The differential diagnosis of neurodegenerative brain diseases may be difficult on clinical grounds only, especially at an early disease stage. Neurodegenerative brain diseases such as Parkinson’s disease (PD), multiple system atrophy (MSA), progressive supranuclear palsy (PSP), corticobasal degeneration (CBD), dementia with Lewy Bodies (DLB), Alzheimer’s disease (AD) and frontotemporal dementia (FTD) have overlapping features at presentation, while the typical clinical syndrome may become clear at later disease stages. It is important to diagnose these patients early, because prognosis and treatment options differ between neurodegenerative brain diseases. For this reason, there is increasing interest to use neuroimaging techniques in the hope to discover abnormal patterns of brain structure, energy consumption or network activity changes which are characteristic of such diseases.
Functional imaging of cerebral glucose metabolism with [18F]-fluoro-deoxyglucose positron emission tomography (FDG PET) provides an index for regional neuronal activity. As the PET scan has become widely available for clinical practice in recent years (mainly for the diagnostics in malignancies), it is possible to study the role of the FDG PET scan in the diagnostics of neurodegenerative brain diseases. Many investigations have been performed to identify metabolic brain patterns in FDG PET images of neurodegenerative brain diseases; first with univariate analysis techniques, and more recently with multivariate statistical analysis techniques. By using the SSM/PCA method (a multivariate statistical analysis) the research team in Groningen has implemented metabolic patterns for 4 neurodegenerative diseases (in collaboration with the team of Eidelberg, New York (USA)): PD, MSA, PSP and AD.
GLIMPS is a Dutch multicenter imaging project: Glucose Imaging in Parkinsonian Syndromes. The GLIMPS project has been initiated to create a database of FDG PET images of patients in different disease categories together with their specific clinical information. The data will be stored anonymously. The aim of this database is to include enough patients to create several metabolic brain patterns of all relevant neurodegenerative brain diseases.
The first goal of the project is to assist clinical practice in the differential diagnosis of neurodegenerative brain diseases in individual patients. The possible presence of a disease-specific metabolic brain pattern in an individual patients will be calculated by applying the image-based classification algorithm for the to date identified metabolic brain patterns. The outcome of the classification algorithm will be compared to the final clinical diagnosis to evaluate the effectiveness of the proposed new technique.
A second goal is to develop further the glucose metabolic brain patterns in different disease categories in specific scientific projects. It will be clear that further elaboration of glucose metabolic brain patterns for neurodegenerative brain diseases largely depend on the correct selection of the necessary reference patient groups for the identification procedure.
Expanding the sample size will also increase sensitivity and specificity of the listed metabolic brain patterns and will enable subclassifications in for example left/right body-side affected patients. Furthermore a large control sample consisting of enough patients within different age categories is important to study differences in for example man and women, right left handedness and to see differences in brain patterns in an aging population. In this way, patient care focused on the early differential diagnosis of patients with neurodegenerative brain diseases is expected to improve.