Alzheimer’s Disease-Related Metabolic Pattern in Diverse Forms of Neurodegenerative Diseases

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.

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

“…We observed that ADRP expression was elevated in DLB patients, probably on the account of overlapping pathology, topography of neurodegeneration and clinical presentation. Elevated ADRP expression in DLB patients in comparison to NC has been reported before ( Katako et al, 2018 , Lau et al, 2021 ). To account for overlapping topography we used an orthogonalization procedure to remove the contribution of AD-network and re-derived a pattern reflecting the “pure” DLB pathology, i. e. DLBRP ⊥ ADRP.…”

Do Epilepsy Patients with Cognitive Impairment Have Alzheimer’s Disease-like Brain Metabolism?

“…Unfortunately, the clinical notes for this patient did not include rationale for why no neuropsychological assessment was performed, or whether this patient required a neuropsychological assessment and/or exhibited cognitive deficits at a later point in time. Although a larger sample is needed to confirm these findings, our recent work also showed low levels of MAD+ designations in healthy controls, those with stable MCI (not progressing to AD for >3 years) [ 30 , 34 ], and primary psychiatric disorders [ 34 ].…”

“…PET data were preprocessed and analyzed in SPM12 software (version 7487, RRID:SCR_007037; Wellcome Trust Centre for Neuroimaging, London, UK, (accessed on 19 February 2020)) using MATLAB (version r2018a, The MathWorks Inc., Natick, MA, USA), as described elsewhere [ 30 , 34 ]. Briefly, FDG-PET images were spatially normalized to standard MNI space for PET (using “Old Normalize” as this does not require T 1 -weighted images), then smoothened with an 8 mm full-width half-maximum Gaussian smoothing kernel.…”

“…SVM is an effective algorithm [ 31 , 32 ] commonly used for neuroimaging [ 31 , 33 ], and uses a binary classifier that draws a boundary between different groups in higher-dimensional space. We have also demonstrated that MAD is also sensitive to other dementia types that are commonly seen in memory clinics, including dementia with Lewy bodies and primary progressive aphasia, and to a lesser degree, frontotemporal dementia [ 30 , 34 ]. Based on the congruence between AD and epilepsy, we applied the SVM-ISDA algorithm to assess the FDG-PET scans of patients with epilepsy and classify them as either MAD+ (AD-like) or MAD- (non-AD-like), particularly noting scans of patients with cognitive deficits as already determined by clinical neuropsychological assessments.…”

Monitoring Alzheimer’s Disease Progression in Mild Cognitive Impairment Stage Using Machine Learning-Based FDG-PET Classification Methods

“…Unlike other tracers that bind to specific proteins that characterizes AD such as florobetapir [ 25 ], florbetaben [ 26 ], flutemetamol [ 27 ], and flortaucipir [ 28 ], the FDG uptake level represents overall “health” of the brain regions, the decline of which is potentially associated with neuronal loss, mitochondrial dysfunction, loss of synaptic activities, or a combination of these [ 29 ]. In fact, we have recently demonstrated that MAD scores were also elevated in some patients with other types of dementia such as dementia with Lewy bodies, frontotemporal dementia, and primary progressive aphasia, suggesting the non-specificity of FDG-PET-based markers for AD in non-AD dementia [ 30 ]. And, it has been previously demonstrated that AD patients also show an accelerated pattern of morphological [ 31 ] and metabolic [ 32 ] changes associated with healthy aging itself.…”