock protein 90 (HSP90) inhibition and combining results from clinical trials and animal treatment, we demonstrated that HSP90 inhibition leads to a blockade of glycolytic flux in HNSCC cells by simultaneously suppressing PKM2 and PFKP at both the transcriptional and posttranslational levels. Down-regulation of tumor glycolysis facilitates tumor infiltration of cytotoxic T cells via suppression of glycolysis-dependent interleukin-8 signaling. The addition of ganetespib to radiation attenuates radiation-induced up-regulation of PKM2 and PFKP and potentiates T cell-mediated antitumor immunity, resulting in a more potent antitumor effect than either treatment alone, providing a molecular basis for exploring the combination of HSP90 inhibitors with radiotherapy to improve outcomes for patients with HNSCC.

A Network Imaging Biomarker of X-Linked Dystonia-Parkinsonism.

OBJECTIVE: The purpose of this study was to characterize a metabolic brain network associated with X-linked dystonia-parkinsonism (XDP). METHODS: Thirty right-handed Filipino men with XDP (age = 44.4 ± 8.5 years) and 30 XDP-causing mutation negative healthy men from the same population (age = 37.4 ± 10.5 years) underwent [18 F]-fluorodeoxyglucose positron emission tomography. Scans were analyzed using spatial covariance mapping to identify a significant XDP-related metabolic pattern (XDPRP). Patients were rated clinically at the time of imaging according to the XDP-Movement Disorder Society of the Philippines (MDSP) scale. RESULTS: We identified a significant XDPRP topography from 15 randomly selected subjects with XDP and 15 control subjects. This pattern was characterized by bilateral metabolic reductions in caudate/putamen, frontal operculum, and cingulate cortex, with relative increases in the bilateral somatosensory cortex and cerebellar vermis. Age-corrected expression of XDPRP was significantly elevated (p < 0.0001) in XDP compared to controls in the derivation set and in the remaining 15 patients (testing set). We validated the XDPRP topography by identifying a similar pattern in the original testing set (r = 0.90, p < 0.0001; voxel-wise correlation between both patterns). Significant correlations between XDPRP expression and clinical ratings for parkinsonism-but not dystonia-were observed in both XDP groups. Further network analysis revealed abnormalities of information transfer through the XDPRP space, with loss of normal connectivity and gain of abnormal functional connections linking network nodes with outside brain regions. INTERPRETATION: XDP is associated with a characteristic metabolic network associated with abnormal functional connectivity among the basal ganglia, thalamus, motor regions, and cerebellum. Clinical signs may relate to faulty information transfer through the network to outside brain regions. ANN NEUROL 2023;94:684-695.

Longitudinal changes in metabolic network activity in early Alzheimer's disease.

INTRODUCTION: The progression of Alzheimer's disease (AD) has been linked to two metabolic networks, the AD-related pattern (ADRP) and the default mode network (DMN). METHODS: Converting and clinically stable cognitively normal subjects (n = 47) and individuals with mild cognitive impairment (n = 96) underwent 2-[18 F]fluoro-2-deoxy-D-glucose (FDG) positron emission tomography (PET) three or more times over 6 years (nscans  = 705). Expression levels for ADRP and DMN were measured in each subject and time point, and the resulting changes were correlated with cognitive performance. The role of network expression in predicting conversion to dementia was also evaluated. RESULTS: Longitudinal increases in ADRP expression were observed in converters, while age-related DMN loss was seen in converters and nonconverters. Cognitive decline correlated with increases in ADRP and declines in DMN, but conversion to dementia was predicted only by baseline ADRP levels. DISCUSSION: The results point to the potential utility of ADRP as an imaging biomarker of AD progression.

Patterns of toxicity burden for FDA-approved immune checkpoint inhibitors in the United States.

BACKGROUND: Immune-related adverse events (irAEs) are a common phenomenon in cancer patients treated with immune checkpoint inhibitors (ICIs). Surprisingly, the toxicity burdens of these irAEs have not been illustrated clearly. In this study, we analyzed irAEs for seven FDA-approved ICIs in cancer treatment to show the pattern of toxicity burden among cancer patients. METHODS: irAEs associated with seven FDA-approved ICIs, including three PD-1 inhibitors (cemiplimab, nivolumab and pembrolizumab), three PD-L1 inhibitors (atezolizumab, avelumab and durvalumab), and one CTLA-4 inhibitor (ipilimumab), were analyzed based on data from 149,303 reported cases (from January 1, 2015 to June 30, 2022) collected from the FDA Adverse Events Reporting System (FAERS) public dashboard. Proportions of serious irAEs and correlations with tumor type, age and sex were assessed via R package and GraphPad software. RESULTS: irAEs related to anti-PD-1 ICIs required less hospital care resources compared with anti-PD-L1 and anti-CTLA-4 ICIs. Patients treated with pembrolizumab had relatively fewer serious cases. Treatment with ICIs led to the highest probability of serious irAEs in patients with lung cancer. 'Respiratory, thoracic and mediastinal disorders' and 'gastrointestinal disorders' were the two most common groups of disorders caused by the seven ICIs studied. 'Cardiac disorders' was the main type of disorders caused by these ICIs in cancer patients aged 65-85, while 'reproductive system and breast disease' was the main type of disorder in cancer patients aged 18-64. 'Respiratory, thoracic, mediastinal diseases' and 'reproductive system and breast diseases' were the main types of disorders associated with treatment with these ICIs in male and female patients, respectively. CONCLUSION: Tissue and organ toxicities of ICIs are age and sex specific. There are risks of respiratory and urinary system toxicity in male patients and reproductive system toxicity in female patients treated with the ICIs studied. Future studies on the toxicity burden of ICIs should incorporate age and sex differences to better understand the relevance of ICI toxicity burden to human immune function to develop appropriate tumor immune and therapeutic intervention strategies.

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.

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

BACKGROUND AND PURPOSE: Although sporadic Creutzfeldt-Jakob disease (sCJD) is a rare cause of dementia, it is critical to understand its functional networks as the prion protein spread throughout the brain may share similar mechanisms with other more common neurodegenerative disorders. In this study, the metabolic brain network associated with sCJD was investigated and its internal network organization was explored. METHODS: We explored 2-[18 F]fluoro-2-deoxy-d-glucose positron emission tomography (FDG-PET) brain scans of 29 sCJD patients, 56 normal controls (NCs) and 46 other dementia patients from two independent centers. sCJD-related pattern (CJDRP) was identified in a cohort of 16 pathologically proven sCJD patients and 16 age-matched NCs using scaled subprofile modeling/principal component analysis and was prospectively validated in an independent cohort of 13 sCJD patients and 20 NCs. The pattern's specificity was tested on other dementia patients and its clinical relevance by clinical correlations. The pattern's internal organization was further studied using graph theory methods. RESULTS: The CJDRP was characterized by relative hypometabolism in the bilateral caudate, thalami, middle and superior frontal gyri, parietal lobe and posterior cingulum in association with relative hypermetabolism in the hippocampi, parahippocampal gyri and cerebellum. The pattern's expression significantly discriminated sCJD from NCs and other dementia patients (p < 0.005; receiver operating characteristic analysis CJD vs. NCs area under the curve [AUC] 0.90-0.96, sCJD vs. Alzheimer's disease AUC 0.78, sCJD vs. behavioral variant of frontotemporal dementia AUC 0.84). The pattern's expression significantly correlated with cognitive, functional decline and disease duration. The metabolic connectivity analysis revealed inefficient information transfer with specific network reorganization. CONCLUSIONS: The CJDRP is a robust metabolic biomarker of sCJD. Due to its excellent clinical correlations it has the potential to monitor disease in emerging disease-modifying trials.

Disease specific and nonspecific metabolic brain networks in behavioral variant of frontotemporal dementia.

Behavioral variant of frontotemporal dementia (bvFTD) is common among young-onset dementia patients. While bvFTD-specific multivariate metabolic brain pattern (bFDRP) has been identified previously, little is known about its temporal evolution, internal structure, effect of atrophy, and its relationship with nonspecific resting-state networks such as default mode network (DMN). In this multicenter study, we explored FDG-PET brain scans of 111 bvFTD, 26 Alzheimer's disease, 16 Creutzfeldt-Jakob's disease, 24 semantic variant primary progressive aphasia (PPA), 18 nonfluent variant PPA and 77 healthy control subjects (HC) from Slovenia, USA, and Germany. bFDRP was identified in a cohort of 20 bvFTD patients and age-matched HC using scaled subprofile model/principle component analysis and validated in three independent cohorts. It was characterized by hypometabolism in frontal cortex, insula, anterior/middle cingulate, caudate, thalamus, and temporal poles. Its expression in bvFTD patients was significantly higher compared to HC and other dementia syndromes (p < .0004), correlated with cognitive decline (p = .0001), and increased over time in longitudinal cohort (p = .0007). Analysis of internal network organization by graph-theory methods revealed prominent network disruption in bvFTD patients. We have further found a specific atrophy-related pattern grossly corresponding to bFDRP; however, its contribution to the metabolic pattern was minimal. Finally, despite the overlap between bFDRP and FDG-PET-derived DMN, we demonstrated a predominant role of the specific bFDRP. Taken together, we validated the bFDRP network as a diagnostic/prognostic biomarker specific for bvFTD, provided a unique insight into its highly reproducible internal structure, and proved that bFDRP is unaffected by structural atrophy and independent of normal resting state networks loss.

The use of metaphors by service users with diverse long-term conditions: a secondary qualitative data analysis.

Long-term conditions and accompanied co-morbidities now affect about a quarter of the UK population. Enabling patients and caregivers to communicate their experience of illness in their own words is vital to developing a shared understanding of the condition and its impact on patients' and caregivers' lives and in delivering person-centred care. Studies of patient language show how metaphors provide insight into the physical and emotional world of the patient, but such studies are often limited by their focus on a single illness. The authors of this study undertook a secondary qualitative data analysis of 25 interviews, comparing the metaphors used by patients and parents of patients with five longterm conditions. Analysis shows how similar metaphors can be used in empowering and disempowering ways as patients strive to accept illness in their daily lives and how metaphor use depends on the manifestation, diagnosis, and treatment of individual conditions. The study concludes with implications for how metaphorical expressions can be attended to by healthcare professionals as part of shared care planning.

Automated differential diagnosis of dementia syndromes using FDG PET and machine learning.

Background: Metabolic brain imaging with 2-[18F]fluoro-2-deoxy-D-glucose positron emission tomography (FDG PET) is a supportive diagnostic and differential diagnostic tool for neurodegenerative dementias. In the clinic, scans are usually visually interpreted. However, computer-aided approaches can improve diagnostic accuracy. We aimed to build two machine learning classifiers, based on two sets of FDG PET-derived features, for differential diagnosis of common dementia syndromes. Methods: We analyzed FDG PET scans from three dementia cohorts [63 dementia due to Alzheimer's disease (AD), 79 dementia with Lewy bodies (DLB) and 23 frontotemporal dementia (FTD)], and 41 normal controls (NCs). Patients' clinical diagnosis at follow-up (25 ± 20 months after scanning) or cerebrospinal fluid biomarkers for Alzheimer's disease was considered a gold standard. FDG PET scans were first visually evaluated. Scans were pre-processed, and two sets of features extracted: (1) the expressions of previously identified metabolic brain patterns, and (2) the mean uptake value in 95 regions of interest (ROIs). Two multi-class support vector machine (SVM) classifiers were tested and their diagnostic performance assessed and compared to visual reading. Class-specific regional feature importance was assessed with Shapley Additive Explanations. Results: Pattern- and ROI-based classifier achieved higher overall accuracy than expert readers (78% and 80% respectively, vs. 71%). Both SVM classifiers performed similarly to one another and to expert readers in AD (F1 = 0.74, 0.78, and 0.78) and DLB (F1 = 0.81, 0.81, and 0.78). SVM classifiers outperformed expert readers in FTD (F1 = 0.87, 0.83, and 0.63), but not in NC (F1 = 0.71, 0.75, and 0.92). Visualization of the SVM model showed bilateral temporal cortices and cerebellum to be the most important features for AD; occipital cortices, hippocampi and parahippocampi, amygdala, and middle temporal lobes for DLB; bilateral frontal cortices, middle and anterior cingulum for FTD; and bilateral angular gyri, pons, and vermis for NC. Conclusion: Multi-class SVM classifiers based on the expression of characteristic metabolic brain patterns or ROI glucose uptake, performed better than experts in the differential diagnosis of common dementias using FDG PET scans. Experts performed better in the recognition of normal scans and a combined approach may yield optimal results in the clinical setting.

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.

Identification and validation of Alzheimer's disease-related metabolic brain pattern in biomarker confirmed Alzheimer's dementia patients.

Metabolic brain biomarkers have been incorporated in various diagnostic guidelines of neurodegenerative diseases, recently. To improve their diagnostic accuracy a biologically and clinically homogeneous sample is needed for their identification. Alzheimer's disease-related pattern (ADRP) has been identified previously in cohorts of clinically diagnosed patients with dementia due to Alzheimer's disease (AD), meaning that its diagnostic accuracy might have been reduced due to common clinical misdiagnosis. In our study, we aimed to identify ADRP in a cohort of AD patients with CSF confirmed diagnosis, validate it in large out-of-sample cohorts and explore its relationship with patients' clinical status. For identification we analyzed 2-[18F]FDG PET brain scans of 20 AD patients and 20 normal controls (NCs). For validation, 2-[18F]FDG PET scans from 261 individuals with AD, behavioral variant of frontotemporal dementia, mild cognitive impairment and NC were analyzed. We identified an ADRP that is characterized by relatively reduced metabolic activity in temporoparietal cortices, posterior cingulate and precuneus which co-varied with relatively increased metabolic activity in the cerebellum. ADRP expression significantly differentiated AD from NC (AUC = 0.95) and other dementia types (AUC = 0.76-0.85) and its expression correlated with clinical measures of global cognition and neuropsychological indices in all cohorts.

Metabolic brain pattern in dementia with Lewy bodies: Relationship to Alzheimer's disease topography.

PURPOSE: Dementia with Lewy bodies (DLB) is the second most common neurodegenerative dementia, that shares clinical and metabolic similarities with both Alzheimer's and Parkinson's disease. In this study we aimed to identify a DLB-related pattern (DLBRP), study its relationship with other metabolic brain patterns and explore its diagnostic and prognostic value. METHODS: A cohort of 79 participants with DLB, 63 with dementia due to Alzheimer's disease (AD) and 41 normal controls (NCs) and their 2-[18F]FDG PET scans were analysed for identification and validation of DLBRP. Voxel-wise correlation and multiple linear regression were used to study the relation between DLBRP and Alzheimer's disease-related pattern (ADRP), Parkinson's disease-related pattern (PDRP) and PD-related cognitive pattern (PDCP). Diagnostic and prognostic value of DLBRP and of modified DLBRP after accounting for ADRP overlap (DLBRP ⊥ ADRP), were explored. RESULTS: The newly identified DLBRP shared topographic similarities with ADRP (R2 = 24%) and PDRP (R2 = 37%), but not with PDCP. We could accurately discriminate between DLB and NC (AUC = 0.99) based on DLBRP expression, and between DLB and AD (AUC = 0.87) based on DLBRP ⊥ ADRP expression. DLBRP expression correlated with cognitive impairment, but the correlation was lost after accounting for ADRP overlap. DLBRP and DLBRP ⊥ ADRP correlated with patients' survival time. CONCLUSION: DLBRP has proven to be a specific metabolic brain biomarker of DLB, sharing similarities with ADRP and PDRP, but not PDCP. We observed a similar metabolic mechanism underlying cognitive impairment in DLB and AD. DLB-specific metabolic changes were more detrimental for overall survival.

Abnormal metabolic covariance patterns associated with multiple system atrophy and progressive supranuclear palsy.

PURPOSE: Differentiation between neurodegenerative parkinsonisms, whose early clinical presentation is similar, may be improved with metabolic brain imaging. In this study we applied a specific network analysis to 2-[18F]FDG PET brain scans to identify the characteristic metabolic patterns for multiple system atrophy (MSA) and progressive supranuclear palsy (PSP) in a new European cohort. We also developed a new tool to recognize and estimate patients' metabolic brain heterogeneity. METHODS: 20 MSA-P patients, 20 PSP patients and 20 healthy controls (HC) underwent 2-[18F]FDG PET brain imaging. The scaled subprofile model/principal component analysis was applied to identify MSA/PSP-related patterns; MSARP and PSPRP. Additional, 56 MSA, 45 PSP, 116 PD and 61 HC subjects were analyzed for validation. We innovatively applied heat-map analysis to extract and graphically display the pattern's regional sub-scores in individual subjects. RESULTS: MSARP was characterized by hypometabolism in cerebellum and putamen, and PSPRP by hypometabolism in medial prefrontal cortices, nucleus caudatus, frontal cortices and mesencephalon. Patterns' expression discriminated between MSA/PSP patients and HCs as well as between different parkinsonian cohorts (p < 0.001). Both patterns were sensitive and specific (AUC for MSARP/PSPRP: 0.96/0.99). Heat-map analysis showed differences within MSA/PSP subjects and HCs consistent with clinical presentation. CONCLUSIONS: Replication and validation of MSARP and PSPRP confirms robustness of these metabolic biomarkers and supports its application in clinical and research practice. Heat-map analysis gives us an insight into the contribution of various pattern's regions to patterns' expression in individual subjects, which improves our insight into the heterogeneity of studied syndromes.

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

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

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

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

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

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