Mild Cognitive Impairment Conversion Research Articles

Paired plasma lipidomics and proteomics analysis in the conversion from mild cognitive impairment to Alzheimer's disease

BackgroundAlzheimer's disease (AD) is a neurodegenerative condition for which there is currently no available medication that can stop its progression. Previous studies suggest that mild cognitive impairment (MCI) is a phase that precedes the disease. Therefore, a better understanding of the molecular mechanisms behind MCI conversion to AD is needed. MethodHere, we propose a machine learning-based approach to detect the key metabolites and proteins involved in MCI progression to AD using data from the European Medical Information Framework for Alzheimer's Disease Multimodal Biomarker Discovery Study. Proteins and metabolites were evaluated separately in multiclass models (controls, MCI and AD) and together in MCI conversion models (MCI stable vs converter). Only features selected as relevant by 3/4 algorithms proposed were kept for downstream analysis. ResultsMulticlass models of metabolites highlighted nine features further validated in an independent cohort (0.726 mean balanced accuracy). Among these features, one metabolite, oleamide, was selected by all the algorithms. Further in-vitro experiments in rodents showed that disease-associated microglia excreted oleamide in vesicles. Multiclass models of proteins stood out with nine features, validated in an independent cohort (0.720 mean balanced accuracy). However, none of the proteins was selected by all the algorithms. Besides, to distinguish between MCI stable and converters, 14 key features were selected (0.872 AUC), including tTau, alpha-synuclein (SNCA), junctophilin-3 (JPH3), properdin (CFP) and peptidase inhibitor 15 (PI15) among others. ConclusionsThis omics integration approach highlighted a set of molecules associated with MCI conversion important in neuronal and glia inflammation pathways.

Sulcal Morphometry Predicts Mild Cognitive Impairment Conversion to Alzheimer's Disease.

Being able to differentiate mild cognitive impairment (MCI) patients who would eventually convert (MCIc) to Alzheimer's disease (AD) from those who would not (MCInc) is a key challenge for prognosis. This study aimed to investigate the ability of sulcal morphometry to predict MCI progression to AD, dedicating special attention to an accurate identification of sulci. Twenty-five AD patients, thirty-seven MCI and twenty-five healthy controls (HC) underwent a brain-MR protocol (1.5T scanner) including a high-resolution T1-weighted sequence. MCI patients underwent a neuropsychological assessment at baseline and were clinically re-evaluated after a mean of 2.3 years. At follow-up, 12 MCI were classified as MCInc and 25 as MCIc. Sulcal morphometry was investigated using the BrainVISA framework. Consistency of sulci across subjects was ensured by visual inspection and manual correction of the automatic labelling in each subject. Sulcal surface, depth, length, and width were retrieved from 106 sulci. Features were compared across groups and their classification accuracy in predicting MCI conversion was tested. Potential relationships between sulcal features and cognitive scores were explored using Spearman's correlation. The width of sulci in the temporo-occipital region strongly differentiated between each pair of groups. Comparing MCIc and MCInc, the width of several sulci in the bilateral temporo-occipital and left frontal areas was significantly altered. Higher width of frontal sulci was associated with worse performances in short-term verbal memory and phonemic fluency. Sulcal morphometry emerged as a strong tool for differentiating HC, MCI, and AD, demonstrating its potential prognostic value for the MCI population.

A generalizable data-driven model of atrophy heterogeneity and progression in memory clinic settings.

Memory clinic patients are a heterogeneous population representing various aetiologies of pathological aging. It is unknown if divergent spatiotemporal progression patterns of brain atrophy, as previously described in Alzheimer's disease (AD) patients, are prevalent and clinically meaningful in this group of older adults. To uncover distinct atrophy subtypes, we applied the Subtype and Stage Inference (SuStaIn) algorithm to baseline structural MRI data from 813 participants enrolled in the DELCODE cohort (mean ± SD age = 70.67 ± 6.07 years, 52% females). Participants were cognitively unimpaired (CU; n = 285) or fulfilled diagnostic criteria for subjective cognitive decline (SCD; n = 342), mild cognitive impairment (MCI; n = 118), or dementia of the Alzheimer's type (n = 68). Atrophy subtypes were compared in baseline demographics, fluid AD biomarker levels, the Preclinical Alzheimer Cognitive Composite (PACC-5), as well as episodic memory and executive functioning. PACC-5 trajectories over up to 240 weeks were examined. To test if baseline atrophy subtype and stage predicted clinical trajectories before manifest cognitive impairment, we analysed PACC-5 trajectories and MCI conversion rates of CU and SCD participants. Limbic-predominant and hippocampal-sparing atrophy subtypes were identified. Limbic-predominant atrophy first affected the medial temporal lobes, followed by further temporal and, finally, the remaining cortical regions. At baseline, this subtype was related to older age, more pathological AD biomarker levels, APOE ε4 carriership, and an amnestic cognitive impairment. Hippocampal-sparing atrophy initially occurred outside the temporal lobe with the medial temporal lobe spared up to advanced atrophy stages. This atrophy pattern also affected individuals with positive AD biomarkers and was associated with more generalised cognitive impairment. Limbic-predominant atrophy, in all and in only unimpaired participants, was linked to more negative longitudinal PACC-5 slopes than observed in participants without or with hippocampal-sparing atrophy and increased the risk of MCI conversion. SuStaIn modelling was repeated in a sample from the Swedish BioFINDER-2 cohort. Highly similar atrophy progression patterns and associated cognitive profiles were identified. Cross-cohort model generalizability, both on the subject and group level, were excellent, indicating reliable performance in previously unseen data. The proposed model is a promising tool for capturing heterogeneity among older adults at early at-risk states for AD in applied settings. The implementation of atrophy subtype- and stage-specific end-points may increase the statistical power of pharmacological trials targeting early AD.

Clinical value of plasma ALZpath pTau217 immunoassay for assessing mild cognitive impairment

BackgroundAmong plasma biomarkers for Alzheimer’s disease (AD), pTau181 and pTau217 are the most promising. However, transition from research to routine clinical use will require confirmation of clinical performance in prospective...

Feature Importance Analysis and Machine Learning for Alzheimer's Disease Early Detection: Feature Fusion of the Hippocampus, Entorhinal Cortex, and Standardized Uptake Value Ratio.

Alzheimer's disease (AD) is a progressive neurological disorder characterized by mild memory loss and ranks as a leading cause of mortality in the USA, accounting for approximately 120,000 deaths per year. It is also the primary form of dementia. Early detection is critical for timely intervention as the neurodegenerative process often starts 15-20 years before cognitive symptoms manifest. This study focuses on determining feature importance in AD classification using fused texture features from 3D magnetic resonance imaging hippocampal and entorhinal cortex and standardized uptake value ratio (SUVR) derived from positron emission tomography (PET) images. To achieve this objective, we employed four distinct classifiers (Linear Support Vector Classification, Linear Discriminant Analysis, Logistic Regression, and Logistic Regression Classifier with Stochastic Gradient Descent Learning). These classifiers were used to derive both average and top-ranked importance scores for each feature based on their outputs. Our framework is designed to distinguish between two classes, AD-negative (or mild cognitive impairment stable [MCIs]) and AD-positive (or MCI conversion [MCIc]), using a probabilistic neural network classifier and the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The findings from the feature importance highlight the crucial role of the GLCM texture features extracted from the hippocampus and entorhinal cortex, demonstrating their superior performance compared to the volume and SUVR. GLCM texture AD classification achieved approximately 90% sensitivity in identifying MCIc cases while maintaining low false positives (below 30%) when fused with other features. Moreover, the receiver operating characteristic curves validate the GLCMs' superior performance in distinguishing between MCIs and MCIc. Additionally, fusing different types of features improved classification performance compared to relying solely on any single feature category. Our study emphasizes the pivotal role of GLCM texture features in early Alzheimer's detection.

The use of individual-based FDG-PET volume of interest in predicting conversion from mild cognitive impairment to dementia

BackgroundBased on a longitudinal cohort design, the aim of this study was to investigate whether individual-based 18F fluorodeoxyglucose positron emission tomography (18F-FDG-PET) regional signals can predict dementia conversion in patients with mild cognitive impairment (MCI).MethodsWe included 44 MCI converters (MCI-C), 38 non-converters (MCI-NC), 42 patients with Alzheimer’s disease with dementia, and 40 cognitively normal controls. Data from annual cognitive measurements, 3D T1 magnetic resonance imaging (MRI) scans, and 18F-FDG-PET scans were used for outcome analysis. An individual-based FDG-PET approach was applied using seven volumes of interest (VOIs), Z transformed using a normal FDG-PET template. Hypometabolism was defined as a Z score < -2 of regional standard uptake value ratio. For the longitudinal cognitive test scores, generalized estimating equations were used. A linear mixed-effects model was used to compare the temporal impact of cortical hypometabolism and cortical thickness degeneration.ResultsThe clinical follow-up period was 6.6 ± 3.8 years (range 3.1 to 16.0 years). The trend of cognitive decline could differentiate MCI-C from MCI-NC after 3 years of follow-up. In the baseline 18F-FDG-PET scan of the patients with MCI, medial temporal lobe (MTL; 94.7% sensitivity, 80.5% specificity) and posterior cingulate cortex (PCC; 89.5% sensitivity, 73.1% specificity) hypometabolism predicted conversion with high accuracy. 18F-FDG-PET hypometabolism preceded dementia conversion at an interval of 3.70 ± 1.68 years and was earlier than volumetric changes, with the exception of the MTL.ConclusionsOur finding supports the use of individual-based 18F-FDG-PET analysis to predict MCI conversion to dementia. Reduced FDG-PET metabolism in the MTL and PCC were strongly associated with future cognitive decline in the MCI-C group. Changes in 18F-FDG-PET occurred 1 to 8 years prior to conversion to dementia. Progressive hypometabolism in the PCC, precuneus and lateral temporal lobe, but not MTL, preceded MRI findings at the MCI stage.

Methylation risk score in peripheral blood predictive of conversion from mild cognitive impairment to Alzheimer's Disease

Background Alzheimer’s disease (AD) is a neurodegenerative and heterogeneous disorder with complex etiology. Mild cognitive impairment (MCI) may represent an intermediate stage of AD, and the ability to identify MCI patients at greater risk of conversion to AD could guide personalized treatments. This study sought to develop a methylation risk score predictive of conversion from MCI to AD using publicly available blood DNA methylation (DNAm) data. Methods Using blood DNA methylation data from an epigenome-wide association study of AD that included 111 subjects with MCI, a methylation risk score of MCI conversion was created using an elastic-net framework. The elastic-net model was trained with a high-variance subset of the DNAm data, age and sex as predictors. Results The final model included three CpG sites: SLC6A3 (cg09892121) and TRIM62 (cg25342005), with a third (cg17292662) near the genes ATP6V1H and RGS20. A significant difference (p &lt; 0.0001, t-test) was observed in the scores for MCI stable subjects compared with MCI converters. No statistically significant difference was observed between AD subjects and controls, suggesting specificity of the risk score for susceptibility to conversion. Conclusions The ability to identify MCI patients at greater risk of progression could inform early interventions and is a critical component in mitigation strategies for AD. This study provides insight into a potential role for epigenetics in the development of a multi-omic risk score of conversion.

Predicting long-term progression of Alzheimer’s disease using a multimodal deep learning model incorporating interaction effects

BackgroundIdentifying individuals with mild cognitive impairment (MCI) at risk of progressing to Alzheimer’s disease (AD) provides a unique opportunity for early interventions. Therefore, accurate and long-term prediction of the conversion from MCI to AD is desired but, to date, remains challenging. Here, we developed an interpretable deep learning model featuring a novel design that incorporates interaction effects and multimodality to improve the prediction accuracy and horizon for MCI-to-AD progression.MethodsThis multi-center, multi-cohort retrospective study collected structural magnetic resonance imaging (sMRI), clinical assessments, and genetic polymorphism data of 252 patients with MCI at baseline from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. Our deep learning model was cross-validated on the ADNI-1 and ADNI-2/GO cohorts and further generalized in the ongoing ADNI-3 cohort. We evaluated the model performance using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, and F1 score.ResultsOn the cross-validation set, our model achieved superior results for predicting MCI conversion within 4 years (AUC, 0.962; accuracy, 92.92%; sensitivity, 88.89%; specificity, 95.33%) compared to all existing studies. In the independent test, our model exhibited consistent performance with an AUC of 0.939 and an accuracy of 92.86%. Integrating interaction effects and multimodal data into the model significantly increased prediction accuracy by 4.76% (P = 0.01) and 4.29% (P = 0.03), respectively. Furthermore, our model demonstrated robustness to inter-center and inter-scanner variability, while generating interpretable predictions by quantifying the contribution of multimodal biomarkers.ConclusionsThe proposed deep learning model presents a novel perspective by combining interaction effects and multimodality, leading to more accurate and longer-term predictions of AD progression, which promises to improve pre-dementia patient care.

Atrophy of hippocampal subfields and amygdala nuclei in subjects with mild cognitive impairment progressing to Alzheimer's disease

The hippocampus and amygdala are the first brain regions to show early signs of Alzheimer's Disease (AD) pathology. AD is preceded by a prodromal stage known as Mild Cognitive Impairment (MCI), a crucial crossroad in the clinical progression of the disease. The topographical development of AD has been the subject of extended investigation. However, it is still largely unknown how the transition from MCI to AD affects specific hippocampal and amygdala subregions. The present study is set to answer that question. We analyzed data from 223 subjects: 75 healthy controls, 52 individuals with MCI, and 96 AD patients obtained from the ADNI. The MCI group was further divided into two subgroups depending on whether individuals in the 48 months following the diagnosis either remained stable (N = 21) or progressed to AD (N = 31). A MANCOVA test evaluated group differences in the volume of distinct amygdala and hippocampal subregions obtained from magnetic resonance images. Subsequently, a stepwise linear discriminant analysis (LDA) determined which combination of magnetic resonance imaging parameters was most effective in predicting the conversion from MCI to AD. The predictive performance was assessed through a Receiver Operating Characteristic analysis. AD patients displayed widespread subregional atrophy. MCI individuals who progressed to AD showed selective atrophy of the hippocampal subiculum and tail compared to stable MCI individuals, who were undistinguishable from healthy controls. Converter MCI showed atrophy of the amygdala's accessory basal, central, and cortical nuclei. The LDA identified the hippocampal subiculum and the amygdala's lateral and accessory basal nuclei as significant predictors of MCI conversion to AD. The analysis returned a sensitivity value of 0.78 and a specificity value of 0.62. These findings highlight the importance of targeted assessments of distinct amygdala and hippocampus subregions to help dissect the clinical and pathophysiological development of the MCI to AD transition.

Polygenic effects on the risk of Alzheimer's disease in the Japanese population.

Polygenic effects have been proposed to account for some disease phenotypes; these effects are calculated as a polygenic risk score (PRS). This score is correlated with Alzheimer's disease (AD)-related phenotypes, such as biomarker abnormalities and brain atrophy, and is associated with conversion from mild cognitive impairment (MCI) to AD. However, the AD PRS has been examined mainly in Europeans, and owing to differences in genetic structure and lifestyle, it is unclear whether the same relationships between the PRS and AD-related phenotypes exist in non-European populations. In this study, we calculated and evaluated the AD PRS in Japanese individuals using genome-wide association study (GWAS) statistics from Europeans. In this study, we calculated the AD PRS in 504 Japanese participants (145 cognitively unimpaired (CU) participants, 220 participants with late mild cognitive impairment (MCI), and 139 patients with mild AD dementia) enrolled in the Japanese Alzheimer's Disease Neuroimaging Initiative (J-ADNI) project. In order to evaluate the clinical value of this score, we (1) determined the polygenic effects on AD in the J-ADNI and validated it using two independent cohorts (a Japanese neuropathology (NP) cohort (n = 565) and the North American ADNI (NA-ADNI) cohort (n = 617)), (2) examined the AD-related phenotypes associated with the PRS, and (3) tested whether the PRS helps predict the conversion of MCI to AD. The PRS using 131 SNPs had an effect independent of APOE. The PRS differentiated between CU participants and AD patients with an area under the curve (AUC) of 0.755 when combined with the APOE variants. Similar AUC was obtained when PRS calculated by the NP and NA-ADNI cohorts was applied. In MCI patients, the PRS was associated with cerebrospinal fluid phosphorylated-tau levels (β estimate = 0.235, p value = 0.026). MCI with a high PRS showed a significantly increased conversion to AD in APOE ε4 noncarriers with a hazard rate of 2.22. In addition, we also developed a PRS model adjusted for LD and observed similar results. We showed that the AD PRS is useful in the Japanese population, whose genetic structure is different from that of the European population. These findings suggest that the polygenicity of AD is partially common across ethnic differences.

An effective and robust lattice Boltzmann model guided by atlas for hippocampal subregions segmentation.

Given the varying vulnerability of the rostral and caudal regions of the hippocampus to neuropathology in the Alzheimer's disease (AD) continuum, accurately assessing structural changes in these subregions is crucial for early AD detection. The development of reliable and robust automatic segmentation methods for hippocampal subregions (HS) is of utmostimportance. Our aim is to propose and validate a HS segmentation model that is both training-free and highly generalizable. This method should exhibit comparable accuracy and efficiency to state-of-the-art techniques. The segmented HS can serve as a biomarker for studying the progression ofAD. We utilized the functional magnetic resonance imaging of the Brain's Integrated Registration and Segmentation Tool (FIRST) to segment the entire hippocampus. By intersecting the segmentation results with the Brainnetome (BN) atlas, we obtained coarse segmentation of the four HS regions. This coarse segmentation was then employed as a shape prior term in the lattice Boltzmann (LB) model, as well as for initializing contours. Additionally, image gradients and local gray levels were integrated into the external force terms of the LB model to refine the coarse segmentation results. We assessed the segmentation accuracy of the model using the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset and evaluated the potential of the segmentation results as AD biomarkers on both the ADNI and Xuanwudatasets. The median Dice similarity coefficients (DSC) for the left caudal, right caudal, left rostral, and right rostral hippocampus were 0.87, 0.88, 0.88, and 0.89, respectively. The proportion of segmentation results with a DSC exceeding 0.8 was 77%, 78%, 77%, and 94% for the respective regions. In terms of volume, the correlation coefficients between the segmentation results of the four HS regions and the gold standard were 0.95, 0.93, 0.96, and 0.96, respectively. Regarding asymmetry, the correlation coefficient between the segmentation result's right caudal minus left caudal and the corresponding gold standard was 0.91, while for right rostral minus left rostral, it was 0.93. Over time, we observed a decline in the volumes of the four HS regions and the total hippocampal volume of mild cognitive impairment (MCI) converters. Analysis of inter-group differences revealed that, except for the right rostral region in the ADNI dataset, the p-values for the four HS regions in the normal controls (NC), MCI, and AD groups from both datasets were all below 0.05. The right caudal hippocampal volume demonstrated correlation coefficients of 0.47 and 0.43 with the mini-mental state examination (MMSE) and Montreal cognitive assessment (MoCA), respectively. Similarly, the left rostral hippocampal volume showed correlation coefficients of 0.50 and 0.58 with MMSE and MoCA,respectively. Our framework allows for direct application to different brain magnetic resonance (MR) datasets without the need for training. It eliminates the requirement for complex image preprocessing steps while achieving segmentation accuracy comparable to deep learning (DL) methods even with small sample sizes. Compared to traditional active contour models (ACM) and atlas-based methods, our approach exhibits significant speed advantages. The segmented HS regions hold promise as potential biomarkers for studying the progression ofAD.

Day-to-day sleep variability with Alzheimer's biomarkers in at-risk elderly.

Measuring day-to-day sleep variability might reveal unstable sleep-wake cycles reflecting neurodegenerative processes. We evaluated the association between Alzheimer's disease (AD) fluid biomarkers with day-to-day sleep variability. In the PREVENT-AD cohort, 203 dementia-free participants (age: 68.3±5.4; 78males) with a parental history of sporadic AD were tested with actigraphy and fluid biomarkers. Day-to-day variability (standard deviations over a week) was assessed for sleep midpoint, duration, efficiency, and nighttime activity count. Lower cerebrospinal fluid (CSF) ApoE, higher CSF p-tau181/amyloid-β (Aβ)42, and higher plasma p-tau231/Aβ42 were associated with higher variability of sleep midpoint, sleep duration, and/or activity count. The associations between fluid biomarkers with greater sleep duration variability were especially observed in those that carried the APOE4 allele, mild cognitive impairment converters, or those with gray matter atrophy. Day-to-day sleep variability were associated with biomarkers of AD in at-risk individuals, suggesting that unstable sleep promotes neurodegeneration or, conversely, that AD neuropathology disrupts sleep-wake cycles.

Identification of Mild Cognitive Impairment Conversion Using Augmented Resting-State Functional Connectivity Under Multi-Modal Parcellation

Identification of Mild Cognitive Impairment Conversion Using Augmented Resting-State Functional Connectivity Under Multi-Modal Parcellation

Structural connectivity from DTI to predict mild cognitive impairment in de novo Parkinson’s disease

BackgroundEarly detection of Parkinson's disease (PD) patients at high risk for mild cognitive impairment (MCI) can help with timely intervention. White matter structural connectivity is considered an early and sensitive indicator of neurodegenerative disease. ObjectivesTo investigate whether baseline white matter structural connectivity features from diffusion tensor imaging (DTI) of de novo PD patients can help predict PD-MCI conversion at an individual level using machine learning methods. MethodsWe included 90 de novo PD patients who underwent DTI and 3D T1-weighted imaging. Elastic net-based feature consensus ranking (ENFCR) was used with 1000 random training sets to select clinical and structural connectivity features. Linear discrimination analysis (LDA), support vector machine (SVM), K-nearest neighbor (KNN) and naïve Bayes (NB) classifiers were trained based on features selected more than 500 times. The area under the ROC curve (AUC), accuracy (ACC), sensitivity (SEN) and specificity (SPE) were used to evaluate model performance. ResultsA total of 57 PD patients were classified as PD-MCI nonconverters, and 33 PD patients were classified as PD-MCI converters. The models trained with clinical data showed moderate performance (AUC range: 0.62–0.68; ACC range: 0.63–0.77; SEN range: 0.45–0.66; SPE range: 0.64–0.84). Models trained with structural connectivity (AUC range, 0.81–0.84; ACC range, 0.75–0.86; SEN range, 0.77–0.91; SPE range, 0.71–0.88) performed similar to models that were trained with both clinical and structural connectivity data (AUC range, 0.81–0.85; ACC range, 0.74–0.85; SEN range, 0.79–0.91; SPE range, 0.70–0.89). ConclusionsBaseline white matter structural connectivity from DTI is helpful in predicting future MCI conversion in de novo PD patients.

Semantic cognition as an early cognitive marker of Alzheimer’s disease: importance of executive processes mediation

AbstractBackgroundImpaired semantic memory is one of the earliest characteristic features of Alzheimer’s disease. Its assessment is crucial to accurately predict conversion to Alzheimer disease related disorders (ADRD). Recent evidence supports the necessity to differentiate between subprocesses of semantic cognition: a) activation in the semantic representation system; b) executive processes to control or select relevant aspects of semantic knowledge. In this study we compare semantic memory tests that differ in these subprocesses to find out which of them are the best predictors of conversion of MCI to ACD in two years. We also want to identify which semantic memory tests have more sensitivity and specificity at the baseline in the differentiation MCI‐no converters and MCI converters after two yearsMethodWe assessed 130 amnestic MCI patients. Semantic memory tests more based on integrity of semantic knowledge (Similarities, ACE‐language) and more executive determined (verbal fluency – phonemic and categories, Boston Naming Test) were administered at baseline and at 2‐years follow‐up, together with cognitive screening tools and IADL measures. A binary regression model was used to examine which semantic memory tests predict MCI progression to Alzheimer’s Clinical Syndrome (ACS) in two years. ROC curves were used to study early clinical diagnosis differentiating MCI converters and MCI non convertersResultOnly semantic category fluency test significantly explained the conversion to ACS (p&lt;.01). Baseline score semantic categories fluency were significantly lower in MCI patients who develop ACS after two years (p&lt;.001). ROC curves showed that semantic categories fluency and Boston Naming tests were the best tests to differentiate at baseline between MCI converter and non‐converter (AUC = 0.768, p&lt;0,0001 and AUC = 0.712, p&lt;0,01, respectively)ConclusionSemantic category fluency test was the best predictor from MCI to ACS conversion in two years. Semantic category fluency and Boston Naming tests clearly identify people with high risk to develop ACS in two years. Semantic memory tests including executive contributions could detect and characterize prodromal Alzheimer’s disease

Dynamic change of hierarchical core voxels of functional brain network in aging

AbstractBackgroundMany methods were developed to assess the functional intervoxel connectivity to better understand the brain function in individuals. In this study, k‐core percolation method was assessed to reveal the dynamic hierarchical structure on resting‐state fMRI (rsfMRI) on voxel‐level and the feasibility of the method was evaluated by applying it in the aging process.MethodTotal 150 individuals were included in this study; 38 young healthy individuals from Seoul National University (SNU), and 32 aged healthy, 22 mild cognitive impairment‐converter (MCI‐C), 26 mild cognitive impairment non‐converter (MCI‐NC), and 32 Alzheimer’s disease (AD) subjects from Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. K‐core percolation method, a voxel‐based approach was applied to reveal the hierarchical structure of voxels in the brain. k max‐core and coreness k values derived from k‐core percolation characterizing the time‐varying core voxels were visualized on various plots to display the dynamic hierarchical structure more intuitively. Analysis was done in both static, dynamic studies, and in positive, negative correlations. Coreness k value map on brain T1 MRI was generated for further evaluation of the distribution of coreness k values.ResultDynamic hierarchical structure of voxels on various plots revealed time‐varying change of k max‐core voxels and coreness k values, reflecting the dynamic change of brain function in an individual, which was not fully reflected on static study. Dynamic flow pattern was different in positive and negative correlations, portraying the dynamic brain function in different neuronal networks. Asymmetric, unsynchronized distribution was deteriorated in the aging process on corneas k map and the asymmetry indexes of coreness k values revealed distinctive difference between young and aged healthy groups. The difference was more evident on dynamic study than static study. Also, as the age increased, coreness k values decreased, representing decreased connectivity in aging. However, asymmetry and decrease of coreness k values were not deteriorated by the severity of cognitive impairment.ConclusionInvestigation of dynamic functional connectivity with k‐core percolation on voxel‐level revealed time‐varying change of hierarchical structure of voxels in the aging process. With this method, characteristics of dynamic hierarchical structure of an individual can be discovered and have shown possibility of further clinical application.

Regional cortical hypoperfusion observed in elderly adults prior to development of mild cognitive impairment

AbstractBackgroundCardiovascular disease is associated with increased risk of developing mild cognitive impairment (MCI) and Alzheimer’s disease (AD), although the underlying mechanisms are not well understood. While it has been theorised that cerebral hypoperfusion caused by heart failure may directly lead to the formation of amyloid plaques and neurofibrillary tangles characteristic of AD, it has also been suggested that decreased perfusion occurs later in disease progression. We used arterial spin labelling (ASL) MRI to compare cerebral blood flow (CBF) between cognitively unimpaired elderly individuals who would remain cognitively stable and those who would later develop MCI to determine if hypoperfusion is detectable at a preclinical stage.Methods647 cognitively normal elderly participants with ASL and structural T1‐weighted MRI were selected from the Vallecas Project cohort, a single‐centre longitudinal study currently in its 12th year of annual follow‐up. 586 participants remained cognitively stable (CS) while 61 participants developed MCI over the course of the study. Cortical CBF maps derived from ASL images at the visit before MCI diagnosis were analysed in a voxelwise comparison with CS individuals, with age, sex, and total intracranial volume as covariates.ResultsCBF was significantly lower in the right insular cortex in future MCI converters than CS individuals (p&lt;0.05, family‐wise error (FWE) corrected). Hypoperfusion was also significant in the anterior corpus callosum in future converters.ConclusionCerebral hypoperfusion, as measured by ASL, can already be observed in individuals before the onset of clinical symptoms.

Regional cortical hypoperfusion observed in elderly adults prior to development of mild cognitive impairment

AbstractBackgroundCardiovascular disease is associated with increased risk of developing mild cognitive impairment (MCI) and Alzheimer’s disease (AD), although the underlying mechanisms are not well understood. While it has been theorised that cerebral hypoperfusion caused by heart failure may directly lead to the formation of amyloid plaques and neurofibrillary tangles characteristic of AD, it has also been suggested that decreased perfusion occurs later in disease progression. We used arterial spin labelling (ASL) MRI to compare cerebral blood flow (CBF) between cognitively unimpaired elderly individuals who would remain cognitively stable and those who would later develop MCI to determine if hypoperfusion is detectable at a preclinical stage.Method647 cognitively normal elderly participants with ASL and structural T1‐weighted MRI were selected from the Vallecas Project cohort, a single‐centre longitudinal study currently in its 12th year of annual follow‐up. 586 participants remained cognitively stable (CS) while 61 participants developed MCI over the course of the study. Cortical CBF maps derived from ASL images at the visit before MCI diagnosis were analysed in a voxelwise comparison with CS individuals, with age, sex, and total intracranial volume as covariates.ResultCBF was significantly lower in the right insular cortex in future MCI converters than CS individuals (p&lt;0.05, family‐wise error (FWE) corrected). Hypoperfusion was also significant in the anterior corpus callosum in future converters.ConclusionCerebral hypoperfusion, as measured by ASL, can already be observed in individuals before the onset of clinical symptoms.

Allostatic load profiles of stress to predict Alzheimer’s disease: an accessible opportunity to identify risk at early stages

AbstractBackgroundAlzheimer’s disease (AD) can start decades before symptoms appear, yet detection at preclinical stages remains a challenge due to a lack of screening tools that are both accurate and accessible. The multi‐system Allostatic Load index (AL), a stress‐related marker of cumulative biological risk, is associated with β‐amyloid (Aβ), tau proteins, and hippocampal atrophy, potentially linking AL to AD. If this is correct, a low‐cost and readily accessible peripheral blood AL‐based screening tool can offer an opportunity to identify individuals at risk for AD in primary care facilities. Here, we determined the AL profiles that predict the conversion risk from mild cognitive impairment (MCI) to AD by 36 months before diagnosis and associated with hippocampus volume (HV) reduction.MethodsUsing the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, we analyzed longitudinal data from 385 older adults with MCI at baseline, 12, and 36 months. Blood neuroendocrine (cortisol, LH, prolactin, testosterone) and immunologic (C‐reactive protein,fibrinogen,IL‐13,IL‐16,IL‐18,IL‐3,IL‐6,MCP‐3,MCP4,TNF‐alpha,insulin‐like growth factor) stress‐related biomarkers obtained at baseline and 12 months were transformed into AL indexes to generate the following profiles: 1)Decreasing AL: high at baseline and decrease over 12‐months; 2)Increasing AL: low at baseline, increase; and 3)Sustained High AL: high at baseline, remain high or increase. The ADNI consensus diagnosis determined the AD conversion and brain MRI assessed HV. Models were adjusted for socioeconomic and APOE4 covariates.Results41.6% of participants with Sustained High AL converted to AD, while 27.9% with Decreasing profiles remained MCI (p = 0.01). Increasing AL showed higher conversion risk than Decreasing profile (OR: 3.96). Sustained High AL was associated with faster HV reduction (p = 0.04). AL and blood Aβ models were compared for prediction accuracy.ConclusionThe odds of converting to AD were almost four times higher among participants with Sustained High AL profiles with faster hippocampus reduction than Decreasing AL profiles, suggesting that elevated AL profiles might be a risk factor and a potential mechanism explaining MCI conversion to AD. These findings set the foundation for exploring accessible bio‐behavioral screening tools to sign risk in vulnerable individuals in primary care facilities and refer them to specialized clinics for timely intervention.

A high‐dimensional incomplete‐modality transfer learning method for early prediction of Alzheimer’s disease

AbstractBackgroundPrediction of Alzheimer’s disease (AD) risk for individuals with mild cognitive impairment (MCI) provides an opportunity for early intervention. Neuroimaging of different types/modalities has shown promise, but not every patient has all the modalities due to the cost and accessibility constraints. To integrate incomplete multi‐modality datasets, we previously developed a machine learning (ML) model called incomplete‐modality transfer learning (IMTL). We extended the capacity of IMTL to handle high‐dimensional feature sets, namely, HD‐IMTL, to further improve accuracy and robustness.MethodOur dataset included 1319 T1‐MRI scans from MCI patients in ADNI; among them, 1002 had FDG‐PET and 612 had amyloid‐PET. 156 regional volumetric and thickness features were computed from MRI and 83 and 83 regional SUVR features from FDG‐PET and amyloid‐PET, respectively. The dataset is randomly split into training and test sets. The goal of HD‐IMTL was to jointly train 4 ML models to predict MCI conversion to AD in 36 months, with each model based on a certain combination of available modalities, namely, MRI, MRI+FDG, MRI+amyloid, and MRI+FDG+amyloid. These correspond to patient sub‐cohorts that differ in their access to imaging modalities. To handle high‐dimensional features, we employed feature screening to remove uninformative features, performed modality‐wise partial least squares (PLS) to condense remaining features into PLS components, and used correlation tests to select components. To jointly train the 4 ML prediction models, IMTL was used, which is a generative model that uses expectation‐maximization (EM) in joint parameter estimation to facilitate transfer learning. To account for sample imbalance in training, the Synthetic Minority Over‐sampling Technique (SMOTE) was used. The trained models were applied to the test set. 20 training/test splits were repeated and AUCs on the test set were averaged. For comparison, three existing ML models for incomplete‐modality fusion were applied to the same dataset.ResultThe AUCs by HD‐IMTL were 0.802, 0.840, 0.868, and 0.880 for sub‐cohorts with MRI, MRI+FDG, MRI+amyloid, and MRI+FDG+amyloid, respectively. The AUCs by existing methods were lower, with ranges of 0.749‐0.793, 0.769‐0.826, 0.816‐0.863, and 0.832‐0.868.ConclusionHD‐IMTL demonstrated high accuracy in predicting MCI conversion to AD for patients with varying access/availability of imaging modalities.