Predictive Modelling using Neuroimaging Data in the Presence of Confounds

When training predictive models from neuroimaging data, we typically have available non-imaging variables such as age and gender that affect the imaging data but which we may be uninterested in from a clinical perspective. Such variables are commonly referred to as 'confounds'. In this work, we firstly give a working definition for confound in the context of training predictive models from samples of neuroimaging data. We define a confound as a variable which affects the imaging data and has an association with the target variable in the sample that differs from that in the population-of-interest, i.e., the population over which we intend to apply the estimated predictive model. The focus of this paper is the scenario in which the confound and target variable are independent in the population-of-interest, but the training sample is biased due to a sample association between the target and confound. We then discuss standard approaches for dealing with confounds in predictive modelling such as image adjustment and including the confound as a predictor, before deriving and motivating an Instance Weighting scheme that attempts to account for confounds by focusing model training so that it is optimal for the population-of-interest. We evaluate the standard approaches and Instance Weighting in two regression problems with neuroimaging data in which we train models in the presence of confounding, and predict samples that are representative of the population-of-interest. For comparison, these models are also evaluated when there is no confounding present. In the first experiment we predict the MMSE score using structural MRI from the ADNI database with gender as the confound, while in the second we predict age using structural MRI from the IXI database with acquisition site as the confound. Considered over both datasets we find that none of the methods for dealing with confounding gives more accurate predictions than a baseline model which ignores confounding, although including the confound as a predictor gives models that are less accurate than the baseline model. We do find, however, that different methods appear to focus their predictions on specific subsets of the population-of-interest, and that predictive accuracy is greater when there is no confounding present. We conclude with a discussion comparing the advantages and disadvantages of each approach, and the implications of our evaluation for building predictive models that can be used in clinical practice

A Weighted Prognostic Covariate Adjustment Method for Efficient and Powerful Treatment Effect Inferences in Randomized Controlled Trials

A crucial task for a randomized controlled trial (RCT) is to specify a statistical method that can yield an efficient estimator and powerful test for the treatment effect. A novel and effective strategy to obtain efficient and powerful treatment effect inferences is to incorporate predictions from generative artificial intelligence (AI) algorithms into covariate adjustment for the regression analysis of a RCT. Training a generative AI algorithm on historical control data enables one to construct a digital twin generator (DTG) for RCT participants, which utilizes a participant's baseline covariates to generate a probability distribution for their potential control outcome. Summaries of the probability distribution from the DTG are highly predictive of the trial outcome, and adjusting for these features via regression can thus improve the quality of treatment effect inferences, while satisfying regulatory guidelines on statistical analyses, for a RCT. However, a critical assumption in this strategy is homoskedasticity, or constant variance of the outcome conditional on the covariates. In the case of heteroskedasticity, existing covariate adjustment methods yield inefficient estimators and underpowered tests. We propose to address heteroskedasticity via a weighted prognostic covariate adjustment methodology (Weighted PROCOVA) that adjusts for both the mean and variance of the regression model using information obtained from the DTG. We prove that our method yields unbiased treatment effect estimators, and demonstrate via comprehensive simulation studies and case studies from Alzheimer's disease that it can reduce the variance of the treatment effect estimator, maintain the Type I error rate, and increase the power of the test for the treatment effect from 80% to 85%~90% when the variances from the DTG can explain 5%~10% of the variation in the RCT participants' outcomes.Comment: 49 pages, 6 figures, 12 table

Escalation of Tau Accumulation after a Traumatic Brain Injury: Findings from Positron Emission Tomography.

Traumatic brain injury (TBI) has come to be recognized as a risk factor for Alzheimer's disease (AD), with poorly understood underlying mechanisms. We hypothesized that a history of TBI would be associated with greater tau deposition in elders with high-risk for dementia. A Groups of 20 participants with self-reported history of TBI and 100 without any such history were scanned using [18F]-AV1451 positron emission tomography as part of the Alzheimer's Disease Neuroimaging Initiative (ADNI). Scans were stratified into four groups according to TBI history, and by clinical dementia rating scores into cognitively normal (CDR = 0) and those showing cognitive decline (CDR ≥ 0.5). We pursued voxel-based group comparison of [18F]-AV1451 uptake to identify the effect of TBI history on brain tau deposition, and for voxel-wise correlation analyses between [18F]-AV1451 uptake and different neuropsychological measures and cerebrospinal fluid (CSF) biomarkers. Compared to the TBI-/CDR ≥ 0.5 group, the TBI+/CDR ≥ 0.5 group showed increased tau deposition in the temporal pole, hippocampus, fusiform gyrus, and inferior and middle temporal gyri. Furthermore, the extent of tau deposition in the brain of those with TBI history positively correlated with the extent of cognitive decline, CSF-tau, and CSF-amyloid. This might suggest TBI to increase the risk for tauopathies and Alzheimer's disease later in life

The Impact of Dementia on Women Internationally: an Integrative Review

Women are disproportionately affected by dementia, both in terms of developing dementia and becoming caregivers. We conducted an integrative review of English language literature of the issues affecting women in relation to dementia from an international perspective. The majority of relevant studies were conducted in high income countries, and none were from low-income countries. The effects of caregiving on health, wellbeing and finances are greater for women; issues facing women, particularly in low and middle-income countries need to be better understood. Research should focus on building resilience to help people adjust and cope long term

What do adolescents perceive to be key features of an effective dementia education and awareness initiative?

The development of dementia friendly communities is a current global and national priority for the UK. As a response to policy, there have been a number of dementia awareness initiatives disseminated with the aim of reducing the stigma associated with a diagnosis of dementia. The inclusion of adolescents in such initiatives in imperative in order to sustain dementia friendly communities. With this is mind, the aim of this study was to establish the dementia education needs of adolescents and effective dissemination strategies to convey key messages. A total of 42 adolescents aged 12 to 18 years participated in eight focus group discussions. Key themes to emerge from discussions included: the importance of dementia awareness, topics of interest within dementia, preferred methods of learning, the inclusion of the person living with dementia and the use of social media. The findings of the study will enable the development of appropriate dementia awareness initiatives for adolescents and thus facilitate the sustainability of dementia friendly communities

Instantiated mixed effects modeling of Alzheimer's disease markers

The assessment and prediction of a subject's current and future risk of developing neurodegenerative diseases like Alzheimer's disease are of great interest in both the design of clinical trials as well as in clinical decision making. Exploring the longitudinal trajectory of markers related to neurodegeneration is an important task when selecting subjects for treatment in trials and the clinic, in the evaluation of early disease indicators and the monitoring of disease progression. Given that there is substantial intersubject variability, models that attempt to describe marker trajectories for a whole population will likely lack specificity for the representation of individual patients. Therefore, we argue here that individualized models provide a more accurate alternative that can be used for tasks such as population stratification and a subject-specific prognosis. In the work presented here, mixed effects modeling is used to derive global and individual marker trajectories for a training population. Test subject (new patient) specific models are then instantiated using a stratified “marker signature” that defines a subpopulation of similar cases within the training database. From this subpopulation, personalized models of the expected trajectory of several markers are subsequently estimated for unseen patients. These patient specific models of markers are shown to provide better predictions of time-to-conversion to Alzheimer's disease than population based models

Elevated homocysteine is associated with increased rates of epigenetic aging in a population with mild cognitive impairment

Elevated plasma total homocysteine (tHcy) is associated with the development of Alzheimer's disease and other forms of dementia. In this study, we report the relationship between tHcy and epigenetic age in older adults with mild cognitive impairment from the VITACOG study. Epigenetic age and rate of aging (ROA) were assessed using various epigenetic clocks, including those developed by Horvath and Hannum, DNAmPhenoAge, and with a focus on Index, a new principal component‐based epigenetic clock that, like DNAmPhenoAge, is trained to predict an individual's “PhenoAge.” We identified significant associations between tHcy levels and ROA, suggesting that hyperhomocysteinemic individuals were aging at a faster rate. Moreover, Index revealed a normalization of accelerated epigenetic aging in these individuals following treatment with tHcy‐lowering B‐vitamins. Our results indicate that elevated tHcy is a risk factor for accelerated epigenetic aging, and this can be ameliorated with B‐vitamins. These findings have broad relevance for the sizable proportion of the worldwide population with elevated tHcy

Health promotion intervention for people with early-stage dementia: A quasi-experimental study

Introduction: With the limited advancements in medical treatment, there is a growing need for supporting people with early‐stage dementia adjust to their diagnosis and improve their quality of life. This study aimed to investigate the effects of a 12‐week health promotion course for people with early‐stage dementia. Methods: Quasi‐experimental, single group, pretest‐posttest design. A total of 108 persons with dementia participated in this study, and for each participant, a carer was interviewed. The 12‐week health promotion intervention consisted of 2‐hr sessions at weekly intervals. Outcome measures were cognition, measured by Mini‐Mental State Examination, personal, and instrumental activities of daily living (P‐ADL and I‐ADL), measured by Lawton and Brody's Physical Self‐Maintenance Scale and Instrumental Activities of Daily Living Scale, self‐rated health, measured by the European Quality of life Visual Analogue Scale, depressive symptoms, measured by the Cornell Scale for Depression in Dementia, and neuropsychiatric symptoms, measured by The Neuropsychiatric Inventory. Assessments were conducted at baseline and at follow‐up 1–2 months postintervention. Results: The results demonstrate a small but statistically significant improvement in depressive symptoms (p = .015) and in self‐rated health (p = .031). The results also demonstrated a small statistically significant decline in the participants’ I‐ADL (p = .007). The participants’ cognitive function, P‐ADL, and neuropsychiatric symptoms were stable during the 4‐month follow‐up. Conclusion: This study demonstrates promising results with regard to the benefit of attending a 12‐week health promotion intervention in promoting health and well‐being in people with early‐stage dementia. With the majority of participants with early‐stage dementia living at home without any healthcare services in a vulnerable stage of the condition, this study makes an important contribution to highlighting the need for, and benefit of, educational approaches for this population.publishedVersio

Biological correlates of elevated soluble TREM2 in cerebrospinal fluid

Cerebrospinal fluid (CSF) soluble triggering receptor expressed on myeloid cells-2 (sTREM2) is an emerging biomarker of neuroinflammation in Alzheimer's disease (AD). Yet, sTREM2 expression has not been systematically evaluated in relation to concomitant drivers of neuroinflammation. While associations between sTREM2 and tau in CSF are established, we sought to determine additional biological correlates of CSF sTREM2 during the prodromal stages of AD by evaluating CSF Aβ species (Aβx-40), a fluid biomarker of blood-brain barrier integrity (CSF/plasma albumin ratio), and CSF biomarkers of neurodegeneration measured in 155 participants from the Vanderbilt Memory and Aging Project. A novel association between high CSF levels of both sTREM2 and Aβx-40 was observed and replicated in an independent dataset. Aβx-40 levels, as well as the CSF/plasma albumin ratio, explained additional and unique variance in sTREM2 levels above and beyond that of CSF biomarkers of neurodegeneration. The component of sTREM2 levels correlated with Aβx-40 levels best predicted future cognitive performance. We highlight potential contributions of Aβ homeostasis and blood-brain barrier integrity to elevated CSF sTREM2, underscoring novel biomarker associations relevant to disease progression and clinical outcome measures

Systematic comparison of different techniques to measure hippocampal subfield volumes in ADNI2

OBJECTIVE: Subfield-specific measurements provide superior information in the early stages of neurodegenerative diseases compared to global hippocampal measurements. The overall goal was to systematically compare the performance of five representative manual and automated T1 and T2 based subfield labeling techniques in a sub-set of the ADNI2 population. METHODS: The high resolution T2 weighted hippocampal images (T2-HighRes) and the corresponding T1 images from 106 ADNI2 subjects (41 controls, 57 MCI, 8 AD) were processed as follows. A. T1-based: 1. Freesurfer + Large-Diffeomorphic-Metric-Mapping in combination with shape analysis. 2. FreeSurfer 5.1 subfields using in-vivo atlas. B. T2-HighRes: 1. Model-based subfield segmentation using ex-vivo atlas (FreeSurfer 6.0). 2. T2-based automated multi-atlas segmentation combined with similarity-weighted voting (ASHS). 3. Manual subfield parcellation. Multiple regression analyses were used to calculate effect sizes (ES) for group, amyloid positivity in controls, and associations with cognitive/memory performance for each approach. RESULTS: Subfield volumetry was better than whole hippocampal volumetry for the detection of the mild atrophy differences between controls and MCI (ES: 0.27 vs 0.11). T2-HighRes approaches outperformed T1 approaches for the detection of early stage atrophy (ES: 0.27 vs.0.10), amyloid positivity (ES: 0.11 vs 0.04), and cognitive associations (ES: 0.22 vs 0.19). CONCLUSIONS: T2-HighRes subfield approaches outperformed whole hippocampus and T1 subfield approaches. None of the different T2-HghRes methods tested had a clear advantage over the other methods. Each has strengths and weaknesses that need to be taken into account when deciding which one to use to get the best results from subfield volumetry