scholarly journals Diffusion MRI Metrics and their Relation to Dementia Severity: Effects of Harmonization Approaches

2021 ◽  
Author(s):  
Sophia I Thomopoulos ◽  
Talia M Nir ◽  
Julio E Villalon-Reina ◽  
Artemis Zavaliangos-Petropulu ◽  
Piyush Maiti ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Strongly Correlated ◽  
Clinical Dementia Rating ◽  
Diffusion Weighted ◽  
Novel Drugs ◽  
Clinical Impairment ◽  
Amyloid Load ◽  
Weighted Magnetic Resonance Imaging ◽  
Clinical Measures

Diffusion-weighted magnetic resonance imaging (dMRI) is sensitive to microstructural changes in the brain that occur with normal aging and Alzheimer's disease (AD). There is much interest in which dMRI measures are most strongly correlated with clinical measures of AD severity, such as the clinical dementia rating (CDR), and biological processes that may be disrupted in AD, such as brain amyloid load measured using PET. Of these processes, some can be targeted using novel drugs. Since 2016, the Alzheimer's Disease Neuroimaging Initiative (ADNI) has collected dMRI data from three scanner manufacturers across 58 sites using 7 different protocols that vary in angular resolution, scan duration, and in the number and distribution of diffusion-weighted gradients. Here, we assessed dMRI data from 730 of those individuals (447 cognitively normal controls, 214 with mild cognitive impairment, 69 with dementia; age: 74.1±7.9 years; 381 female/349 male). To harmonize data from different protocols, we applied ComBat, ComBat-GAM, and CovBat to dMRI metrics from 28 white matter regions of interest. We ranked all dMRI metrics in order of the strength of clinically relevant associations, and assessed how this depended on the harmonization methods employed. dMRI metrics were associated with age and clinical impairment, but also with amyloid positivity. All harmonization methods gave comparable results while enabling data integration across multiple scanners and protocols.

scholarly journals Diffusion MRI Indices and their Relation to Cognitive Impairment in Brain Aging: The updated multi-protocol approach in ADNI3

2018 ◽  
Author(s):  
Artemis Zavaliangos-Petropulu ◽  
Talia M. Nir ◽  
Sophia I. Thomopoulos ◽  
Robert I. Reid ◽  
Matt A. Bernstein ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
White Matter ◽  
Rating Scale ◽  
Brain Aging ◽  
Screening Tools ◽  
Disease Assessment ◽  
Clinical Impairment ◽  
Clinical Measures

AbstractBrain imaging with diffusion-weighted MRI (dMRI) is sensitive to microstructural white matter changes associated with brain aging and neurodegeneration. In its third phase, the Alzheimer’s Disease Neuroimaging Initiative (ADNI3) is collecting data across multiple sites and scanners using different dMRI acquisition protocols, to better understand disease effects. It is vital to understand when data can be pooled across scanners, and how the choice of dMRI protocol affects the sensitivity of extracted measures to differences in clinical impairment. Here, we analyzed ADNI3 data from 317 participants (mean age: 75.4±7.9 years; 143 men/174 women), who were each scanned at one of 47 sites with one of six dMRI protocols using scanners from three different manufacturers. We computed four standard diffusion tensor imaging (DTI) indices including fractional anisotropy (FADTI) and mean, radial, and axial diffusivity, and one FA index based on the tensor distribution function (FATDF), in 24 bilaterally averaged white matter regions of interest. We found that protocol differences significantly affected dMRI indices, in particular FADTI. We ranked the diffusion indices for their strength of association with four clinical assessments. In addition to diagnosis, we evaluated cognitive impairment as indexed by three commonly used screening tools for detecting dementia and Alzheimer’s disease: the Alzheimer’s Disease Assessment Scale (ADAS-cog), the Mini-Mental State Examination (MMSE), and the Clinical Dementia Rating scale sum-of-boxes (CDR-sob). Using a nested random-effects model to account for protocol and site, we found that across all dMRI indices and clinical measures, the hippocampal-cingulum and fornix (crus) / stria terminalis regions most consistently showed strong associations with clinical impairment. Overall, the greatest effect sizes were detected in the hippocampal-cingulum and uncinate fasciculus for associations between axial or mean diffusivity and CDR-sob. FATDF detected robust widespread associations with clinical measures, while FADTI was the weakest of the five indices for detecting associations. Ultimately, we were able to successfully pool dMRI data from multiple acquisition protocols from ADNI3 and detect consistent and robust associations with clinical impairment and age.

scholarly journals The Free and Cued Selective Reminding Test Predicts Braak Stage

2021 ◽  
pp. 1-9
Author(s):  
Ellen Grober ◽  
Qi Qi ◽  
Lynn Kuo ◽  
Jason Hassenstab ◽  
Richard J. Perrin ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Free Recall ◽  
Rating Scale ◽  
Stage Iv ◽  
Braak Stage ◽  
Clinical Dementia Rating ◽  
Selective Reminding ◽  
Selective Reminding Test ◽  
Clinical Measures

Background: The ultimate validation of a clinical marker for Alzheimer’s disease (AD) is its association with AD neuropathology. Objective: To identify clinical measures that predict pathology, we evaluated the relationships of the picture version of the Free and Cued Selective Reminding Test (pFCSRT + IR), the Mini-Mental State Exam (MMSE), and the Clinical Dementia Rating scale Sum of Boxes (CDR-SB) to Braak stage. Methods: 315 cases from the clinicopathologic series at the Knight Alzheimer’s Disease Research Center were classified according to Braak stage. Boxplots of each predictor were compared to identify the earliest stage at which decline was observed and ordinal logistic regression was used to predict Braak stage. Results: Looking at the assessment closest to death, free recall scores were lower in individuals at Braak stage III versus Braak stages 0 and I (combined) while MMSE and CDR scores for individuals did not differ from Braak stages 0/I until Braak stage IV. The sum of free recall and total recall scores independently predicted Braak stage and had higher predictive validity than MMSE and CDR-SB in models including all three. Conclusion: pFCSRT + IR + IR scores may be more sensitive to early pathological changes than either the CDR-SB or the MMSE.

scholarly journals Speech and orofacial apraxias in Alzheimer's disease

2013 ◽  
Vol 25 (10) ◽  
pp. 1679-1685 ◽  
Author(s):  
Maysa Luchesi Cera ◽  
Karin Zazo Ortiz ◽  
Paulo Henrique Ferreira Bertolucci ◽  
Thaís Soares Cianciarullo Minett
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Oral Communication ◽  
Mean Difference ◽  
Strongly Correlated ◽  
Women Patients ◽  
Clinical Dementia Rating ◽  
The Mean ◽  
State Examination ◽  
Normal Controls

ABSTRACTBackground:Alzheimer's disease (AD) affects not only memory but also other cognitive functions, such as orientation, language, praxis, attention, visual perception, or executive function. Most studies on oral communication in AD focus on aphasia; however, speech and orofacial apraxias are also present in these patients. The aim of this study was to investigate the presence of speech and orofacial apraxias in patients with AD with the hypothesis that apraxia severity is strongly correlated with disease severity.Methods:Ninety participants in different stages of AD (mild, moderate, and severe) underwent the following assessments: Clinical Dementia Rating, Mini-Mental State Examination, Lawton Instrumental Activities of Daily Living, a specific speech and orofacial praxis assessment, and the oral agility subtest of the Boston diagnostic aphasia examination.Results:The mean age was 80.2±7.2 years and 73% were women. Patients with AD had significantly lower scores than normal controls for speech praxis (mean difference=−2.9, 95% confidence interval (CI)=−3.3 to −2.4) and orofacial praxis (mean difference=−4.9, 95% CI=−5.4 to −4.3). Dementia severity was significantly associated with orofacial apraxia severity (moderate AD: β=−19.63, p=0.011; and severe AD: β=−51.68, p < 0.001) and speech apraxia severity (moderate AD: β=7.07, p = 0.001; and severe AD: β= 8.16, p < 0.001).Conclusion:Speech and orofacial apraxias were evident in patients with AD and became more pronounced with disease progression.

Phytochemicals for drug discovery in Alzheimer’s disease: In silico Advances

2020 ◽  
Vol 26 ◽  
Author(s):  
Smriti Sharma ◽  
Vinayak Bhatia
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Drug Design ◽  
In Silico ◽  
Drug Targets ◽  
Development Process ◽  
Phytochemical Analysis ◽  
Computational Drug Design ◽  
Novel Drugs ◽  
Game Changer

: The search for novel drugs that can prevent or control Alzheimer’s disease has attracted lot of attention from researchers across the globe. Phytochemicals are increasingly being used to provide scaffolds to design drugs for AD. In silico techniques, have proven to be a game-changer in this drug design and development process. In this review, the authors have focussed on current advances in the field of in silico medicine, applied to phytochemicals, to discover novel drugs to prevent or cure AD. After giving a brief context of the etiology and available drug targets for AD, authors have discussed the latest advances and techniques in computational drug design of AD from phytochemicals. Some of the prototypical studies in this area are discussed in detail. In silico phytochemical analysis is a tool of choice for researchers all across the globe and helps integrate chemical biology with drug design.

scholarly journals A multilayer multimodal detection and prediction model based on explainable artificial intelligence for Alzheimer’s disease

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Shaker El-Sappagh ◽  
Jose M. Alonso ◽  
S. M. Riazul Islam ◽  
Ahmad M. Sultan ◽  
Kyung Sup Kwak
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cross Validation ◽  
Disease Risk ◽  
Binary Classification ◽  
Fuzzy Rule ◽  
Large Set ◽  
Detection Model ◽  
Multi Class Classification ◽  
Clinical Measures

AbstractAlzheimer’s disease (AD) is the most common type of dementia. Its diagnosis and progression detection have been intensively studied. Nevertheless, research studies often have little effect on clinical practice mainly due to the following reasons: (1) Most studies depend mainly on a single modality, especially neuroimaging; (2) diagnosis and progression detection are usually studied separately as two independent problems; and (3) current studies concentrate mainly on optimizing the performance of complex machine learning models, while disregarding their explainability. As a result, physicians struggle to interpret these models, and feel it is hard to trust them. In this paper, we carefully develop an accurate and interpretable AD diagnosis and progression detection model. This model provides physicians with accurate decisions along with a set of explanations for every decision. Specifically, the model integrates 11 modalities of 1048 subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) real-world dataset: 294 cognitively normal, 254 stable mild cognitive impairment (MCI), 232 progressive MCI, and 268 AD. It is actually a two-layer model with random forest (RF) as classifier algorithm. In the first layer, the model carries out a multi-class classification for the early diagnosis of AD patients. In the second layer, the model applies binary classification to detect possible MCI-to-AD progression within three years from a baseline diagnosis. The performance of the model is optimized with key markers selected from a large set of biological and clinical measures. Regarding explainability, we provide, for each layer, global and instance-based explanations of the RF classifier by using the SHapley Additive exPlanations (SHAP) feature attribution framework. In addition, we implement 22 explainers based on decision trees and fuzzy rule-based systems to provide complementary justifications for every RF decision in each layer. Furthermore, these explanations are represented in natural language form to help physicians understand the predictions. The designed model achieves a cross-validation accuracy of 93.95% and an F1-score of 93.94% in the first layer, while it achieves a cross-validation accuracy of 87.08% and an F1-Score of 87.09% in the second layer. The resulting system is not only accurate, but also trustworthy, accountable, and medically applicable, thanks to the provided explanations which are broadly consistent with each other and with the AD medical literature. The proposed system can help to enhance the clinical understanding of AD diagnosis and progression processes by providing detailed insights into the effect of different modalities on the disease risk.

scholarly journals Effects of optogenetic stimulation of basal forebrain parvalbumin neurons on Alzheimer’s disease pathology

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Caroline A. Wilson ◽  
Sarah Fouda ◽  
Shuzo Sakata
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Basal Forebrain ◽  
Gamma Oscillations ◽  
Cortical Region ◽  
Sensory Stimuli ◽  
Beneficial Effects ◽  
Amyloid Load ◽  
5Xfad Mice ◽  
Frontal Cortical Region

Abstract Neuronal activity can modify Alzheimer’s disease pathology. Overexcitation of neurons can facilitate disease progression whereas the induction of cortical gamma oscillations can reduce amyloid load and improve cognitive functions in mouse models. Although previous studies have induced cortical gamma oscillations by either optogenetic activation of cortical parvalbumin-positive (PV+) neurons or sensory stimuli, it is still unclear whether other approaches to induce gamma oscillations can also be beneficial. Here we show that optogenetic activation of PV+ neurons in the basal forebrain (BF) increases amyloid burden, rather than reducing it. We applied 40 Hz optical stimulation in the BF by expressing channelrhodopsin-2 (ChR2) in PV+ neurons of 5xFAD mice. After 1-h induction of cortical gamma oscillations over three days, we observed the increase in the concentration of amyloid-β42 in the frontal cortical region, but not amyloid-β40. Amyloid plaques were accumulated more in the medial prefrontal cortex and the septal nuclei, both of which are targets of BF PV+ neurons. These results suggest that beneficial effects of cortical gamma oscillations on Alzheimer’s disease pathology can depend on the induction mechanisms of cortical gamma oscillations.

scholarly journals Autoregulation of Cerebral Blood Flow to Changes in Arterial Pressure in Mild Alzheimer's Disease

2010 ◽  
Vol 30 (11) ◽  
pp. 1883-1889 ◽  
Author(s):  
Allyson R Zazulia ◽  
Tom O Videen ◽  
John C Morris ◽  
William J Powers
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Blood Flow ◽  
Cerebral Blood Flow ◽  
Arterial Pressure ◽  
The Elderly ◽  
Local Defect ◽  
Clinical Dementia Rating ◽  
Positron Emission ◽  
Before And After

Studies in transgenic mice overexpressing amyloid precursor protein (APP) demonstrate impaired autoregulation of cerebral blood flow (CBF) to changes in arterial pressure and suggest that cerebrovascular dysfunction may be critically important in the development of pathological Alzheimer's disease (AD). Given the relevance of such a finding for guiding hypertension treatment in the elderly, we assessed autoregulation in individuals with AD. Twenty persons aged 75±6 years with very mild or mild symptomatic AD (Clinical Dementia Rating 0.5 or 1.0) underwent 15O-positron emission tomography (PET) CBF measurements before and after mean arterial pressure (MAP) was lowered from 107±13 to 92±9 mm Hg with intravenous nicardipine; 11C-PIB-PET imaging and magnetic resonance imaging (MRI) were also obtained. There were no significant differences in mean CBF before and after MAP reduction in the bilateral hemispheres (−0.9±5.2 mL per 100 g per minute, P=0.4, 95% confidence interval (CI)=−3.4 to 1.5), cortical borderzones (−1.9±5.0 mL per 100 g per minute, P=0.10, 95% CI=−4.3 to 0.4), regions of T2W-MRI-defined leukoaraiosis (−0.3±4.4 mL per 100 g per minute, P=0.85, 95% CI=−3.3 to 3.9), or regions of peak 11C-PIB uptake (−2.5±7.7 mL per 100 g per minute, P=0.30, 95% CI=−7.7 to 2.7). The absence of significant change in CBF with a 10 to 15 mm Hg reduction in MAP within the normal autoregulatory range demonstrates that there is neither a generalized nor local defect of autoregulation in AD.

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