Metabolic Brain Changes Can Predict the Underlying Pathology in Neurodegenerative Brain Disorders: A Case Report of Sporadic Creutzfeldt-Jakob Disease with Concomitant Parkinson's Disease.

The co-occurrence of multiple proteinopathies is being increasingly recognized in neurodegenerative disorders and poses a challenge in differential diagnosis and patient selection for clinical trials. Changes in brain metabolism captured by positron emission tomography (PET) with 18 F-fluorodeoxyglucose (FDG) allow us to differentiate between different neurodegenerative disorders either by visual exploration or by studying disease-specific metabolic networks in individual patients. However, the impact of multiple proteinopathies on brain metabolism and metabolic networks remains unknown due to the absence of pathological studies. In this case study, we present a 67-year-old patient with rapidly progressing dementia clinically diagnosed with probable sporadic Creutzfeldt-Jakob disease (sCJD). However, in addition to the expected pronounced cortical and subcortical hypometabolism characteristic of sCJD, the brain FDG PET revealed an intriguing finding of unexpected relative hypermetabolism in the bilateral putamina, raising suspicions of coexisting Parkinson's disease (PD). Additional investigation of disease-specific metabolic brain networks revealed elevated expression of both CJD-related pattern (CJDRP) and PD-related pattern (PDRP) networks. The patient eventually developed akinetic mutism and passed away seven weeks after symptom onset. Neuropathological examination confirmed neuropathological changes consistent with sCJD and the presence of Lewy bodies confirming PD pathology. Additionally, hyperphosphorylated tau and TDP-43 pathology were observed, a combination of four proteinopathies that had not been previously reported. Overall, this case provides valuable insights into the complex interplay of neurodegenerative pathologies and their impact on metabolic brain changes, emphasizing the role of metabolic brain imaging in evaluating potential presence of multiple proteinopathies.

Functional brain networks in the evaluation of patients with neurodegenerative disorders.

Network analytical tools are increasingly being applied to brain imaging maps of resting metabolic activity (PET) or blood oxygenation-dependent signals (functional MRI) to characterize the abnormal neural circuitry that underlies brain diseases. This approach is particularly valuable for the study of neurodegenerative disorders, which are characterized by stereotyped spread of pathology along discrete neural pathways. Identification and validation of disease-specific brain networks facilitate the quantitative assessment of pathway changes over time and during the course of treatment. Network abnormalities can often be identified before symptom onset and can be used to track disease progression even in the preclinical period. Likewise, network activity can be modulated by treatment and might therefore be used as a marker of efficacy in clinical trials. Finally, early differential diagnosis can be achieved by simultaneously measuring the activity levels of multiple disease networks in an individual patient's scans. Although these techniques were originally developed for PET, over the past several years analogous methods have been introduced for functional MRI, a more accessible non-invasive imaging modality. This advance is expected to broaden the application of network tools to large and diverse patient populations.

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.

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.

Adverse effects of levodopa/carbidopa intrajejunal gel treatment: A single-center long-term follow-up study.

OBJECTIVES: Levodopa/carbidopa intrajejunal gel (LCIG) is an effective therapeutic strategy to overcome levodopa-induced motor complications in advanced Parkinson's disease (PD). However, it requires invasive percutaneous endoscopic gastrojejunostomy (PEG-J) and may be associated with serious adverse effects (AE). In this study, we aimed to evaluate long-term AEs related to LCIG treatment in a large homogenous cohort of advanced PD patients. METHODS: One hundred three consecutive PD patients were regularly monitored for LCIG-related, PEG-J-related, and device-related AEs up to 14 years. Incidence of AEs was studied in time applying a time-to-event analysis and Cox proportional hazard model with age, disease duration, gender, and recurrent AE as covariates. Health-related quality of life (HRQoL) was estimated at each visit and compared to HRQoL before the LCIG treatment. RESULTS: Among 296 AEs noted, 48.8% were LCIG-related, 32.4% PEG-J-related, and 19.6% device-related. While most of the studied AEs steadily accumulated throughout the follow-up period, 24.3% of the patients (95% CI 10.1%-36.3%) experienced PEG-J-related AE already within the first days after the PEG-J insertion. Cox model revealed that older patients had higher probability of psychosis, PEG-J- and device-related AEs (p < .05, p < .05, and p = .02) and suggested increased recurrence risk in those with early PEG-J and device-related AEs. Despite relatively high incidence of AEs, HRQoL significantly increased in the follow-up period (p < .0001). CONCLUSION: AEs related to LCIG treatment are common. Therefore, careful patient selection and monitoring throughout the treatment is recommended, especially in those with early side effects. Nevertheless, LCIG significantly improves HRQoL in advanced PD patients on a long term.

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.

Atypical clinical presentation of pathologically proven Parkinson's disease: The role of Parkinson's disease related metabolic pattern.

Regional changes in brain metabolism upgraded with measurements of specific metabolic brain patterns and automated diagnostic algorithms can help to differentiate among neurodegenerative parkinsonisms, but with few reports on pathological confirmation. Here we describe a parkinsonian patient with atypical presentation and 18F-FDG-PET imaging consistent with idiopathic Parkinson's disease. The latter was confirmed at the pathohistological examination.

Differential diagnosis of parkinsonian syndromes: a comparison of clinical and automated - metabolic brain patterns' based approach.

PURPOSE: Differentiation among parkinsonian syndromes may be clinically challenging, especially at early disease stages. In this study, we used 18F-FDG-PET brain imaging combined with an automated image classification algorithm to classify parkinsonian patients as Parkinson's disease (PD) or as an atypical parkinsonian syndrome (APS) at the time when the clinical diagnosis was still uncertain. In addition to validating the algorithm, we assessed its utility in a "real-life" clinical setting. METHODS: One hundred thirty-seven parkinsonian patients with uncertain clinical diagnosis underwent 18F-FDG-PET and were classified using an automated image-based algorithm. For 66 patients in cohort A, the algorithm-based diagnoses were compared with their final clinical diagnoses, which were the gold standard for cohort A and were made 2.2 ± 1.1 years (mean ± SD) later by a movement disorder specialist. Seventy-one patients in cohort B were diagnosed by general neurologists, not strictly following diagnostic criteria, 2.5 ± 1.6 years after imaging. The clinical diagnoses were compared with the algorithm-based ones, which were considered the gold standard for cohort B. RESULTS: Image-based automated classification of cohort A resulted in 86.0% sensitivity, 92.3% specificity, 97.4% positive predictive value (PPV), and 66.7% negative predictive value (NPV) for PD, and 84.6% sensitivity, 97.7% specificity, 91.7% PPV, and 95.5% NPV for APS. In cohort B, general neurologists achieved 94.7% sensitivity, 83.3% specificity, 81.8% PPV, and 95.2% NPV for PD, while 88.2%, 76.9%, 71.4%, and 90.9% for APS. CONCLUSION: The image-based algorithm had a high specificity and the predictive values in classifying patients before a final clinical diagnosis was reached by a specialist. Our data suggest that it may improve the diagnostic accuracy by 10-15% in PD and 20% in APS when a movement disorder specialist is not easily available.

Parkinson's disease in a patient with multiple sclerosis and heterozygous glucocerebrosidase gene mutation.

More than 30 patients with multiple sclerosis (MS) and Parkinson's disease (PD) have been reported so far. Theories on the co-occurrence of MS and PD range from coincidental to causal. There has been only one report of MS in young onset PD in a patient heterozygous for Parkin mutation. We report a patient with MS who developed signs typical for PD and was found to be heterozygous mutation carrier in the gene for glucocerebrosidase (GBA1), a well-known risk factor for PD.

High Incidence of Sporadic Creutzfeldt-Jakob Disease in Slovenia in 2015: A Case Series.

BACKGROUND: Creutzfeldt-Jakob disease (CJD) is a rare fatal neurodegenerative disorder presenting with rapid cognitive decline and additional signs. The clinical characteristics of an increasing number of sporadic CJD (sCJD) patients admitted to the Ljubljana University Medical Centre are presented as well as the incidence of sCJD in Slovenia in 2015 compared to previous years. METHODS: We investigated patients presenting with rapidly progressive dementia and at least one additional sign. The diagnosis was made based on clinical diagnostic criteria and an autopsy was performed in all cases. Data on definite sCJD cases in Slovenia since 1999 were obtained and its incidence was calculated. RESULTS: Eight patients with definite sCJD died in 2015 in Slovenia (incidence: 3.89 cases per million). The long-term incidence 1999 was 1.67 per million. CONCLUSIONS: The incidence of sCJD was considerably higher in 2015. It reflects fluctuations in sporadic cases of this rare disease. The rising trend might indicate a previous underestimation and better recognition of the disease.

Distribution of HPV genotypes in Slovenian patients with anal carcinoma: preliminary results.

The aim of the present study was to obtain first data on the distribution of human papillomavirus (HPV) genotypes in patients with anal cancer (AC) in Slovenia. A total of 21 samples of AC (16 archival FFPE samples and 5 fresh-frozen tissue samples) collected from the same number of patients were analysed. All samples were tested for the presence of HPV DNA using a consensus GP5+/ GP6+ PCR and HPV genotypes determined by the INNO LiPA HPV Genotyping Extra test, capable of recognizing 28 different alfa-HPV genotypes. All 21 AC samples were HPV DNA positive. The most frequent HPV genotype, found in 19/21 AC samples, was HPV-16. Only low-risk HPV-6 was detected in one sample and infection with high-risk HPV-52 and low-risk HPV-61 was identified in one sample. Prophylactic HPV vaccination with currently available vaccines could potentially prevent the great majority of anal cancers in Slovenia.