Multimodal and Multiscale Deep Neural Networks for the Early Diagnosis of Alzheimer's Disease using structural MR and FDG-PET images.

Alzheimer's Disease (AD) is a progressive neurodegenerative disease where biomarkers for disease based on pathophysiology may be able to provide objective measures for disease diagnosis and staging. Neuroimaging scans acquired from MRI and metabolism images obtained by FDG-PET provide in-vivo measurements of structure and function (glucose metabolism) in a living brain. It is hypothesized that combining multiple different image modalities providing complementary information could help improve early diagnosis of AD. In this paper, we propose a novel deep-learning-based framework to discriminate individuals with AD utilizing a multimodal and multiscale deep neural network. Our method delivers 82.4% accuracy in identifying the individuals with mild cognitive impairment (MCI) who will convert to AD at 3 years prior to conversion (86.4% combined accuracy for conversion within 1-3 years), a 94.23% sensitivity in classifying individuals with clinical diagnosis of probable AD, and a 86.3% specificity in classifying non-demented controls improving upon results in published literature

Predicting Alzheimer’s disease progression using multi-modal deep learning approach

Alzheimer’s disease (AD) is a progressive neurodegenerative condition marked by a decline in cognitive functions with no validated disease modifying treatment. It is critical for timely treatment to detect AD in its earlier stage before clinical manifestation. Mild cognitive impairment (MCI) is an intermediate stage between cognitively normal older adults and AD. To predict conversion from MCI to probable AD, we applied a deep learning approach, multimodal recurrent neural network. We developed an integrative framework that combines not only cross-sectional neuroimaging biomarkers at baseline but also longitudinal cerebrospinal fluid (CSF) and cognitive performance biomarkers obtained from the Alzheimer’s Disease Neuroimaging Initiative cohort (ADNI). The proposed framework integrated longitudinal multi-domain data. Our results showed that 1) our prediction model for MCI conversion to AD yielded up to 75% accuracy (area under the curve (AUC) = 0.83) when using only single modality of data separately; and 2) our prediction model achieved the best performance with 81% accuracy (AUC = 0.86) when incorporating longitudinal multi-domain data. A multi-modal deep learning approach has potential to identify persons at risk of developing AD who might benefit most from a clinical trial or as a stratification approach within clinical trials

An IL1RL1 genetic variant lowers soluble ST2 levels and the risk effects of APOE-ε4 in female patients with Alzheimer’s disease

Changes in the levels of circulating proteins are associated with Alzheimer’s disease (AD), whereas their pathogenic roles in AD are unclear. Here, we identified soluble ST2 (sST2), a decoy receptor of interleukin-33–ST2 signaling, as a new disease-causing factor in AD. Increased circulating sST2 level is associated with more severe pathological changes in female individuals with AD. Genome-wide association analysis and CRISPR–Cas9 genome editing identified rs1921622, a genetic variant in an enhancer element of IL1RL1, which downregulates gene and protein levels of sST2. Mendelian randomization analysis using genetic variants, including rs1921622, demonstrated that decreased sST2 levels lower AD risk and related endophenotypes in females carrying the Apolipoprotein E (APOE)-ε4 genotype; the association is stronger in Chinese than in European-descent populations. Human and mouse transcriptome and immunohistochemical studies showed that rs1921622/sST2 regulates amyloid-beta (Aβ) pathology through the modulation of microglial activation and Aβ clearance. These findings demonstrate how sST2 level is modulated by a genetic variation and plays a disease-causing role in females with AD

Hierarchical Two-Stage Cost-Sensitive Clinical Decision Support System for Screening Prodromal Alzheimer's Disease and Related Dementias

Background: The detection of subtle cognitive impairment in a clinical setting is difficult. Because time is a key factor in small clinics and research sites, the brief cognitive assessments that are relied upon often misclassify patients with very mild impairment as normal. Objective: In this study, we seek to identify a parsimonious screening tool in one stage, followed by additional assessments in an optional second stage if additional specificity is desired, tested using a machine learning algorithm capable of being integrated into a clinical decision support system. Methods: The best primary stage incorporated measures of short-term memory, executive and visuospatial functioning, and self-reported memory and daily living questions, with a total time of 5 minutes. The best secondary stage incorporated a measure of neurobiology as well as additional cognitive assessment and brief informant report questionnaires, totaling 30 minutes including delayed recall. Combined performance was evaluated using 25 sets of models, trained on 1,181 ADNI participants and tested on 127 patients from a memory clinic. Results: The 5-minute primary stage was highly sensitive (96.5%) but lacked specificity (34.1%), with an AUC of 87.5% and diagnostic odds ratio of 14.3. The optional secondary stage increased specificity to 58.6%, resulting in an overall AUC of 89.7% using the best model combination of logistic regression and gradient-boosted machine. Conclusion: The primary stage is brief and effective at screening, with the optional two-stage technique further increasing specificity. The hierarchical two-stage technique exhibited similar accuracy but with reduced costs compared to the more common single-stage paradigm

The Mediating Role of Inflammation in the Relationship Between alpha-Synuclein and Cognitive Functioning

Accumulating evidence suggests that alpha-synuclein plays a role in the pathophysiology of Alzheimer's disease (AD). This study examined whether alpha-synuclein level in cerebrospinal fluid (CSF) was associated with cognitive functioning among older adults. We also explored whether this relationship was mediated by proinflammatory cytokines TNF-alpha and IL-6, along with sIL-6R and vascular endothelial growth factor (VEGF).Using a cross-sectional Alzheimer's Disease Neuroimaging Initiative (ADNI; N = 148) sample, we examined the relationship between alpha-synuclein and participants' performance on Mini-Mental State Examination (MMSE) and Alzheimer's Disease Assessment Scale Cognitive Subscale (ADAS-Cog 13) at baseline. Mediation analyses were utilized, adjusting for age, education, APOEe4, and Geriatric Depression Scale scores. All biological markers were measured in CSF. Participants in the current sample were 58.3% males, 41.7% females, and Caucasian (95.5%); their average education and age were 15.5 (standard deviation [SD] = 2.97) and 74.4 (SD = 7.51) years, respectively. Higher accumulation of alpha-synuclein was associated with poorer MMSE scores (beta = -0.41, standard error [SE] = 1.54, p < .001). This relationship appeared to be mediated by VEGF (beta = 0.27, SE = 2.15, p = .025) and IL-6r (beta = 0.22, SE = 1.66, p < .026). In addition, alpha-synuclein was associated with poorer performance on the ADAS-Cog 13 (beta = 0.34, p = .005) and mediated by VEGF (beta = -0.19, SE = 4.13, p = .025) after adjusting for age, education, APOEe4, and depressive symptoms. alpha-Synuclein may serve as an additional biomarker for determining poor cognitive functioning. VEGF and IL-6 soluble receptors were significant mediators of the relationship between alpha-synuclein and cognitive functioning. If confirmed in prospective analyses, these findings can further inform the pathologic cascade and early diagnosis of AD

Tau‐Atrophy Variability Reveals Phenotypic Heterogeneity in Alzheimer's Disease

ObjectiveTau neurofibrillary tangles (T) are the primary driver of downstream neurodegeneration (N) and subsequent cognitive impairment in Alzheimer's disease (AD). However, there is substantial variability in the T-N relationship - manifested in higher or lower atrophy than expected for level of tau in a given brain region. The goal of this study was to determine if region-based quantitation of this variability allows for identification of underlying modulatory factors, including polypathology.MethodsCortical thickness (N) and 18 F-Flortaucipir SUVR (T) were computed in 104 gray matter regions from a cohort of cognitively-impaired, amyloid-positive (A+) individuals. Region-specific residuals from a robust linear fit between SUVR and cortical thickness were computed as a surrogate for T-N mismatch. A summary T-N mismatch metric defined using residuals were correlated with demographic and imaging-based modulatory factors, and to partition the cohort into data-driven subgroups.ResultsThe summary T-N mismatch metric correlated with underlying factors such as age and burden of white matter hyperintensity lesions. Data-driven subgroups based on clustering of residuals appear to represent different biologically relevant phenotypes, with groups showing distinct spatial patterns of higher or lower atrophy than expected.InterpretationThese data support the notion that a measure of deviation from a normative relationship between tau burden and neurodegeneration across brain regions in individuals on the AD continuum captures variability due to multiple underlying factors, and can reveal phenotypes, which if validated, may help identify possible contributors to neurodegeneration in addition to tau, which may ultimately be useful for cohort selection in clinical trials. ANN NEUROL 2021;90:751-762

Association Between Anticholinergic Medication Use and Cognition, Brain Metabolism, and Brain Atrophy in Cognitively Normal Older Adults.

ImportanceThe use of anticholinergic (AC) medication is linked to cognitive impairment and an increased risk of dementia. To our knowledge, this is the first study to investigate the association between AC medication use and neuroimaging biomarkers of brain metabolism and atrophy as a proxy for understanding the underlying biology of the clinical effects of AC medications.ObjectiveTo assess the association between AC medication use and cognition, glucose metabolism, and brain atrophy in cognitively normal older adults from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and the Indiana Memory and Aging Study (IMAS).Design, setting, and participantsThe ADNI and IMAS are longitudinal studies with cognitive, neuroimaging, and other data collected at regular intervals in clinical and academic research settings. For the participants in the ADNI, visits are repeated 3, 6, and 12 months after the baseline visit and then annually. For the participants in the IMAS, visits are repeated every 18 months after the baseline visit (402 cognitively normal older adults in the ADNI and 49 cognitively normal older adults in the IMAS were included in the present analysis). Participants were either taking (hereafter referred to as the AC+ participants [52 from the ADNI and 8 from the IMAS]) or not taking (hereafter referred to as the AC- participants [350 from the ADNI and 41 from the IMAS]) at least 1 medication with medium or high AC activity. Data analysis for this study was performed in November 2015.Main outcomes and measuresCognitive scores, mean fludeoxyglucose F 18 standardized uptake value ratio (participants from the ADNI only), and brain atrophy measures from structural magnetic resonance imaging were compared between AC+ participants and AC- participants after adjusting for potential confounders. The total AC burden score was calculated and was related to target measures. The association of AC use and longitudinal clinical decline (mean [SD] follow-up period, 32.1 [24.7] months [range, 6-108 months]) was examined using Cox regression.ResultsThe 52 AC+ participants (mean [SD] age, 73.3 [6.6] years) from the ADNI showed lower mean scores on Weschler Memory Scale-Revised Logical Memory Immediate Recall (raw mean scores: 13.27 for AC+ participants and 14.16 for AC- participants; P = .04) and the Trail Making Test Part B (raw mean scores: 97.85 seconds for AC+ participants and 82.61 seconds for AC- participants; P = .04) and a lower executive function composite score (raw mean scores: 0.58 for AC+ participants and 0.78 for AC- participants; P = .04) than the 350 AC- participants (mean [SD] age, 73.3 [5.8] years) from the ADNI. Reduced total cortical volume and temporal lobe cortical thickness and greater lateral ventricle and inferior lateral ventricle volumes were seen in the AC+ participants relative to the AC- participants.Conclusions and relevanceThe use of AC medication was associated with increased brain atrophy and dysfunction and clinical decline. Thus, use of AC medication among older adults should likely be discouraged if alternative therapies are available

Cortical atrophy in patients with cerebral amyloid angiopathy: a case-control study

Paroxysmal Cerebral Disorder

Cerebral Microbleeds, Cerebral Amyloid Angiopathy, and Their Relationships to Quantitative Markers of Neurodegeneration

10.1212/WNL.0000000000200142NEUROLOGY9816E1605-E161