Brain metabolic patterns in patients with suspected non-Alzheimer’s pathophysiology (SNAP) and Alzheimer’s disease (AD): is [18F] FDG a specific biomarker in these patients?

Chiaravalloti, Agostino; Barbagallo, Gaetano; Martorana, Alessandro; Castellano, Anna Elisa; Ursini, Francesco; Schillaci, Orazio

doi:10.1007/s00259-019-04379-4

Cited by 14 publications

(20 citation statements)

References 35 publications

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“…Previous studies reported that tau deposition in SNAP is located in the bilateral medial and lateral temporal region (Dodich et al, 2020), as well as hypometabolism in temporoparietal regions (Schreiber et al, 2017;Chiaravalloti et al, 2019). This is somehow in line with the magnetic resonance imaging (MRI) findings, which found more severe baseline hippocampal atrophy in SNAP than normal control (NC; Caroli et al, 2015;Burnham et al, 2016;Gordon et al, 2016;Chung et al, 2017).…”

Section: Introductionsupporting

confidence: 83%

Distinct Brain Functional Impairment Patterns Between Suspected Non-Alzheimer Disease Pathophysiology and Alzheimer’s Disease: A Study Combining Static and Dynamic Functional Magnetic Resonance Imaging

Luo

et al. 2020

Front. Aging Neurosci.

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Background: Suspected non-Alzheimer disease pathophysiology (SNAP) refers to the subjects who feature negative β-amyloid (Aβ) but positive tau or neurodegeneration biomarkers. It accounts for a quarter of the elderly population and is associated with cognitive decline. However, the underlying pathophysiology is still unclear.Methods: We included 111 non-demented subjects, then classified them into three groups using cerebrospinal fluid (CSF) Aβ 1–42 (A), phosphorylated tau 181 (T), and total tau (N). Specifically, we identified the normal control (NC; subjects with normal biomarkers, A-T-N-), SNAP (subjects with normal amyloid but abnormal tau, A−T+), and predementia Alzheimer’s disease (AD; subjects with abnormal amyloid and tau, A+T+). Then, we used the static amplitude of low-frequency fluctuation (sALFF) and dynamic ALFF (dALFF) variance to reflect the intrinsic functional network strength and stability, respectively. Further, we performed a correlation analysis to explore the possible relationship between intrinsic brain activity changes and cognition.Results: SNAP showed decreased sALFF in left superior frontal gyrus (SFG) while increased sALFF in left insula as compared to NC. Regarding the dynamic metric, SNAP showed a similarly decreased dALFF in the left SFG and left paracentral lobule as compared to NC. By contrast, when compared to NC, predementia AD showed decreased sALFF in left inferior parietal gyrus (IPG) and right precuneus, while increased sALFF in the left insula, with more widely distributed decreased dALFF variance across the frontal, parietal and occipital lobe. When directly compared to SNAP, predementia AD showed decreased sALFF in left middle occipital gyrus and IPG, while showing decreased dALFF variance in the left temporal pole. Further correlation analysis showed that increased sALFF in the insula had a negative correlation with the general cognition in the SNAP group. Besides, sALFF and dALFF variance in the right precuneus negatively correlated with attention in the predementia AD group.Conclusion: SNAP and predementia AD show distinct functional impairment patterns. Specifically, SNAP has functional impairments that are confined to the frontal region, which is usually spared in early-stage AD, while predementia AD exhibits widely distributed functional damage involving the frontal, parietal and occipital cortex.

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Section: Introductionsupporting

confidence: 83%

Distinct Brain Functional Impairment Patterns Between Suspected Non-Alzheimer Disease Pathophysiology and Alzheimer’s Disease: A Study Combining Static and Dynamic Functional Magnetic Resonance Imaging

Luo

et al. 2020

Front. Aging Neurosci.

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“…Moreover, in all patients, a neurologist examination, neuropsychological examination, a complete neuropsychiatric evaluation, and neuroimaging consisting of magnetic resonance imaging (1.5 T MRI) was performed. Exclusion criteria were the following: isolated deficits and/or unmodified MMSE (<25/30) on revisit (6, 12, and 18 months follow-up), clinically manifest acute stroke in the last 6 months (Hachinsky scale >4, and radiological evidence of subcortical lesions), as described in previous papers [ 11 , 12 , 13 ]. None of the patients enrolled had pyramidal and/or extrapyramidal signs reported at the neurological examination.…”

Section: Methodsmentioning

confidence: 99%

“…Then, CSF total Tau (T-Tau) and phosphorylated Tau (Thr181, p-Tau) concentration was evaluated using a sandwich ELISA (Innotest hTAU-Ag, Innogenetics, Gent, Belgium). CSF Aβ1–42 levels were determined using a sandwich ELISA (Innotest ® ß- amyloid (1–42), Innogenetics, Gent, Belgium), specifically elaborated to measure Aβ containing both the first and 42nd amino acid, as described in previous papers [ 11 , 12 , 13 , 14 , 15 ].…”

Section: Methodsmentioning

confidence: 99%

The Brain Metabolic Correlates of the Main Indices of Neuropsychological Assessment in Alzheimer’s Disease

Chiaravalloti

Ricci

Biagio

et al. 2020

JPM

Self Cite

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Background: The study aimed to investigate the relationships between F-18 fluorodeoxyglucose (18F)FDG uptake and neuropsychological assessment in Alzheimer’s disease (AD). Methods: We evaluated 116 subjects with AD according to the NINCDS-ADRDA criteria. All the subjects underwent a brain PET/CT with (18F)FDG, cerebrospinal fluid (CSF) assay, mini-mental state examination (MMSE) and further neuropsychological tests: Rey auditory verbal learning test, immediate recall (RAVLT immediate); Rey auditory verbal learning test, delayed recall (RAVLT, delayed); Rey complex figure test, copy (RCFT, copy); Rey complex figure test, delayed recall (RCFT, delayed); Raven’s colored progressive matrices (RCPM); phonological word fluency test (PWF) and Stroop test. We performed the statistical analysis by using statistical parametric mapping (SPM12; Wellcome Department of Cognitive Neurology, London, UK). Results: A significant relationship has been reported between (18F)FDG uptake and RAVLT immediate test in Brodmann area (BA)37 and BA22 and with RCFT, copy in BA40, and BA7. We did not find any significant relationships with other tests. Conclusion: In the AD population, brain (18F)FDG uptake is moderately related to the neuropsychological assessment, suggesting a limited impact on statistical data analysis of glucose brain metabolism.

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“…For this reason, a number of biomarkers have been tested, including imaging studies, cerebrospinal fluid samples (CSF), and more recently blood tests that may be easier to perform. Imaging studies, for example, include glucose metabolism (measured by PET 18fluoroglucose uptake into the brain) [61,103,104], structural imaging studies showing atrophy including the fornix [105][106][107][108], functional MRI and resting state MRI to characterize blood flow changes and network changes [109][110][111], and assessment of CSF clearance to estimate glymphatic flow [43,63,69,[112][113][114]. There are also studies prospectively analyzing the use of direct amyloid imaging to compare aging controls with early Alzheimer's patients [115].…”

Section: Biomarkersmentioning

confidence: 99%

“…Tau may also be detected in serum [10,12,122]. The relative efficacy of several of these biomarkers has also been assessed for early detection of AD [61,103,115,122,123].…”

Section: Biomarkersmentioning

confidence: 99%

Contrasting Metabolic Insufficiency in Aging and Dementia

Turner

2021

Aging and disease

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Metabolic insufficiency and neuronal dysfunction occur in normal aging but is exaggerated in dementia and Alzheimer's disease (AD). Metabolic insufficiency includes factors important for both substrate supply and utilization in the brain. Metabolic insufficiency occurs through a number of serial mechanisms, particularly changes in cerebrovascular supply through blood vessel abnormalities (ie, small and large vessel vasculopathy, stroke), alterations in neurovascular coupling providing dynamic blood flow supply in relation to neuronal demand, abnormalities in blood brain barrier including decreased glucose and amino acid transport, altered glymphatic flow in terms of substrate supply across the extracellular space to cells and drainage into CSF of metabolites, impaired transport into cells, and abnormal intracellular metabolism with more reliance on glycolysis and less on mitochondrial function. Recent studies have confirmed abnormal neurovascular coupling in a mouse model of AD in response to metabolic challenges, but the supply chain from the vascular system into neurons is disrupted much earlier in dementia than in equivalently aged individuals, contributing to the progressive neuronal degeneration and cognitive dysfunction associated with dementia. We discuss several metabolic treatment approaches, but these depend on characterizing patients as to who would benefit the most. Surrogate biomarkers of metabolism are being developed to include dynamic estimates of neuronal demand, sufficiency of neurovascular coupling, and glymphatic flow to supplement traditional static measurements. These surrogate biomarkers could be used to gauge efficacy of metabolic treatments in slowing down or modifying dementia time course.

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Brain metabolic patterns in patients with suspected non-Alzheimer’s pathophysiology (SNAP) and Alzheimer’s disease (AD): is [18F] FDG a specific biomarker in these patients?

Cited by 14 publications

References 35 publications

Distinct Brain Functional Impairment Patterns Between Suspected Non-Alzheimer Disease Pathophysiology and Alzheimer’s Disease: A Study Combining Static and Dynamic Functional Magnetic Resonance Imaging

Distinct Brain Functional Impairment Patterns Between Suspected Non-Alzheimer Disease Pathophysiology and Alzheimer’s Disease: A Study Combining Static and Dynamic Functional Magnetic Resonance Imaging

The Brain Metabolic Correlates of the Main Indices of Neuropsychological Assessment in Alzheimer’s Disease

Contrasting Metabolic Insufficiency in Aging and Dementia

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