Patterns of longitudinal cortical atrophy over 3 years in empirically derived MCI subtypes

Combined quantitative amyloid-β PET and structural MRI features improve Alzheimer's Disease classification in random forest model - A multicenter study

RATIONALE AND OBJECTIVES

Prior to clinical presentations of Alzheimer's Disease (AD), neuropathological changes, such as amyloid-β and brain atrophy, have accumulated at the earlier stages of the disease. The combination of such biomarkers assessed by multiple modalities commonly improves the likelihood of AD etiology. We aimed to explore the discriminative ability of Aβ PET features and whether combining Aβ PET and structural MRI features can improve the classification performance of the machine learning model in older healthy control (OHC) and mild cognitive impairment (MCI) from AD.

MATERIAL AND METHODS

We collected 94 AD patients, 82 MCI patients, and 85 OHC from three different cohorts. 17 global/regional Aβ features in Centiloid, 122 regional volume, and 68 regional cortical thickness were extracted as imaging features. Single or combined modality features were used to train the random forest model on the testing set. The top 10 features were sorted based on the Gini index in each binary classification.

RESULTS

The results showed that AUC scores were 0.81/0.86 and 0.69/0.68 using sMRI/Aβ PET features on the testing set in differentiating OHC and MCI from AD. The performance was improved while combining two-modality features with an AUC of 0.89 and an AUC of 0.71 in two classifications. Compared to sMRI features, particular Aβ PET features contributed more to differentiating AD from others.

CONCLUSION

Our study demonstrated the discriminative ability of Aβ PET features in differentiating AD from OHC and MCI. A combination of Aβ PET and structural MRI features can improve the RF model performance.

History of traumatic brain injury is associated with increased grey-matter loss in patients with mild cognitive impairment

OBJECTIVES

To investigate whether a history of traumatic brain injury (TBI) is associated with greater long-term grey-matter loss in patients with mild cognitive impairment (MCI).

METHODS

85 patients with MCI were identified, including 26 with a previous history of traumatic brain injury (MCI[TBI-]) and 59 without (MCI[TBI+]). Cortical thickness was evaluated by segmenting T1-weighted MRI scans acquired longitudinally over a 2-year period. Bayesian multilevel modelling was used to evaluate group differences in baseline cortical thickness and longitudinal change, as well as group differences in neuropsychological measures of executive function.

RESULTS

At baseline, the MCI[TBI+] group had less grey matter within right entorhinal, left medial orbitofrontal and inferior temporal cortex areas bilaterally. Longitudinally, the MCI[TBI+] group also exhibited greater longitudinal declines in left rostral middle frontal, the left caudal middle frontal and left lateral orbitofrontal areas sover the span of 2 years (median = 1-2%, 90%HDI [-0.01%: -0.001%], probability of direction (PD) = 90-99%). The MCI[TBI+] group also displayed greater longitudinal declines in Trail-Making-Test (TMT)-derived ratio (median: 0.737%, 90%HDI: [0.229%: 1.31%], PD = 98.8%) and differences scores (median: 20.6%, 90%HDI: [-5.17%: 43.2%], PD = 91.7%).

CONCLUSIONS

Our findings support the notion that patients with MCI and a history of TBI are at risk of accelerated neurodegeneration, displaying greatest evidence for cortical atrophy within the left middle frontal and lateral orbitofrontal frontal cortex. Importantly, these results suggest that long-term TBI-mediated atrophy is more pronounced in areas vulnerable to TBI-related mechanical injury, highlighting their potential relevance for diagnostic forms of intervention in TBI.

Delineating the Heterogeneity of Alzheimer's Disease and Mild Cognitive Impairment Using Normative Models of Dynamic Brain Functional Networks

BACKGROUND

Alzheimer's disease (AD), which has been identified as the most common type of dementia, presents considerable heterogeneity in its clinical manifestations. Early intervention at the stage of mild cognitive impairment (MCI) holds potential in AD prevention. However, characterizing the heterogeneity of neurobiological abnormalities and identifying MCI subtypes pose significant challenges.

METHODS

We constructed sex-specific normative age models of dynamic brain functional networks and mapped the deviations of the brain characteristics for individuals from multiple datasets, including 295 patients with AD, 441 patients with MCI, and 1160 normal control participants. Then, based on these individual deviation patterns, subtypes for both AD and MCI were identified using the clustering method, and their similarities and differences were comprehensively assessed.

RESULTS

Individuals with AD and MCI were clustered into 2 subtypes, and these subtypes exhibited significant differences in their intrinsic brain functional phenotypes and spatial atrophy patterns, as well as in disease progression and cognitive decline trajectories. The subtypes with positive deviations in AD and MCI shared similar deviation patterns, as did those with negative deviations. There was a potential transformation of MCI with negative deviation patterns into AD, and participants with MCI had a more severe cognitive decline rate.

CONCLUSIONS

In this study, we quantified neurophysiological heterogeneity by analyzing deviation patterns from the dynamic functional connectome normative model and identified disease subtypes of AD and MCI using a comprehensive resting-state functional magnetic resonance imaging multicenter dataset. The findings provide new insights for developing early prevention and personalized treatment strategies for AD.

Data-driven classification of cognitively normal and mild cognitive impairment subtypes predicts progression in the NACC dataset

INTRODUCTION

Data-driven neuropsychological methods can identify mild cognitive impairment (MCI) subtypes with stronger associations to dementia risk factors than conventional diagnostic methods.

METHODS

Cluster analysis used neuropsychological data from participants without dementia (mean age = 71.6 years) in the National Alzheimer's Coordinating Center (NACC) Uniform Data Set (n = 26,255) and the "normal cognition" subsample (n = 16,005). Survival analyses examined MCI or dementia progression.

RESULTS

Five clusters were identified: "Optimal" cognitively normal (oCN; 13.2%), "Typical" CN (tCN; 28.0%), Amnestic MCI (aMCI; 25.3%), Mixed MCI-Mild (mMCI-Mild; 20.4%), and Mixed MCI-Severe (mMCI-Severe; 13.0%). Progression to dementia differed across clusters (oCN < tCN < aMCI < mMCI-Mild < mMCI-Severe). Cluster analysis identified more MCI cases than consensus diagnosis. In the "normal cognition" subsample, five clusters emerged: High-All Domains (High-All; 16.7%), Low-Attention/Working Memory (Low-WM; 22.1%), Low-Memory (36.3%), Amnestic MCI (16.7%), and Non-amnestic MCI (naMCI; 8.3%), with differing progression rates (High-All < Low-WM = Low-Memory < aMCI < naMCI).

DISCUSSION

Our data-driven methods outperformed consensus diagnosis by providing more precise information about progression risk and revealing heterogeneity in cognition and progression risk within the NACC "normal cognition" group.

Neuropsychiatric symptoms and brain morphology in patients with mild cognitive impairment, cerebrovascular disease and Parkinson disease: A cross sectional and longitudinal study

OBJECTIVES

Neuropsychiatric symptoms (NPS) increase risk of developing dementia and are linked to various neurodegenerative conditions, including mild cognitive impairment (MCI due to Alzheimer's disease [AD]), cerebrovascular disease (CVD), and Parkinson's disease (PD). We explored the structural neural correlates of NPS cross-sectionally and longitudinally across various neurodegenerative diagnoses.

METHODS

The study included individuals with MCI due to AD, (n = 74), CVD (n = 143), and PD (n = 137) at baseline, and at 2-years follow-up (MCI due to AD, n = 37, CVD n = 103, and PD n = 84). We assessed the severity of NPS using the Neuropsychiatric Inventory Questionnaire. For brain structure we included cortical thickness and subcortical volume of predefined regions of interest associated with corticolimbic and frontal-executive circuits.

RESULTS

Cross-sectional analysis revealed significant negative correlations between appetite with both circuits in the MCI and CVD groups, while apathy was associated with these circuits in both the MCI and PD groups. Longitudinally, changes in apathy scores in the MCI group were negatively linked to the changes of the frontal-executive circuit. In the CVD group, changes in agitation and nighttime behavior were negatively associated with the corticolimbic and frontal-executive circuits, respectively. In the PD group, changes in disinhibition and apathy were positively associated with the corticolimbic and frontal-executive circuits, respectively.

CONCLUSIONS

The observed correlations suggest that underlying pathological changes in the brain may contribute to alterations in neural activity associated with MBI. Notably, the difference between cross-sectional and longitudinal results indicates the necessity of conducting longitudinal studies for reproducible findings and drawing robust inferences.

Cognition and Amyloid-β in Older Veterans: Characterization and Longitudinal Outcomes of Data-Derived Phenotypes

Background

Within older Veterans, multiple factors may contribute to cognitive difficulties. Beyond Alzheimer's disease (AD), psychiatric (e.g., PTSD) and health comorbidities (e.g., TBI) may also impact cognition.

Objective

This study aimed to derive subgroups based on objective cognition, subjective cognitive decline (SCD), and amyloid burden, and then compare subgroups on clinical characteristics, biomarkers, and longitudinal change in functioning and global cognition.

Methods

Cluster analysis of neuropsychological measures, SCD, and amyloid PET was conducted on 228 predominately male Vietnam-Era Veterans from the Department of Defense-Alzheimer's Disease Neuroimaging Initiative. Cluster-derived subgroups were compared on baseline characteristics as well as 1-year changes in everyday functioning and global cognition.

Results

The cluster analysis identified 3 groups. Group 1 (n = 128) had average-to-above average cognition with low amyloid burden. Group 2 (n = 72) had the lowest memory and language, highest SCD, and average amyloid burden; they also had the most severe PTSD, pain, and worst sleep quality. Group 3 (n = 28) had the lowest attention/executive functioning, slightly low memory and language, elevated amyloid and the worst AD biomarkers, and the fastest rate of everyday functioning and cognitive decline.

CONCLUSIONS

Psychiatric and health factors likely contributed to Group 2's low memory and language performance. Group 3 was most consistent with biological AD, yet attention/executive function was the lowest score. The complexity of older Veterans' co-morbid conditions may interact with AD pathology to show attention/executive dysfunction (rather than memory) as a prominent early symptom. These results could have important implications for the implementation of AD-modifying drugs in older Veterans.

Increased Likelihood of Dementia with Coexisting Atrophy of Multiple Regions of Interest

BACKGROUND

Brain volume is associated with cognitive decline in later life, and cortical brain atrophy exceeding the normal range is related to inferior cognitive and behavioral outcomes in later life.

OBJECTIVE

To investigate the likelihood of cognitive decline, mild cognitive impairment (MCI), or dementia, when regional atrophy is present in participants' magnetic resonance imaging (MRI).

METHODS

Multi-center MRI data of 2,545 adults were utilized to measure regional volumes using NEUROPHET AQUA. Four lobes (frontal, parietal, temporal, and occipital), four Alzheimer's disease-related regions (entorhinal, fusiform, inferior temporal, and middle temporal area), and the hippocampus in the left and right hemispheres were measured and analyzed. The presence of regional atrophy from brain MRI was defined as ≤1.5 standard deviation (SD) compared to the age- and sex-matched cognitively normal population. The risk ratio for cognitive decline was investigated for participants with regional atrophy in contrast to those without regional atrophy.

RESULTS

The risk ratio for cognitive decline was significantly higher when hippocampal atrophy was present (MCI, 1.84, p < 0.001; dementia, 4.17, p < 0.001). Additionally, participants with joint atrophy in multiple regions showed a higher risk ratio for dementia, e.g., 9.6 risk ratio (95% confidence interval, 8.0-11.5), with atrophy identified in the frontal, temporal, and hippocampal gray matter, than those without atrophy.

CONCLUSIONS

Our study showed that individuals with multiple regional atrophy (either lobar or AD-specific regions) have a higher likelihood of developing dementia compared to the age- and sex-matched population without atrophy. Thus, further consideration is needed when assessing MRI findings.

Neuroanatomical profiles of cognitive phenotypes in patients with primary brain tumors

Background

Patients with brain tumors demonstrate heterogeneous patterns of cognitive impairment, likely related to multifactorial etiologies and variable tumor-specific factors. Cognitive phenotyping offers a patient-centered approach to parsing heterogeneity by classifying individuals based on patterns of impairment. The aim of this study was to investigate the neuroanatomical patterns associated with each phenotype to gain a better understanding of the mechanisms underlying impairments.

Methods

Patients with primary brain tumors were recruited for a prospective, observational study. Patients were cognitively phenotyped using latent profile analysis in a prior study, revealing 3 distinct groups: generalized, isolated verbal memory, and minimal impairment. Whole brain cortical thickness (CT), fractional anisotropy, and mean diffusivity (MD) were compared across phenotypes, and associations between imaging metrics and cognitive scores were explored.

Results

Neurocognitive, structural MRI, and diffusion MRI data were available for 82 participants at baseline. Compared to the minimal impairment group, the generalized impairment group showed a widespread, bi-hemispheric pattern of decreased CT (P-value range: .004-.049), while the verbal memory impairment group showed decreased CT (P-value range: .006-.049) and increased MD (P-value range: .015-.045) bilaterally in the temporal lobes. In the verbal memory impairment group only, increased parahippocampal MD was associated with lower verbal memory scores (P-values < .01).

Conclusions

Cognitive phenotypes in patients with brain tumors showed unique patterns of brain pathology, suggesting different underlying mechanisms of their impairment profiles. These distinct patterns highlight the biological relevance of our phenotyping approach and help to identify areas of structural and microstructural vulnerability that could inform treatment decisions.

Heterogeneity in subjective cognitive decline in the Sino Longitudinal Study on Cognitive Decline(SILCODE): Empirically derived subtypes, structural and functional verification

AIMS

We evaluated whether Subjective Cognitive Decline (SCD) subtypes could be empirically derived within the Sino Longitudinal Study on Cognitive Decline (SILCODE) SCD cohort and examined associated neuroimaging markers, biomarkers, and clinical outcomes.

METHODS

A cluster analysis was performed on eight neuropsychological test scores from 124 SCD SILCODE participants and 57 normal control (NC) subjects. Structural and functional neuroimaging indices were used to evaluate the SCD subgroups.

RESULTS

Four subtypes emerged: (1) dysexecutive/mixed SCD (n = 23), (2) neuropsychiatric SCD (n = 24), (3) amnestic SCD (n = 22), and (4) cluster-derived normal (n = 55) who exhibited normal performance in neuropsychological tests. Compared with the NC group, each subgroup showed distinct patterns in gray matter (GM) volume and the amplitude of low-frequency fluctuations (ALFF). Lower fractional anisotropy (FA) values were only found in the neuropsychiatric SCD group relative to NC.

CONCLUSION

The identification of empirically derived SCD subtypes demonstrates the presence of heterogeneity in SCD neuropsychological profiles. The cluster-derived normal group may represent the majority of SCD individuals who do not show progressive cognitive decline; the dysexecutive/mixed SCD and amnestic SCD might represent high-risk groups with progressing cognitive decline; and finally, the neuropsychiatric SCD may represent a new topic in SCD research.

Grey matter volume and CSF biomarkers predict neuropsychological subtypes of MCI

There is increasing evidence of different subtypes of individuals with mild cognitive impairment (MCI). An important line of research is whether neuropsychologically-defined subtypes have distinct patterns of neurodegeneration and cerebrospinal fluid (CSF) biomarker composition. In our study, we demonstrated that MCI participants of the ADNI database (N = 640) can be discriminated into 3 coherent neuropsychological subgroups. Our clustering approach revealed amnestic MCI, mixed MCI, and cluster-derived normal subgroups. Furthermore, classification modeling revealed that specific predictive features can be used to differentiate amnestic and mixed MCI from cognitively normal (CN) controls: CSF Aβ142 concentration for the former and CSF Aβ1-42 concentration, tau concentration as well as grey matter atrophy (especially in the temporal and occipital lobes) for the latter. In contrast, participants from the cluster-derived normal subgroup exhibited an identical profile to CN controls in terms of cognitive performance, brain structure, and CSF biomarker levels. Our comprehensive data analytics strategy provides further evidence that multimodal neuropsychological subtyping is both clinically and neurobiologically meaningful.

Cognitive phenotypes in late-onset epilepsy: results from the atherosclerosis risk in communities study

Introduction

Cognitive phenotyping is a widely used approach to characterize the heterogeneity of deficits in patients with a range of neurological disorders but has only recently been applied to patients with epilepsy. In this study, we identify cognitive phenotypes in older adults with late-onset epilepsy (LOE) and examine their demographic, clinical, and vascular profiles. Further, we examine whether specific phenotypes pose an increased risk for progressive cognitive decline.

Methods

Participants were part of the Atherosclerosis Risk in Communities Study (ARIC), a prospective longitudinal community-based cohort study of 15,792 individuals initially enrolled in 1987-1989. LOE was identified from linked Centers for Medicare and Medicaid Services claims data. Ninety-one participants with LOE completed comprehensive testing either prior to or after seizure onset as part of a larger cohort in the ARIC Neurocognitive Study in either 2011-2013 or 2016-2017 (follow-up mean = 4.9 years). Cognitive phenotypes in individuals with LOE were derived by calculating test-level impairments for each participant (i.e., ≤1 SD below cognitively normal participants on measures of language, memory, and executive function/processing speed); and then assigning participants to phenotypes if they were impaired on at least two tests within a domain. The total number of impaired domains was used to determine the cognitive phenotypes (i.e., Minimal/No Impairment, Single Domain, or Multidomain).

Results

At our baseline (Visit 5), 36.3% met criteria for Minimal/No Impairment, 35% for Single Domain Impairment (with executive functioning/ processing speed impaired in 53.6%), and 28.7% for Multidomain Impairment. The Minimal/No Impairment group had higher education and occupational complexity. There were no differences in clinical or vascular risk factors across phenotypes. Of those participants with longitudinal data (Visit 6; n = 24), 62.5% declined (i.e., progressed to a more impaired phenotype) and 37.5% remained stable. Those who remained stable were more highly educated compared to those that declined.

Discussion

Our results demonstrate the presence of identifiable cognitive phenotypes in older adults with LOE. These results also highlight the high prevalence of cognitive impairments across domains, with deficits in executive function/processing speed the most common isolated impairment. We also demonstrate that higher education was associated with a Minimal/No Impairment phenotype and lower risk for cognitive decline over time.

An open-source tool for longitudinal whole-brain and white matter lesion segmentation

In this paper we describe and validate a longitudinal method for whole-brain segmentation of longitudinal MRI scans. It builds upon an existing whole-brain segmentation method that can handle multi-contrast data and robustly analyze images with white matter lesions. This method is here extended with subject-specific latent variables that encourage temporal consistency between its segmentation results, enabling it to better track subtle morphological changes in dozens of neuroanatomical structures and white matter lesions. We validate the proposed method on multiple datasets of control subjects and patients suffering from Alzheimer's disease and multiple sclerosis, and compare its results against those obtained with its original cross-sectional formulation and two benchmark longitudinal methods. The results indicate that the method attains a higher test-retest reliability, while being more sensitive to longitudinal disease effect differences between patient groups. An implementation is publicly available as part of the open-source neuroimaging package FreeSurfer.

Development and application of the International Classification of Cognitive Disorders in Epilepsy (IC-CoDE): Initial results from a multi-center study of adults with temporal lobe epilepsy

[Correction Notice: An Erratum for this article was reported online in Neuropsychology on Sep 15 2022 (see record 2023-01997-001). In the original article, there was an error in Figure 2. In the box at the top left of the figure, the fourth explanation incorrectly stated, "Generalized impairment = At least one test < -1.0 or -1.5SD in three or more domains." The correct wording is "Generalized impairment = At least two tests < -1.0 or -1.5SD in each of three or more domains." All versions of this article have been corrected.] Objective: To describe the development and application of a consensus-based, empirically driven approach to cognitive diagnostics in epilepsy research-The International Classification of Cognitive Disorders in Epilepsy (IC-CoDE) and to assess the ability of the IC-CoDE to produce definable and stable cognitive phenotypes in a large, multi-center temporal lobe epilepsy (TLE) patient sample.

METHOD

Neuropsychological data were available for a diverse cohort of 2,485 patients with TLE across seven epilepsy centers. Patterns of impairment were determined based on commonly used tests within five cognitive domains (language, memory, executive functioning, attention/processing speed, and visuospatial ability) using two impairment thresholds (≤1.0 and ≤1.5 standard deviations below the normative mean). Cognitive phenotypes were derived across samples using the IC-CoDE and compared to distributions of phenotypes reported in existing studies.

RESULTS

Impairment rates were highest on tests of language, followed by memory, executive functioning, attention/processing speed, and visuospatial ability. Application of the IC-CoDE using varying operational definitions of impairment (≤ 1.0 and ≤ 1.5 SD) produced cognitive phenotypes with the following distribution: cognitively intact (30%-50%), single-domain (26%-29%), bi-domain (14%-19%), and generalized (10%-22%) impairment. Application of the ≤ 1.5 cutoff produced a distribution of phenotypes that was consistent across cohorts and approximated the distribution produced using data-driven approaches in prior studies.

CONCLUSIONS

The IC-CoDE is the first iteration of a classification system for harmonizing cognitive diagnostics in epilepsy research that can be applied across neuropsychological tests and TLE cohorts. This proof-of-principle study in TLE offers a promising path for enhancing research collaborations globally and accelerating scientific discoveries in epilepsy. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Accounting for word production, comprehension, and repetition in semantic dementia, Alzheimer's dementia, and mild cognitive impairment

It has been known since Pick (1892, 1904) that word retrieval is commonly impaired in left temporal lobe degeneration. Individuals with semantic dementia (SD), Alzheimer's dementia (AD), and mild cognitive impairment (MCI) present with word retrieval difficulty, while comprehension is less affected and repetition is preserved. Whereas computational models have elucidated performance in poststroke and progressive aphasias, including SD, simulations are lacking for AD and MCI. Here, the WEAVER++/ARC model, which has provided neurocognitive computational accounts of poststroke and progressive aphasias, is extended to AD and MCI. Assuming a loss of activation capacity in semantic memory in SD, AD, and MCI, the simulations showed that severity variation accounts for 99% of the variance in naming, comprehension, and repetition at the group level and 95% at the individual patient level (N = 49). Other plausible assumptions do less well. This supports a unified account of performance in SD, AD, and MCI.

Automatic analysis of skull thickness, scalp-to-cortex distance and association with age and sex in cognitively normal elderly

Personalized neurostimulation has been a potential treatment for many brain diseases, which requires insights into brain/skull geometry. Here, we developed an open source efficient pipeline BrainCalculator for automatically computing the skull thickness map, scalp-to-cortex distance (SCD), and brain volume based on T 1 -weighted magnetic resonance imaging (MRI) data. We examined the influence of age and sex cross-sectionally in 407 cognitively normal older adults (71.9±8.0 years, 60.2% female) from the ADNI. We demonstrated the compatibility of our pipeline with commonly used preprocessing packages and found that BrainSuite Skullfinder was better suited for such automatic analysis compared to FSL Brain Extraction Tool 2 and SPM12- based unified segmentation using ground truth. We found that the sphenoid bone and temporal bone were thinnest among the skull regions in both females and males. There was no increase in regional minimum skull thickness with age except in the female sphenoid bone. No sex difference in minimum skull thickness or SCD was observed. Positive correlations between age and SCD were observed, faster in females (0.307%/y) than males (0.216%/y) in temporal SCD. A negative correlation was observed between age and whole brain volume computed based on brain surface (females -1.031%/y, males -0.998%/y). In conclusion, we developed an automatic pipeline for MR-based skull thickness map, SCD, and brain volume analysis and demonstrated the sex-dependent association between minimum regional skull thickness, SCD and brain volume with age. This pipeline might be useful for personalized neurostimulation planning.

Language patterns in Japanese patients with Alzheimer disease: A machine learning approach

AIM

The authors applied natural language processing and machine learning to explore the disease-related language patterns that warrant objective measures for assessing language ability in Japanese patients with Alzheimer disease (AD), while most previous studies have used large publicly available data sets in Euro-American languages.

METHODS

The authors obtained 276 speech samples from 42 patients with AD and 52 healthy controls, aged 50 years or older. A natural language processing library for Python was used, spaCy, with an add-on library, GiNZA, which is a Japanese parser based on Universal Dependencies designed to facilitate multilingual parser development. The authors used eXtreme Gradient Boosting for our classification algorithm. Each unit of part-of-speech and dependency was tagged and counted to create features such as tag-frequency and tag-to-tag transition-frequency. Each feature's importance was computed during the 100-fold repeated random subsampling validation and averaged.

RESULTS

The model resulted in an accuracy of 0.84 (SD = 0.06), and an area under the curve of 0.90 (SD = 0.03). Among the features that were important for such predictions, seven of the top 10 features were related to part-of-speech, while the remaining three were related to dependency. A box plot analysis demonstrated that the appearance rates of content words-related features were lower among the patients, whereas those with stagnation-related features were higher.

CONCLUSION

The current study demonstrated a promising level of accuracy for predicting AD and found the language patterns corresponding to the type of lexical-semantic decline known as 'empty speech', which is regarded as a characteristic of AD.

Predicting conversion to Alzheimer's disease in individuals with Mild Cognitive Impairment using clinically transferable features

Patients with Mild Cognitive Impairment (MCI) have an increased risk of Alzheimer's disease (AD). Early identification of underlying neurodegenerative processes is essential to provide treatment before the disease is well established in the brain. Here we used longitudinal data from the ADNI database to investigate prediction of a trajectory towards AD in a group of patients defined as MCI at a baseline examination. One group remained stable over time (sMCI, n = 357) and one converted to AD (cAD, n = 321). By running two independent classification methods within a machine learning framework, with cognitive function, hippocampal volume and genetic APOE status as features, we obtained a cross-validation classification accuracy of about 70%. This level of accuracy was confirmed across different classification methods and validation procedures. Moreover, the sets of misclassified subjects had a large overlap between the two models. Impaired memory function was consistently found to be one of the core symptoms of MCI patients on a trajectory towards AD. The prediction above chance level shown in the present study should inspire further work to develop tools that can aid clinicians in making prognostic decisions.

Cortical Thickness Changes After Computerized Working Memory Training in Patients With Mild Cognitive Impairment

Background

Adaptive computerized working memory (WM) training has shown favorable effects on cerebral cortical thickness as compared to non-adaptive training in healthy individuals. However, knowledge of WM training-related morphological changes in mild cognitive impairment (MCI) is limited.

Objective

The primary objective of this double-blind randomized study was to investigate differences in longitudinal cortical thickness trajectories after adaptive and non-adaptive WM training in patients with MCI. We also investigated the genotype effects on cortical thickness trajectories after WM training combining these two training groups using longitudinal structural magnetic resonance imaging (MRI) analysis in Freesurfer.

Method

Magnetic resonance imaging acquisition at 1.5 T were performed at baseline, and after four- and 16-weeks post training. A total of 81 individuals with MCI accepted invitations to undergo 25 training sessions over 5 weeks. Longitudinal Linear Mixed effect models investigated the effect of adaptive vs. non-adaptive WM training. The LME model was fitted for each location (vertex). On all statistical analyzes, a threshold was applied to yield an expected false discovery rate (FDR) of 5%. A secondary LME model investigated the effects of LMX1A and APOE-ε4 on cortical thickness trajectories after WM training.

Results

A total of 62 participants/patients completed the 25 training sessions. Structural MRI showed no group difference between the two training regimes in our MCI patients, contrary to previous reports in cognitively healthy adults. No significant structural cortical changes were found after training, regardless of training type, across all participants. However, LMX1A-AA carriers displayed increased cortical thickness trajectories or lack of decrease in two regions post-training compared to those with LMX1A-GG/GA. No training or training type effects were found in relation to the APOE-ε4 gene variants.

Conclusion

The MCI patients in our study, did not have improved cortical thickness after WM training with either adaptive or non-adaptive training. These results were derived from a heterogeneous population of MCI participants. The lack of changes in the cortical thickness trajectory after WM training may also suggest the lack of atrophy during this follow-up period. Our promising results of increased cortical thickness trajectory, suggesting greater neuroplasticity, in those with LMX1A-AA genotype need to be validated in future trials.

Using the Alzheimer's Disease Neuroimaging Initiative to improve early detection, diagnosis, and treatment of Alzheimer's disease

INTRODUCTION

The Alzheimer's Disease Neuroimaging Initiative (ADNI) has accumulated 15 years of clinical, neuroimaging, cognitive, biofluid biomarker and genetic data, and biofluid samples available to researchers, resulting in more than 3500 publications. This review covers studies from 2018 to 2020.

METHODS

We identified 1442 publications using ADNI data by conventional search methods and selected impactful studies for inclusion.

RESULTS

Disease progression studies supported pivotal roles for regional amyloid beta (Aβ) and tau deposition, and identified underlying genetic contributions to Alzheimer's disease (AD). Vascular disease, immune response, inflammation, resilience, and sex modulated disease course. Biologically coherent subgroups were identified at all clinical stages. Practical algorithms and methodological changes improved determination of Aβ status. Plasma Aβ, phosphorylated tau181, and neurofilament light were promising noninvasive biomarkers. Prognostic and diagnostic models were externally validated in ADNI but studies are limited by lack of ethnocultural cohort diversity.

DISCUSSION

ADNI has had a profound impact in improving clinical trials for AD.

Regional Radiomics Similarity Networks Reveal Distinct Subtypes and Abnormality Patterns in Mild Cognitive Impairment

Individuals with mild cognitive impairment (MCI) of different subtypes show distinct alterations in network patterns. The first aim of this study is to identify the subtypes of MCI by employing a regional radiomics similarity network (R2SN). The second aim is to characterize the abnormality patterns associated with the clinical manifestations of each subtype. An individual-level R2SN is constructed for N = 605 normal controls (NCs), N = 766 MCI patients, and N = 283 Alzheimer's disease (AD) patients. MCI patients' R2SN profiles are clustered into two subtypes using nonnegative matrix factorization. The patterns of brain alterations, gene expression, and the risk of cognitive decline in each subtype are evaluated. MCI patients are clustered into "similar to the pattern of NCs" (N-CI, N = 252) and "similar to the pattern of AD" (A-CI, N = 514) subgroups. Significant differences are observed between the subtypes with respect to the following: 1) clinical measures; 2) multimodal neuroimaging; 3) the proportion of progression to dementia (61.54% for A-CI and 21.77% for N-CI) within three years; 4) enriched genes for potassium-ion transport and synaptic transmission. Stratification into the two subtypes provides new insight for risk assessment and precise early intervention for MCI patients.

Cognitive Heterogeneity and Risk of Progression in Data-Driven Subtle Cognitive Decline Phenotypes

BACKGROUND

There is increasing recognition of cognitive and pathological heterogeneity in early-stage Alzheimer's disease and other dementias. Data-driven approaches have demonstrated cognitive heterogeneity in those with mild cognitive impairment (MCI), but few studies have examined this heterogeneity and its association with progression to MCI/dementia in cognitively unimpaired (CU) older adults.

OBJECTIVE

We identified cluster-derived subgroups of CU participants based on comprehensive neuropsychological data and compared baseline characteristics and rates of progression to MCI/dementia or a Dementia Rating Scale (DRS) of ≤129 across subgroups.

METHODS

Hierarchical cluster analysis was conducted on individual baseline neuropsychological test scores from 365 CU participants in the UCSD Shiley-Marcos Alzheimer's Disease Research Center longitudinal cohort. Cox regressions examined the risk of progression to consensus diagnosis of MCI or dementia, or to DRS score ≤129, by cluster group.

RESULTS

Cluster analysis identified 5 groups: All-Average (n = 139), Low-Visuospatial (n = 46), Low-Executive (n = 51), Low-Memory/Language (n = 83), and Low-All Domains (n = 46). Subgroups had unique demographic and clinical characteristics. Rates of progression to MCI/dementia or to DRS ≤129 were faster for all subgroups (Low-All Domains progressed the fastest > Low Memory/Language≥Low-Visuospatial and Low-Executive) relative to the All-Average subgroup.

CONCLUSION

Faster progression in the Low-Visuospatial, Low-Executive, and Low-Memory/Language groups compared to the All-Average group suggests that there are multiple pathways and/or unique subtle cognitive decline profiles that ultimately lead to a diagnosis of MCI/dementia. Use of comprehensive neuropsychological test batteries that assess several domains may be a key first step toward an individualized approach to early detection and fewer missed opportunities for early intervention.

Effects of Meditation on Structural Changes of the Brain in Patients With Mild Cognitive Impairment or Alzheimer's Disease Dementia

Previous cross-sectional studies reported positive effects of meditation on the brain areas related to attention and executive function in the healthy elderly population. Effects of long-term regular meditation in persons with mild cognitive impairment (MCI) and Alzheimer's disease dementia (AD) have rarely been studied. In this study, we explored changes in cortical thickness and gray matter volume in meditation-naïve persons with MCI or mild AD after long-term meditation intervention. MCI or mild AD patients underwent detailed clinical and neuropsychological assessment and were assigned into meditation or non-meditation groups. High resolution T1-weighted magnetic resonance images (MRI) were acquired at baseline and after 6 months. Longitudinal symmetrized percentage changes (SPC) in cortical thickness and gray matter volume were estimated. Left caudal middle frontal, left rostral middle frontal, left superior parietal, right lateral orbitofrontal, and right superior frontal cortices showed changes in both cortical thickness and gray matter volume; the left paracentral cortex showed changes in cortical thickness; the left lateral occipital, left superior frontal, left banks of the superior temporal sulcus (bankssts), and left medial orbitofrontal cortices showed changes in gray matter volume. All these areas exhibited significantly higher SPC values in meditators as compared to non-meditators. Conversely, the left lateral occipital, and right posterior cingulate cortices showed significantly lower SPC values for cortical thickness in the meditators. In hippocampal subfields analysis, we observed significantly higher SPC in gray matter volume of the left CA1, molecular layer HP, and CA3 with a trend for increased gray matter volume in most other areas. No significant changes were found for the hippocampal subfields in the right hemisphere. Analysis of the subcortical structures revealed significantly increased volume in the right thalamus in the meditation group. The results of the study point out that long-term meditation practice in persons with MCI or mild AD leads to salutary changes in cortical thickness and gray matter volumes. Most of these changes were observed in the brain areas related to executive control and memory that are prominently at risk in neurodegenerative diseases.

DNA Methylation and Protein Markers of Chronic Inflammation and Their Associations With Brain and Cognitive Aging

BACKGROUND AND OBJECTIVES

To investigate chronic inflammation in relation to cognitive aging by comparison of an epigenetic and serum biomarker of C-reactive protein and their associations with neuroimaging and cognitive outcomes.

METHODS

At baseline, participants (n = 521) were cognitively normal, around 73 years of age (mean 72.4, SD 0.716), and had inflammation, vascular risk (cardiovascular disease history, hypertension, diabetes, smoking, alcohol consumption, body mass index), and neuroimaging (structural and diffusion MRI) data available. Baseline inflammatory status was quantified by a traditional measure of peripheral inflammation-serum C-reactive protein (CRP)-and an epigenetic measure (DNA methylation [DNAm] signature of CRP). Linear models were used to examine the inflammation-brain health associations; mediation analyses were performed to interrogate the relationship between chronic inflammation, brain structure, and cognitive functioning.

RESULTS

We demonstrate that DNAm CRP shows significantly (on average 6.4-fold) stronger associations with brain health outcomes than serum CRP. DNAm CRP is associated with total brain volume (β = -0.197, 95% confidence interval [CI] -0.28 to -0.12, p FDR = 8.42 × 10-6), gray matter volume (β = -0.200, 95% CI -0.28 to -0.12, p FDR = 1.66 × 10-5), and white matter volume (β = -0.150, 95% CI -0.23 to -0.07, p FDR = 0.001) and regional brain atrophy. We also find that DNAm CRP has an inverse association with global and domain-specific (speed, visuospatial, and memory) cognitive functioning and that brain structure partially mediates this CRP-cognitive association (up to 29.7%), dependent on lifestyle and health factors.

DISCUSSION

These results support the hypothesis that chronic inflammation may contribute to neurodegenerative brain changes that underlie differences in cognitive ability in later life and highlight the potential of DNAm proxies for indexing chronic inflammatory status.

CLASSIFICATION OF EVIDENCE

This study provides Class II evidence that a DNAm signature of CRP levels is more strongly associated with brain health outcomes than serum CRP levels.

Data-Driven vs Consensus Diagnosis of MCI: Enhanced Sensitivity for Detection of Clinical, Biomarker, and Neuropathologic Outcomes

BACKGROUND AND OBJECTIVES

Given prior work demonstrating that mild cognitive impairment (MCI) can be empirically differentiated into meaningful cognitive subtypes, we applied actuarial methods to comprehensive neuropsychological data from the University of California San Diego Alzheimer's Disease Research Center (ADRC) in order to identify cognitive subgroups within ADRC participants without dementia and to examine cognitive, biomarker, and neuropathologic trajectories.

METHODS

Cluster analysis was performed on baseline neuropsychological data (n = 738; mean age 71.8). Survival analysis examined progression to dementia (mean follow-up 5.9 years). CSF Alzheimer disease (AD) biomarker status and neuropathologic findings at follow-up were examined in a subset with available data.

RESULTS

Five clusters were identified: optimal cognitively normal (CN; n = 130) with above-average cognition, typical CN (n = 204) with average cognition, nonamnestic MCI (naMCI; n = 104), amnestic MCI (aMCI; n = 216), and mixed MCI (mMCI; n = 84). Progression to dementia differed across MCI subtypes (mMCI > aMCI > naMCI), with the mMCI group demonstrating the highest rate of CSF biomarker positivity and AD pathology at autopsy. Actuarial methods classified 29.5% more of the sample with MCI and outperformed consensus diagnoses in capturing those who had abnormal biomarkers, progressed to dementia, or had AD pathology at autopsy.

DISCUSSION

We identified subtypes of MCI and CN with differing cognitive profiles, clinical outcomes, CSF AD biomarkers, and neuropathologic findings over more than 10 years of follow-up. Results demonstrate that actuarial methods produce reliable cognitive phenotypes, with data from a subset suggesting unique biological and neuropathologic signatures. Findings indicate that data-driven algorithms enhance diagnostic sensitivity relative to consensus diagnosis for identifying older adults at risk for cognitive decline.

Robust Discovery of Mild Cognitive Impairment Subtypes and Their Risk of Alzheimer's Disease Conversion Using Unsupervised Machine Learning and Gaussian Mixture Modeling

BACKGROUND

Alzheimer's Disease (AD) is an irreversible, progressive brain disorder that slowly destroys memory and thinking skills. The ability to correctly predict the diagnosis of Alzheimer's disease in its earliest stages can help physicians make more informed clinical decisions on therapy plans.

OBJECTIVE

This study aimed to determine whether the unsupervised discovering of latent classes of subjects with Mild Cognitive Impairment (MCI) may be useful in finding different prodromal AD stages and/or subjects with a low MCI to AD conversion risk.

METHODS

Total 18 features relevant to the MCI to AD conversion process led to the identification of 681 subjects with early MCI. Subjects were divided into training (70%) and validation (30%) sets. Subjects from the training set were analyzed using consensus clustering, and Gaussian Mixture Models (GMM) were used to describe the latent classes. The discovered GMM predicted the latent class of the validation set. Finally, descriptive statistics, rates of conversion, and Odds Ratios (OR) were computed for each discovered class.

RESULTS

Through consensus clustering, we discovered three different clusters among MCI subjects. The three clusters were associated with low-risk (OR = 0.12, 95%CI = 0.04 to 0.3|), medium-risk (OR = 1.33, 95%CI = 0.75 to 2.37), and high-risk (OR = 3.02, 95%CI = 1.64 to 5.57) of converting from MCI to AD, with the high-risk and low-risk groups highly contrasting. Hence, prodromal AD subjects were present in only two clusters.

CONCLUSION

We successfully discovered three different latent classes among MCI subjects with varied risks of MCI-to-AD conversion through consensus clustering. Two of the discovered classes may represent two different prodromal presentations of Alzheimer´s disease.

Latent Profile Analysis of Cognition in a Non-Demented Diverse Cohort: A Focus on Modifiable Cardiovascular and Lifestyle Factors

BACKGROUND

Cognitively-defined subgroups are well-documented within neurodegeneration.

OBJECTIVE

We examined such profiles in diverse non-demented older adults and considered how resulting subgroups relate to modifiable factors associated with neurodegeneration.

METHODS

121 non-demented (MMSE = 28.62) diverse (46%non-Latino Black, 40%non-Latino White, 15%Latino) community-dwelling adults (age = 67.7 years) completed cognitive, cardiovascular, physical activity, and diet evaluations. Latent profile analyses (LPA) employed six cognitive scores (letter fluency, letter-number sequencing, confrontational naming, 'animal' fluency, list-learning delayed recall, and recognition discriminability) to characterize cognitively-defined subgroups. Differences between resulting subgroups on cardiovascular (composite scores of overall health; specific health components including fasting blood levels) and lifestyle (sedentary behavior; moderate-to-vigorous physical activity; Mediterranean diet consumption) factors were examined using ANCOVAs adjusting for relevant confounders.

RESULTS

Based on sample means across cognitive scores, LPA resulted in the following cognitive subgroups: 1) high-average cognition, 55%non-Latino White and 64%female participants; 2) average cognition, 58%non-Latino Black and 68%male participants; 3) lower memory, 58%non-Latino Black participants; and 4) lower executive functioning, 70%Latinos. The high-average subgroup reported significantly higher Mediterranean diet consumption than the average subgroup (p = 0.001). The lower executive functioning group had higher fasting glucose and hemoglobin A1c than all other subgroups (p-values<0.001).

CONCLUSION

LPA revealed two average subgroups reflecting level differences in cognition previously reported between non-Latino White and Black adults, and two lower cognition subgroups in domains similar to those documented in neurodegeneration. These subgroups, and their differences, suggest the importance of considering social determinants of health in cognitive aging and modifiable risk.

Addressing neuropsychological diagnostics in adults with epilepsy: Introducing the International Classification of Cognitive Disorders in Epilepsy: The IC CODE Initiative

This paper addresses the absence of an international diagnostic taxonomy for cognitive disorders in patients with epilepsy. Initiated through the 2020 Memorandum of Understanding between the International League Against Epilepsy and the International Neuropsychological Society, neuropsychological representatives from both organizations met to address the problem and consequences of the absence of an international diagnostic taxonomy for cognitive disorders in epilepsy, overview potential solutions, and propose specific solutions going forward. The group concluded that a classification of cognitive disorders in epilepsy, including an overall taxonomy and associated operational criteria, was clearly lacking and sorely needed. This paper reviews the advantages and shortcomings of four existing cognitive diagnostic approaches, including taxonomies derived from the US National Neuropsychology Network, DSM-V Neurocognitive Disorders, the Mild Cognitive Impairment classification from the aging/preclinical dementia literature, and the Research Domain Criteria Initiative. We propose a framework to develop a consensus-based classification system for cognitive disorders in epilepsy that will be international in scope and be applicable for clinical practice and research globally and introduce the International Classification of Cognitive Disorders in Epilepsy (IC-CODE) project.

Cognitive and MRI trajectories for prediction of Alzheimer's disease

The concept of Mild Cognitive Impairment (MCI) is used to describe the early stages of Alzheimer's disease (AD), and identification and treatment before further decline is an important clinical task. We selected longitudinal data from the ADNI database to investigate how well normal function (HC, n= 134) vs. conversion to MCI (cMCI, n= 134) and stable MCI (sMCI, n=333) vs. conversion to AD (cAD, n= 333) could be predicted from cognitive tests, and whether the predictions improve by adding information from magnetic resonance imaging (MRI) examinations. Features representing trajectories of change in the selected cognitive and MRI measures were derived from mixed effects models and used to train ensemble machine learning models to classify the pairs of subgroups based on a subset of the data set. Evaluation in an independent test set showed that the predictions for HC vs. cMCI improved substantially when MRI features were added, with an increase in [Formula: see text]-score from 60 to 77%. The [Formula: see text]-scores for sMCI vs. cAD were 77% without and 78% with inclusion of MRI features. The results are in-line with findings showing that cognitive changes tend to manifest themselves several years after the Alzheimer's disease is well-established in the brain.

Distinct Patterns of Brain Atrophy associated with Mild Behavioral Impairment in Cognitively Normal Elderly Adults

A cross-sectional study was conducted to evaluate patterns of gray matter changes in cognitively normal elderly adults with mild behavioral impairment (MBI). Sixteen MBI patients and 18 healthy controls were selected. All the participants underwent a neuropsychological assessment battery, including the Mini-mental State Examination (MMSE), Geriatric Depression Scale (GDS), Self-rating Anxiety Scale (SAS), and Chinese version of the mild behavioral impairment-checklist scale (MBI-C), and magnetic resonance imaging (MRI) scans. Imaging data was analyzed based on voxel-based morphometry (VBM). There was no significant difference in age, gender, MMSE score, total intracranial volume, white matter hyperdensity, gray matter volume, white matter volume between the two groups (p > 0.05). MBI group had shorter education years and higher MBI-C score, GDS and SAS scores than the normal control group (p < 0.05). For neuroimaging analysis, compared to the normal control group, the MBI group showed decreased volume in the left brainstem, right temporal transverse gyrus, left superior temporal gyrus, left inferior temporal gyrus, left middle temporal gyrus, right occipital pole, right thalamus, left precentral gyrus and left middle frontal gyrus(uncorrected p < 0.001). The grey matter regions correlated with the MBI-C score included the left postcentral gyrus, right exterior cerebellum, and left superior frontal gyrus. This suggests a link between MBI and decreased grey matter volume in cognitively normal elderly adults. Atrophy in the left frontal cortex and right thalamus in MBI patients is in line with frontal-subcortical circuit deficits, which have been linked to neuropsychiatric symptoms (NPS) in dementia. These initial results imply that MBI might be an early harbinger for subsequent cognitive decline and dementia.

Brainstem Volumetric Integrity in Preclinical and Prodromal Alzheimer's Disease

BACKGROUND

Neuropathological studies have suggested the tau pathology observed in Alzheimer's disease (AD) originates in brainstem nuclei, but no studies to date have quantified brainstem volumes in clinical populations with biomarker-confirmed mild cognitive impairment (MCI) or dementia due to AD or determined the value of brainstem volumetrics in predicting dementia.

OBJECTIVE

The present study examined whether MRI-based brainstem volumes differ among cognitively normal older adults and those with MCI or dementia due to AD and whether preclinical brainstem volumes predict future progression to dementia.

METHODS

Alzheimer's Disease Neuroimaging Initiative participants (N = 1,629) underwent baseline MRI scanning with variable clinical follow-up (6-120 months). Region of interest and voxel-based morphometric methods assessed brainstem volume differences among cognitively normal (n = 814), MCI (n = 542), and AD (n = 273) participants, as well as subsets of cerebrospinal fluid biomarker-confirmed MCI (n = 203) and AD (n = 160) participants.

RESULTS

MCI and AD cases showed smaller midbrain volumes relative to cognitively normal participants when normalizing to whole brainstem volume, and showed smaller midbrain, locus coeruleus, pons, and whole brainstem volumes when normalizing to total intracranial volume. Cognitively normal individuals who later progressed to AD dementia diagnosis exhibited smaller baseline midbrain volumes than individuals who did not develop dementia, and voxel-wise analyses revealed specific volumetric reduction of the locus coeruleus.

CONCLUSION

Findings are consistent with neuropathological observations of early AD-related pathology in brainstem nuclei and further suggest the clinical relevance of brainstem substructural volumes in preclinical and prodromal AD.