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

Diagnostics ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. 2023
Author(s):  
Angus Lau ◽  
Iman Beheshti ◽  
Mandana Modirrousta ◽  
Tiffany A. Kolesar ◽  
Andrew L. Goertzen ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Lewy Bodies ◽  
Future Research ◽  
Metabolic Pattern ◽  
Dementia Patients ◽  
Positron Emission ◽  
Psychological Dysfunction ◽  
Underlying Causes ◽  
Neuroimaging Biomarkers

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.

A Detailed Phenomenological Comparison of Complex Visual Hallucinations in Dementia With Lewy Bodies and Alzheimer's Disease

1997 ◽  
Vol 9 (4) ◽  
pp. 381-388 ◽  
Author(s):  
Clive Ballard ◽  
Ian McKeith ◽  
Richard Harrison ◽  
John O'Brien ◽  
Peter Thompson ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Dementia With Lewy Bodies ◽  
Lewy Bodies ◽  
Time Of Day ◽  
Visual Hallucinations ◽  
Dementia Patients ◽  
Core Feature ◽  
Definition Of

Visual hallucinations (VH) are a core feature of dementia with Lewy bodies (DLB), but little is known about their phenomenology. A total of 73 dementia patients (42 DLB, 30 Alzheimer's disease [AD], 1 undiagnosed) in contact with clinical services were assessed with a detailed standardized inventory. DLB was diagnosed according to the criteria of McKeith and colleagues, AD was diagnosed using the NINCDS-ADRDA criteria. Autopsy confirmation has been obtained when possible. VH were defined using the definition of Burns and colleagues. Detailed descriptions of hallucinatory experiences were recorded. Annual follow-up interviews were undertaken. The clinical diagnosis has been confirmed in 18 of the 19 cases that have come to autopsy. A total of 93% of DLB patients and 27% of AD patients experienced VH. DLB patients were significantly more likely to experience multiple VH that persisted over follow-up. They were significantly more likely to hear their VH speak but there were no significant differences in the other phenomenological characteristics including whether the hallucinations moved, the time of day that they were experienced, their size, the degree of insight, and whether they were complete. VH may be more likely to be multiple, to speak, and to be persistent in DLB patients. These characteristics could potentially aid accurate diagnosis.

scholarly journals Bayesian model reveals latent atrophy factors with dissociable cognitive trajectories in Alzheimer’s disease

2016 ◽  
Vol 113 (42) ◽  
pp. E6535-E6544 ◽  
Author(s):  
Xiuming Zhang ◽  
Elizabeth C. Mormino ◽  
Nanbo Sun ◽  
Reisa A. Sperling ◽  
Mert R. Sabuncu ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Executive Function ◽  
Bayesian Model ◽  
Late Onset ◽  
Temporal Cortex ◽  
Cortical Atrophy ◽  
Future Research ◽  
Dementia Patients ◽  
With Memory

We used a data-driven Bayesian model to automatically identify distinct latent factors of overlapping atrophy patterns from voxelwise structural MRIs of late-onset Alzheimer’s disease (AD) dementia patients. Our approach estimated the extent to which multiple distinct atrophy patterns were expressed within each participant rather than assuming that each participant expressed a single atrophy factor. The model revealed a temporal atrophy factor (medial temporal cortex, hippocampus, and amygdala), a subcortical atrophy factor (striatum, thalamus, and cerebellum), and a cortical atrophy factor (frontal, parietal, lateral temporal, and lateral occipital cortices). To explore the influence of each factor in early AD, atrophy factor compositions were inferred in beta-amyloid–positive (Aβ+) mild cognitively impaired (MCI) and cognitively normal (CN) participants. All three factors were associated with memory decline across the entire clinical spectrum, whereas the cortical factor was associated with executive function decline in Aβ+ MCI participants and AD dementia patients. Direct comparison between factors revealed that the temporal factor showed the strongest association with memory, whereas the cortical factor showed the strongest association with executive function. The subcortical factor was associated with the slowest decline for both memory and executive function compared with temporal and cortical factors. These results suggest that distinct patterns of atrophy influence decline across different cognitive domains. Quantification of this heterogeneity may enable the computation of individual-level predictions relevant for disease monitoring and customized therapies. Factor compositions of participants and code used in this article are publicly available for future research.

The Trajectory of Cerebrospinal Fluid Growth-Associated Protein 43 in the Alzheimer’s Disease Continuum: A Longitudinal Study

2021 ◽  
pp. 1-12
Author(s):  
Heng Zhang ◽  
Diyang Lyu ◽  
Jianping Jia ◽  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cerebrospinal Fluid ◽  
Amyloid Β ◽  
Dementia Patients ◽  
Positron Emission ◽  
Potential Biomarker ◽  
Synaptic Degeneration ◽  
Over Time

Background: Synaptic degeneration has been suggested as an early pathological event that strongly correlates with severity of dementia in Alzheimer’s disease (AD). However, changes in longitudinal cerebrospinal fluid (CSF) growth-associated protein 43 (GAP-43) as a synaptic biomarker in the AD continuum remain unclear. Objective: To assess the trajectory of CSF GAP-43 with AD progression and its association with other AD hallmarks. Methods: CSF GAP-43 was analyzed in 788 participants from the Alzheimer’s Disease Neuroimaging Initiative (ADNI), including 246 cognitively normal (CN) individuals, 415 individuals with mild cognitive impairment (MCI), and 127 with AD dementia based on cognitive assessments. The associations between a multimodal classification scheme with amyloid-β (Aβ), tau, and neurodegeneration, and changes in CSF GAP-43 over time were also analyzed. Results: CSF GAP-43 levels were increased at baseline in MCI and dementia patients, and increased significantly over time in the preclinical (Aβ-positive CN), prodromal (Aβ-positive MCI), and dementia (Aβ-positive dementia) stages of AD. Higher levels of CSF GAP-43 were also associated with higher CSF phosphorylated tau (p-tau) and total tau (t-tau), cerebral amyloid deposition and hypometabolism on positron emission tomography, the hippocampus and middle temporal atrophy, and cognitive performance deterioration at baseline and follow-up. Furthermore, CSF GAP-43 may assist in effectively predicting the probability of dementia onset at 2- or 4-year follow-up. Conclusion: CSF GAP-43 can be used as a potential biomarker associated with synaptic degeneration in subjects with AD; it may also be useful for tracking the disease progression and for monitoring the effects of clinical trials.

Neuroimaging in Alzheimer’s Disease

2013 ◽  
pp. 427-431 ◽  
Author(s):  
Hidenao Fukuyama
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Posterior Part ◽  
Lewy Body Disease ◽  
New Drugs ◽  
Amyloid Protein ◽  
Dementia Patients ◽  
Positron Emission ◽  
Occipital Lobes ◽  
The Brain

The diagnosis of Alzheimer’s disease (AD) is often based on clinical and pathological data. Positron emission tomography (PET) using the tracer 18F-FDG revealed findings specific to AD-mainly the posterior part of the brain and the association cortices of the parietal and occipital lobes were affected by a reduction in glucose metabolism. Recent advances in the development of tracers for amyloid protein, which is the key protein in the pathogenesis of AD, enables the pattern of deposition of amyloid protein in the brain to be visualized. Various tracers have been introduced to visualize other aspects of AD pathology. Recent clinical interests on dementia have focused on the early detection of AD and variation of Parkinson’s disease, namely dementia with Lewy body disease (DLB), because the earlier the diagnosis, the better the prognosis. The differential diagnosis of mild AD or mild cognitive impairment (MCI) as well as DLB has been studied using PET and MRI as part of the NIH’s Alzheimer disease Neuroimaging initiative (ADNI). At present, many countries are participating in the ADNI, which is yielding promising results. This chapter’s study will improve the development of new drugs for the treatment of dementia patients by enabling the evaluation of the effect and efficacy of those drugs.

scholarly journals Az Alzheimer-kór molekuláris képi megjelenítése

2019 ◽  
Vol 160 (33) ◽  
pp. 1289-1295 ◽  
Author(s):  
Annamária Albert ◽  
Katalin Borbély
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Morphological Changes ◽  
Emission Tomography ◽  
Clinical Form ◽  
Pittsburgh Compound B ◽  
Metabolic Pattern ◽  
Functional Changes ◽  
Positron Emission ◽  
Β Amyloid

Abstract: The ever-growing average age of the society significantly increases the occurrence of Alzheimer’s disease. The increased prevalence represents considerable social and economic burden, which urges the development of diagnostic and therapeutic methods in the field. The most common cause of dementia is Alzheimer’s disease, the typical histopathological abnormality of which are well known. The detection of functional changes results in the early diagnosis of the disease, which precedes the morphological changes by years. Positron-emission tomography plays an important role in the demonstration of metabolic changes. The glucose metabolic pattern differs significantly in each clinical form of dementia. The most important β-amyloid-binding radiopharmaceuticals that should be highlighted are [11C]Pittsburgh compound B that is widely used in the research and [18F]florbetapir that is commonly approved in diagnostics. Tracers visualising neurofibrillary tangles consisting of tau protein appeared most recently. The development continues; newer and newer radiopharmaceuticals appear. These tracers play an important role in both the research and the diagnostics. Orv Hetil. 2019; 160(33): 1289–1295.

scholarly journals Frontal Behavior Syndromes in Idiopathic Normal Pressure Hydrocephalus as a Function of Alzheimer’s Disease Biomarker Status

2020 ◽  
Vol 26 (9) ◽  
pp. 883-893
Author(s):  
Madison Niermeyer ◽  
Chad Gaudet ◽  
Paul Malloy ◽  
Irene Piryatinsky ◽  
Stephen Salloway ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Normal Pressure Hydrocephalus ◽  
Neuropsychiatric Symptoms ◽  
Executive Dysfunction ◽  
Normal Pressure ◽  
Idiopathic Normal Pressure Hydrocephalus ◽  
Future Research ◽  
Positron Emission ◽  
Neuropsychiatric Manifestations

AbstractObjectives:Cognitive impairment and apathy are well-documented features of idiopathic normal pressure hydrocephalus (iNPH). However, research examining other neuropsychiatric manifestations of iNPH is scant, and it is unknown whether the neuropsychiatric presentation differs for iNPH patients with comorbid Alzheimer’s disease (AD) versus iNPH without AD. This study aims to advance our understanding of neuropsychiatric syndromes associated with iNPH.Methods:Fifty patients from Butler Hospital’s Normal Pressure Hydrocephalus Clinic met inclusion criteria. Caregiver ratings on the Frontal Systems Behavior Scale (FrSBe) were examined to appraise changes in apathy, executive dysfunction, and disinhibition. Patients also completed cognitive tests of global cognition, psychomotor speed, and executive functioning. AD biomarker status was determined by either amyloid-beta (Aβ) positron emission tomography (PET) imaging or cerebrospinal fluid (CSF) total tau to Aβ-42 ratio.Results:Results revealed clinically significant elevations on the FrSBe’s apathy and executive dysfunction scales and modest correlations among these scales and cognitive measures. Of the 44 patients with available neuroimaging or CSF draw data, 14 presented with comorbid AD. Relative to the iNPH-only group, the iNPH + AD group showed a larger increase from pre-illness to current informant ratings on the executive dysfunction scale, but not the apathy or disinhibition scales.Conclusions:These results replicate and extend prior research by identifying apathy and executive dysfunction as prominent neuropsychiatric symptoms of iNPH and suggest comorbid AD exacerbates dysexecutive behaviors. Future research is warranted to examine the effects of comorbid AD pathology in response to shunt surgery for iNPH, neuropsychiatric symptom changes, and resultant caregiver burden.

A Review of Automated Techniques for Assisting the Early Detection of Alzheimer’s Disease with a Focus on EEG

2021 ◽  
Vol 80 (4) ◽  
pp. 1363-1376
Author(s):  
Eduardo Perez-Valero ◽  
Miguel A. Lopez-Gordo ◽  
Christian Morillas ◽  
Francisco Pelayo ◽  
Miguel A. Vaquero-Blasco
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Early Detection ◽  
Performance Metrics ◽  
Automatic Detection ◽  
Classification Model ◽  
Future Research ◽  
Positron Emission ◽  
Brain Data ◽  
Review State

In this paper, we review state-of-the-art approaches that apply signal processing (SP) and machine learning (ML) to automate the detection of Alzheimer’s disease (AD) and its prodromal stages. In the first part of the document, we describe the economic and social implications of the disease, traditional diagnosis techniques, and the fundaments of automated AD detection. Then, we present electroencephalography (EEG) as an appropriate alternative for the early detection of AD, owing to its reduced cost, portability, and non-invasiveness. We also describe the main time and frequency domain EEG features that are employed in AD detection. Subsequently, we examine some of the main studies of the last decade that aim to provide an automatic detection of AD and its previous stages by means of SP and ML. In these studies, brain data was acquired using multiple medical techniques such as magnetic resonance imaging, positron emission tomography, and EEG. The main aspects of each approach, namely feature extraction, classification model, validation approach, and performance metrics, are compiled and discussed. Lastly, a set of conclusions and recommendations for future research on AD automatic detection are drawn in the final section of the paper.

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