scholarly journals Metabolic Brain Network Analysis of FDG-PET in Alzheimer’s Disease Using Kernel-Based Persistent Features

Molecules ◽  
2019 ◽  
Vol 24 (12) ◽  
pp. 2301 ◽  
Author(s):  
Liqun Kuang ◽  
Deyu Zhao ◽  
Jiacheng Xing ◽  
Zhongyu Chen ◽  
Fengguang Xiong ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Algebraic Topology ◽  
Fdg Pet ◽  
Persistent Homology ◽  
Brain Network ◽  
Imaging Biomarker ◽  
Imaging Data ◽  
Significant Group ◽  
Preclinical Ad

Recent research of persistent homology in algebraic topology has shown that the altered network organization of human brain provides a promising indicator of many neuropsychiatric disorders and neurodegenerative diseases. However, the current slope-based approach may not accurately characterize changes of persistent features over graph filtration because such curves are not strictly linear. Moreover, our previous integrated persistent feature (IPF) works well on an rs-fMRI cohort while it has not yet been studied on metabolic brain networks. To address these issues, we propose a novel univariate network measurement, kernel-based IPF (KBI), based on the prior IPF, to quantify the difference between IPF curves. In our experiments, we apply the KBI index to study fluorodeoxyglucose positron emission tomography (FDG-PET) imaging data from 140 subjects with Alzheimer’s disease (AD), 280 subjects with mild cognitive impairment (MCI), and 280 healthy normal controls (NC). The results show the disruption of network integration in the progress of AD. Compared to previous persistent homology-based measures, as well as other standard graph-based measures that characterize small-world organization and modular structure, our proposed network index KBI possesses more significant group difference and better classification performance, suggesting that it may be used as an effective preclinical AD imaging biomarker.

scholarly journals Disrupted metabolic connectivity in dopaminergic and cholinergic networks at different stages of dementia from 18F-FDG PET brain persistent homology network

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Tun-Wei Hsu ◽  
Jong-Ling Fuh ◽  
Da-Wei Wang ◽  
Li-Fen Chen ◽  
Chia-Jung Chang ◽  
...  
Keyword(s):  
Algebraic Topology ◽  
Fdg Pet ◽  
Persistent Homology ◽  
Network Connectivity ◽  
Brain Regions ◽  
Imaging Data ◽  
Cholinergic Pathways ◽  
Positron Emission ◽  
Metabolic Connectivity ◽  
Neurochemical Changes

AbstractDementia is related to the cellular accumulation of β-amyloid plaques, tau aggregates, or α-synuclein aggregates, or to neurotransmitter deficiencies in the dopaminergic and cholinergic pathways. Cellular and neurochemical changes are both involved in dementia pathology. However, the role of dopaminergic and cholinergic networks in metabolic connectivity at different stages of dementia remains unclear. The altered network organisation of the human brain characteristic of many neuropsychiatric and neurodegenerative disorders can be detected using persistent homology network (PHN) analysis and algebraic topology. We used 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) imaging data to construct dopaminergic and cholinergic metabolism networks, and used PHN analysis to track the evolution of these networks in patients with different stages of dementia. The sums of the network distances revealed significant differences between the network connectivity evident in the Alzheimer’s disease and mild cognitive impairment cohorts. A larger distance between brain regions can indicate poorer efficiency in the integration of information. PHN analysis revealed the structural properties of and changes in the dopaminergic and cholinergic metabolism networks in patients with different stages of dementia at a range of thresholds. This method was thus able to identify dysregulation of dopaminergic and cholinergic networks in the pathology of dementia.

scholarly journals Quantitative Gradient Echo MRI Identifies Dark Matter as a New Imaging Biomarker of Neurodegeneration that Precedes Tisssue Atrophy in Early Alzheimer’s Disease

2021 ◽  
pp. 1-20
Author(s):  
Satya V.V.N. Kothapalli ◽  
Tammie L. Benzinger ◽  
Andrew J. Aschenbrenner ◽  
Richard J. Perrin ◽  
Charles F. Hildebolt ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Dark Matter ◽  
Amyloid Β ◽  
Neuronal Loss ◽  
Imaging Biomarker ◽  
Control Group ◽  
Histopathological Study ◽  
Preclinical Ad

Background: Currently, brain tissue atrophy serves as an in vivo MRI biomarker of neurodegeneration in Alzheimer’s disease (AD). However, postmortem histopathological studies show that neuronal loss in AD exceeds volumetric loss of tissue and that loss of memory in AD begins when neurons and synapses are lost. Therefore, in vivo detection of neuronal loss prior to detectable atrophy in MRI is essential for early AD diagnosis. Objective: To apply a recently developed quantitative Gradient Recalled Echo (qGRE) MRI technique for in vivo evaluation of neuronal loss in human hippocampus. Methods: Seventy participants were recruited from the Knight Alzheimer Disease Research Center, representing three groups: Healthy controls [Clinical Dementia Rating® (CDR®) = 0, amyloid β (Aβ)-negative, n = 34]; Preclinical AD (CDR = 0, Aβ-positive, n = 19); and mild AD (CDR = 0.5 or 1, Aβ-positive, n = 17). Results: In hippocampal tissue, qGRE identified two types of regions: one, practically devoid of neurons, we designate as “Dark Matter”, and the other, with relatively preserved neurons, “Viable Tissue”. Data showed a greater loss of neurons than defined by atrophy in the mild AD group compared with the healthy control group; neuronal loss ranged between 31% and 43%, while volume loss ranged only between 10% and 19%. The concept of Dark Matter was confirmed with histopathological study of one participant who underwent in vivo qGRE 14 months prior to expiration. Conclusion: In vivo qGRE method identifies neuronal loss that is associated with impaired AD-related cognition but is not recognized by MRI measurements of tissue atrophy, therefore providing new biomarkers for early AD detection.

scholarly journals White Matter Brain Network Research in Alzheimer’s Disease Using Persistent Features

Molecules ◽  
2020 ◽  
Vol 25 (11) ◽  
pp. 2472
Author(s):  
Liqun Kuang ◽  
Yan Gao ◽  
Zhongyu Chen ◽  
Jiacheng Xing ◽  
Fengguang Xiong ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
White Matter ◽  
Diffusion Tensor ◽  
Persistent Homology ◽  
Brain Structures ◽  
Brain Network ◽  
Global Network ◽  
Network Research ◽  
Structural Brain

Despite the severe social burden caused by Alzheimer’s disease (AD), no drug than can change the disease progression has been identified yet. The structural brain network research provides an opportunity to understand physiological deterioration caused by AD and its precursor, mild cognitive impairment (MCI). Recently, persistent homology has been used to study brain network dynamics and characterize the global network organization. However, it is unclear how these parameters reflect changes in structural brain networks of patients with AD or MCI. In this study, our previously proposed persistent features and various traditional graph-theoretical measures are used to quantify the topological property of white matter (WM) network in 150 subjects with diffusion tensor imaging (DTI). We found significant differences in these measures among AD, MCI, and normal controls (NC) under different brain parcellation schemes. The decreased network integration and increased network segregation are presented in AD and MCI. Moreover, the persistent homology-based measures demonstrated stronger statistical capability and robustness than traditional graph-theoretic measures, suggesting that they represent a more sensitive approach to detect altered brain structures and to better understand AD symptomology at the network level. These findings contribute to an increased understanding of structural connectome in AD and provide a novel approach to potentially track the progression of AD.

Impairment of Episodic-Specific Autobiographical Memory in Individuals with Subjective Cognitive Decline and in Patients with Prodromal or Mild Alzheimer’s Disease

2021 ◽  
pp. 1-12
Author(s):  
Anna E. Bruus ◽  
Gunhild Waldemar ◽  
Asmus Vogel
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Decline ◽  
Autobiographical Memory ◽  
Short Form ◽  
Brain Network ◽  
Subjective Cognitive Decline ◽  
Significant Group ◽  
Preclinical Phase ◽  
The Difference

Background: Autobiographical memory (AM) is a personal form of memory that becomes impaired in the early, clinical stages of Alzheimer’s disease (AD). In the “preclinical” phase of AD, neuropathological hallmarks are present (especially in a brain network underpinning AM), but performance on standardized neuropsychological tests is normal. Even so, some patients have subjective cognitive decline (SCD). Objective: The aim was to 1) investigate AM performance on two tests with different approaches in SCD, and in prodromal and mild AD, and 2) examine the association between the AM tests. Methods: We included 17 SCD patients with heightened risk of AD, 17 amnestic mild cognitive impairment (aMCI) patients, 17 patients with mild dementia due to AD, and 30 healthy controls. Patients were diagnosed according to international criteria, and all participants had MMSE scores≥24. AM was assessed using the Columbia Autobiographical Memory Interview-Short Form (CAMI-SF) and the Three Events Test. These tests measure the production of contextual details. Results: Significant group effects were found for the Three Events Test and the CAMI-SF. All patient groups produced significantly fewer contextual details than the controls on the Three Events Test. On CAMI-SF, the aMCI and mild AD groups were able to answer fewer questions or gave significantly less detailed answers than the other groups. The SCD patients performed below the controls on CAMI-SF, but the difference was not significant. Conclusion: AM may be impaired in very early AD, even in the phases where standardized episodic memory tests show no decline.

Cerebrospinal Fluid BACE1 Activity and sAβPPβ as Biomarker Candidates of Alzheimer’s Disease

2018 ◽  
Vol 45 (3-4) ◽  
pp. 152-161 ◽  
Author(s):  
Panagiotis Alexopoulos ◽  
Nathalie Thierjung ◽  
Timo Grimmer ◽  
Marion Ortner ◽  
Polychronis Economou ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cerebrospinal Fluid ◽  
Fdg Pet ◽  
Regression Models ◽  
Amyloid Β ◽  
Preclinical Ad ◽  
Positron Emission ◽  
Bace1 Activity ◽  
Mci Due To Ad

Background/Aims: The utility of β-site amyloid-β precursor protein (AβPP) cleaving enzyme 1 (BACE1) activity and soluble AβPP β (sAβPPβ) levels in cerebrospinal fluid (CSF) in detecting Alzheimer’s disease (AD) is still elusive. Methods: BACE1 activity and sAβPPβ concentration were measured in patients with AD dementia (n = 56) and mild cognitive impairment (MCI) due to AD (n = 76) with abnormal routine AD CSF markers, in patients with MCI with normal CSF markers (n = 39), and in controls without preclinical AD (n = 48). In a subsample with available 18F-fluorodeoxyglucose positron emission tomography (FDG PET) data, ordinal regression models were employed to compare the contribution of BACE1 and sAβPPβ to correct diagnostic classification to that of FDG PET. Results: BACE1 activity was significantly higher in patients with MCI due to AD compared to both controls and patients with MCI with normal CSF markers. sAβPPβ did not differ between any of the studied groups. Interestingly, BACE1 activity was not found to be inferior to FDG PET as predictive covariate in differentiating between the diagnostic groups. Conclusions: Further studies using biomarker-underpinned diagnoses are warranted to shed more light on the potential diagnostic utility of BACE1 activity as AD biomarker candidate in MCI.

Crossed Cerebellar Diaschisis in Alzheimer’s Disease

2018 ◽  
Vol 15 (13) ◽  
pp. 1267-1275 ◽  
Author(s):  
F.E. Reesink ◽  
D. Vállez García ◽  
C.A. Sánchez-Catasús ◽  
D.E. Peretti ◽  
A.T. Willemsen ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Left Hemisphere ◽  
Pet Imaging ◽  
Fdg Pet ◽  
Pathophysiological Mechanism ◽  
Crossed Cerebellar Diaschisis ◽  
Amyloid Accumulation ◽  
Cerebellar Diaschisis ◽  
18F Fdg

Background: We describe the phenomenon of crossed cerebellar diaschisis (CCD) in four subjects diagnosed with Alzheimer’s disease (AD) according to the National Institute on Aging - Alzheimer Association (NIA-AA) criteria, in combination with 18F-FDG PET and 11C-PiB PET imaging. Methods: 18F-FDG PET showed a pattern of cerebral metabolism with relative decrease most prominent in the frontal-parietal cortex of the left hemisphere and crossed hypometabolism of the right cerebellum. 11C-PiB PET showed symmetrical amyloid accumulation, but a lower relative tracer delivery (a surrogate of relative cerebral blood flow) in the left hemisphere. CCD is the phenomenon of unilateral cerebellar hypometabolism as a remote effect of supratentorial dysfunction of the brain in the contralateral hemisphere. The mechanism implies the involvement of the cortico-ponto-cerebellar fibers. The pathophysiology is thought to have a functional or reversible basis but can also reflect in secondary morphologic change. CCD is a well-recognized phenomenon, since the development of new imaging techniques, although scarcely described in neurodegenerative dementias. Results: To our knowledge this is the first report describing CCD in AD subjects with documentation of both 18F-FDG PET and 11C-PiB PET imaging. CCD in our subjects was explained on a functional basis due to neurodegenerative pathology in the left hemisphere. There was no structural lesion and the symmetric amyloid accumulation did not correspond with the unilateral metabolic impairment. Conclusion: This suggests that CCD might be caused by non-amyloid neurodegeneration. The pathophysiological mechanism, clinical relevance and therapeutic implications of CCD and the role of the cerebellum in AD need further investigation.

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