Abstract
Dementia is broadly characterized by cognitive and psychological dysfunction that significantly impairs daily functioning. Dementia has many causes including Alzheimer’s disease (AD), dementia with Lewy bodies (DLB), and frontotemporal lobar degeneration (FTLD). Detection and differential diagnosis in the early stages of dementia remains challenging. Fueled by AD Neuroimaging Initiatives (ADNI) (Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. As such, the investigators within ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report.), a number of neuroimaging biomarkers for AD have been proposed, yet it remains to be seen whether these markers are also sensitive to other types of dementia. We assessed AD-related metabolic patterns in 27 patients with diverse forms of dementia (five had probable/possible AD while others had atypical cases) and 20 non-demented individuals. All participants had positron emission tomography (PET) scans on file. We used a pre-trained machine learning-based AD designation (MAD) framework to investigate the AD-related metabolic pattern among the participants under study. The MAD algorithm showed a sensitivity of 0.67 and specificity of 0.90 for distinguishing dementia patients from non-dementia participants. A total of 18/27 dementia patients and 2/20 non-dementia patients were identified as having AD-like patterns of metabolism. These results highlight that many underlying causes of dementia have similar hypometabolic pattern as AD and this similarity is an interesting avenue for future research.
Highlights
Major neurocognitive disorder, commonly known as “dementia” is a broad term used to describe cognitive dysfunction including deficits in memory, self-management, language skills, problem solving, attention, and/or visual perception [1,2]
In the present retrospective study, we investigate how the machine learning-based AD designation (MAD) program classifies different patient groups seen in our local neuropsychiatric clinic and further demonstrates that a common phenotype of glucose metabolic pattern exists across the spectrum of dementia
There was a significant difference in age at examination between dementia and non-dementia cohorts, it is worth noting that age was not associated with an SVM-ISDA designation of Alzheimer’s disease (AD) [44]
Summary
Commonly known as “dementia” is a broad term used to describe cognitive dysfunction including deficits in memory, self-management, language skills, problem solving, attention, and/or visual perception [1,2]. Psychological changes such as apathy, anxiety, depression, irritability, psychotic symptoms, sleep issues, hallucinations, inappropriate behavior, and paranoia are common, depending on the underlying disease [1,3]. Mixed dementia is common, especially as patients age, and has several underlying causes, for example, mixed dementia can include a combination of AD, vascular dementia, or DLB [2,7]
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