Modelling time-varying risk factors of tooth loss: Results from joint model compared with extended Cox regression model.

AIM: To illustrate the use of joint models (JMs) for longitudinal and survival data in estimating risk factors of tooth loss as a function of time-varying endogenous periodontal biomarkers (probing pocket depth [PPD], alveolar bone loss [ABL] and mobility [MOB]). MATERIALS AND METHODS: We used data from the Veterans Affairs Dental Longitudinal Study, a longitudinal cohort study of over 30 years of follow-up. We compared the results from the JM with those from the extended Cox regression model which assumes that the time-varying covariates are exogenous. RESULTS: Our results showed that PPD is an important risk factor of tooth loss, but each model produced different estimates of the hazard. In the tooth-level analysis, based on the JM, the hazard of tooth loss increased by 4.57 (95% confidence interval [CI]: 2.13-8.50) times for a 1-mm increase in maximum PPD, whereas based on the extended Cox model, the hazard of tooth loss increased by 1.60 (95% CI: 1.37-1.87) times. CONCLUSIONS: JMs can incorporate time-varying periodontal biomarkers to estimate the hazard of tooth loss. As JMs are not commonly used in oral health research, we provide a comprehensive set of R codes and an example dataset to implement the method.

Impact of alcohol control policy on hemorrhagic and ischemic stroke mortality rates in Lithuania: An interrupted time series analysis. / Impacto de políticas de control de alcohol en las tasas de mortalidad por ictus hemorrágico e isquémico en Lituania: Análisis de series temporales interrumpidas.

Given the causal impact of alcohol use on stroke, alcohol control policies should presumably reduce stroke mortality rates. This study aimed to test the impact of three major Lithuanian alcohol control policies implemented in 2008, 2017 and 2018 on sex- and stroke subtype-specific mortality rates, among individuals 15+ years-old. Joinpoint regression analyses were performed for each sex- and stroke subtype-specific group to identify timepoints corresponding with significant changes in mortality rate trends. To estimate the impact of each policy, interrupted time series analyses using a generalized additive mixed model were performed on monthly sex- and stroke subtype-specific age-standardized mortality rates from January 2001-December 2018. Significant average annual percent decreases were found for all sex- and stroke subtype-specific mortality rate trends. The alcohol control policies were most impactful on ischemic stroke mortality rates among women. The 2008 policy was followed by a positive level change of 4,498 ischemic stroke deaths per 100,000 women and a negative monthly slope change of -0.048 ischemic stroke deaths per 100,000 women. Both the 2017 and 2018 policy enactment timepoints coincided with a significant negative level change for ischemic stroke mortality rates among women, at -0.901 deaths and -1.431 deaths per 100,000 population, respectively. Hemorrhagic stroke mortality among men was not affected by any of the policies, and hemorrhagic stroke mortality among women and ischemic stroke mortality among men were only associated with the 2008 policy. Our study findings suggest that the impact of alcohol control policies on stroke mortality may vary by sex and subtype.

Dado el impacto del alcohol en los ictus, las políticas de control de alcohol deberían reducir las tasas de mortalidad. Nuestro objetivo fue demostrar el impacto de tres importantes políticas lituanas implementadas en 2008, 2017 y 2018 en las tasas de mortalidad específicas por subtipo de ictus y sexo, en mayores de 15 años. Se realizaron análisis de regresión «joinpoint¼ para identificar los cambios de tendencia. Para estimar el impacto, se realizaron análisis de series temporales interrumpidas utilizando un modelo mixto aditivo generalizado en las tasas mensuales estandarizadas por edad, desde enero 2001 hasta diciembre 2018. Se encontraron disminuciones porcentuales anuales promedio significativas en ambos subtipos de ictus y por sexo. Las políticas tuvieron un mayor impacto en las tasas de mortalidad por ictus isquémico en mujeres. Posterior a la política del 2008, ocurrió un cambio positivo de 4,498 muertes por ictus isquémico por 100 000 mujeres y un cambio de pendiente mensual negativo de -0,048 muertes por ictus isquémico por 100 000 mujeres. Posterior a las políticas de 2017 y 2018, hubo un cambio de tendencia negativo significativo para la mortalidad por ictus isquémico en mujeres, de -0.901 muertes y -1.431 muertes por 100 000 habitantes, respectivamente. La mortalidad por ictus hemorrágico en hombres no se vio afectada, y la mortalidad por ictus hemorrágico en mujeres y por ictus isquémico en hombres solo se vio afectada por la política del 2008. Nuestros hallazgos sugieren que el impacto de las políticas en la mortalidad por ictus puede variar según sexo y subtipo.

Estimating effects of health policy interventions using interrupted time-series analyses: a simulation study.

BACKGROUND: A classic methodology used in evaluating the impact of health policy interventions is interrupted time-series (ITS) analysis, applying a quasi-experimental design that uses both pre- and post-policy data without randomization. In this paper, we took a simulation-based approach to estimating intervention effects under different assumptions. METHODS: Each of the simulated mortality rates contained a linear time trend, seasonality, autoregressive, and moving-average terms. The simulations of the policy effects involved three scenarios: 1) immediate-level change only, 2) immediate-level and slope change, and 3) lagged-level and slope change. The estimated effects and biases of these effects were examined via three matched generalized additive mixed models, each of which used two different approaches: 1) effects based on estimated coefficients (estimated approach), and 2) effects based on predictions from models (predicted approach). The robustness of these two approaches was further investigated assuming misspecification of the models. RESULTS: When one simulated dataset was analyzed with the matched model, the two analytical approaches produced similar estimates. However, when the models were misspecified, the number of deaths prevented, estimated using the predicted vs. estimated approaches, were very different, with the predicted approach yielding estimates closer to the real effect. The discrepancy was larger when the policy was applied early in the time-series. CONCLUSION: Even when the sample size appears to be large enough, one should still be cautious when conducting ITS analyses, since the power also depends on when in the series the intervention occurs. In addition, the intervention lagged effect needs to be fully considered at the study design stage (i.e., when developing the models).

Depression Is Associated With Preserved Cortical Thickness Relative to Apathy in Frontotemporal Dementia.

OBJECTIVES: To understand the differential neuroanatomical substrates underlying apathy and depression in Frontotemporal dementia (FTD). METHODS: T1-MRIs and clinical data of patients with behavioral and aphasic variants of FTD were obtained from an open database. Cortical thickness was derived, its association with apathy severity and difference between the depressed and not depressed were examined with appropriate covariates. RESULTS: Apathy severity was significantly associated with cortical thinning of the lateral parts of the right sided frontal, temporal and parietal lobes. The right sided orbitofrontal, parsorbitalis and rostral anterior cingulate cortex were thicker in depressed compared to patients not depressed. CONCLUSIONS: Greater thickness of right sided ventromedial and inferior frontal cortex in depression compared to patients without depression suggests a possible requisite of gray matter in this particular area for the manifestation of depression in FTD. This study demonstrates a method for deriving neuroanatomical patterns across non-harmonized neuroimaging data in a neurodegenerative disease.

Network-level enrichment provides a framework for biological interpretation of machine learning results.

Machine learning algorithms are increasingly being utilized to identify brain connectivity biomarkers linked to behavioral and clinical outcomes. However, research often prioritizes prediction accuracy at the expense of biological interpretability, and inconsistent implementation of ML methods may hinder model accuracy. To address this, our paper introduces a network-level enrichment approach, which integrates brain system organization in the context of connectome-wide statistical analysis to reveal network-level links between brain connectivity and behavior. To demonstrate the efficacy of this approach, we used linear support vector regression (LSVR) models to examine the relationship between resting-state functional connectivity networks and chronological age. We compared network-level associations based on raw LSVR weights to those produced from the forward and inverse models. Results indicated that not accounting for shared family variance inflated prediction performance, the k-best feature selection via Pearson correlation reduced accuracy and reliability, and raw LSVR model weights produced network-level associations that deviated from the significant brain systems identified by forward and inverse models. Our findings offer crucial insights for applying machine learning to neuroimaging data, emphasizing the value of network enrichment for biological interpretation.

A deep learning MRI approach outperforms other biomarkers of prodromal Alzheimer's disease.

BACKGROUND: The three core pathologies of Alzheimer's disease (AD) are amyloid pathology, tau pathology, and neurodegeneration. Biomarkers exist for each. Neurodegeneration is often detected by neuroimaging, and we hypothesized that a voxel-based deep learning approach using structural MRI might outperform other neuroimaging methods. METHODS: First, we implement an MRI-based deep learning model, trained with a data augmentation strategy, which classifies Alzheimer's dementia and generates class activation maps. Next, we tested the model in prodromal AD and compared its performance to other biomarkers of amyloid pathology, tau pathology, and neuroimaging biomarkers of neurodegeneration. RESULTS: The model distinguished between controls and AD with high accuracy (AUROC = 0.973) with class activation maps that localized to the hippocampal formation. As hypothesized, the model also outperformed other neuroimaging biomarkers of neurodegeneration in prodromal AD (AUROC = 0.788) but also outperformed biomarkers of amyloid (CSF Aß = 0.702) or tau pathology (CSF tau = 0.682), and the findings are interpreted in the context of AD's known anatomical biology. CONCLUSIONS: The advantages of using deep learning to extract biomarker information from conventional MRIs extend practically, potentially reducing patient burden, risk, and cost.

Deep learning of MRI contrast enhancement for mapping cerebral blood volume from single-modal non-contrast scans of aging and Alzheimer's disease brains.

While MRI contrast agents such as those based on Gadolinium are needed for high-resolution mapping of brain metabolism, these contrast agents require intravenous administration, and there are rising concerns over their safety and invasiveness. Furthermore, non-contrast MRI scans are more commonly performed than those with contrast agents and are readily available for analysis in public databases such as the Alzheimer's Disease Neuroimaging Initiative (ADNI). In this article, we hypothesize that a deep learning model, trained using quantitative steady-state contrast-enhanced structural MRI datasets, in mice and humans, can generate contrast-equivalent information from a single non-contrast MRI scan. The model was first trained, optimized, and validated in mice, and was then transferred and adapted to humans. We observe that the model can substitute for Gadolinium-based contrast agents in approximating cerebral blood volume, a quantitative representation of brain activity, at sub-millimeter granularity. Furthermore, we validate the use of our deep-learned prediction maps to identify functional abnormalities in the aging brain using locally obtained MRI scans, and in the brain of patients with Alzheimer's disease using publicly available MRI scans from ADNI. Since it is derived from a commonly-acquired MRI protocol, this framework has the potential for broad clinical utility and can also be applied retrospectively to research scans across a host of neurological/functional diseases.

The neurobiology of auditory and visual perceptual abnormalities in a clinical high-risk for psychosis cohort: A pilot morphometric magnetic resonance imaging study.

Our goal was to examine the neurobiology of auditory and visual perceptual abnormalities in individuals at clinical high-risk for psychosis (CHR) using morphometric magnetic resonance imaging (MRI). We enrolled 72 CHR subjects as delineated by the Structured Interview for Psychosis-Risk Syndromes (SIPS). Greater severity of visual perceptual abnormalities was associated with larger volumes in all regions tested (amygdala, hippocampus, and occipital cortex), while no relationships were observed between auditory perceptual abnormalities and brain volumes. These data support findings that while perceptual abnormalities may share a central set of neurobiological mechanisms, each type may also have distinct pathogeneses.

Deep learning improves utility of tau PET in the study of Alzheimer's disease.

INTRODUCTION: Positron emission tomography (PET) imaging targeting neurofibrillary tau tangles is increasingly used in the study of Alzheimer's disease (AD), but its utility may be limited by conventional quantitative or qualitative evaluation techniques in earlier disease states. Convolutional neural networks (CNNs) are effective in learning spatial patterns for image classification. METHODS: 18F-MK6240 (n = 320) and AV-1451 (n = 446) PET images were pooled from multiple studies. We performed iterations with differing permutations of radioligands, heuristics, and architectures. Performance was compared to a standard region of interest (ROI)-based approach on prediction of memory impairment. We visualized attention of the network to illustrate decision making. RESULTS: Overall, models had high accuracy (> 80%) with good average sensitivity and specificity (75% and 82%, respectively), and had comparable or higher accuracy to the ROI standard. Visualizations of model attention highlight known characteristics of tau radioligand binding. DISCUSSION: CNNs could improve tau PET's role in early disease and extend the utility of tau PET across generations of radioligands.

Insights into the role of diet and dietary flavanols in cognitive aging: results of a randomized controlled trial.

With the world's population aging, age-related memory decline is an impending cognitive epidemic. Assessing the impact of diet on cognitive aging, we conducted a controlled, randomized, parallel-arm dietary intervention with 211 healthy adults (50-75 years) investigating effects of either a placebo or 260, 510 and 770 mg/day of cocoa flavanols for 12-weeks followed by 8-weeks washout. The primary outcome was a newly-developed object-recognition task localized to the hippocampus' dentate gyrus. Secondary outcomes included a hippocampal-dependent list-learning task and a prefrontal cortex-dependent list-sorting task. The alternative Healthy Eating Index and a biomarker of flavanol intake (gVLM) were measured. In an MRI substudy, hippocampal cerebral blood volume was mapped. Object-recognition and list-sorting performance did not correlate with baseline diet quality and did not improve after flavanol intake. However, the hippocampal-dependent list-learning performance was directly associated with baseline diet quality and improved after flavanol intake, particularly in participants in the bottom tertile of baseline diet quality. In the imaging substudy, a region-of-interest analysis was negative but a voxel-based-analysis suggested that dietary flavanols target the dentate gyrus. While replication is needed, these findings suggest that diet in general, and dietary flavanols in particular, may be associated with memory function of the aging hippocampus and normal cognitive decline.

Alzheimer's vulnerable brain region relies on a distinct retromer core dedicated to endosomal recycling.

Whether and how the pathogenic disruptions in endosomal trafficking observed in Alzheimer's disease (AD) are linked to its anatomical vulnerability remain unknown. Here, we began addressing these questions by showing that neurons are enriched with a second retromer core, organized around VPS26b, differentially dedicated to endosomal recycling. Next, by imaging mouse models, we show that the trans-entorhinal cortex, a region most vulnerable to AD, is most susceptible to VPS26b depletion-a finding validated by electrophysiology, immunocytochemistry, and behavior. VPS26b was then found enriched in the trans-entorhinal cortex of human brains, where both VPS26b and the retromer-related receptor SORL1 were found deficient in AD. Finally, by regulating glutamate receptor and SORL1 recycling, we show that VPS26b can mediate regionally selective synaptic dysfunction and SORL1 deficiency. Together with the trans-entorhinal's unique network properties, hypothesized to impose a heavy demand on endosomal recycling, these results suggest a general mechanism that can explain AD's regional vulnerability.

Characterizing Alzheimer's Disease With Image and Genetic Biomarkers Using Supervised Topic Models.

Neuroimaging and genetic biomarkers have been widely studied from discriminative perspectives towards Alzheimer's disease (AD) classification, since neuroanatomical patterns and genetic variants are jointly critical indicators for AD diagnosis. Generative methods, designed to model common occurring patterns, could potentially advance the understanding of this disease, but have not been fully explored for AD characterization. Moreover, the introduction of a supervised component into the generative process can constrain the model for more discriminative characterization. In this study, we propose an original method based on supervised topic modeling to characterize AD from a generative perspective, yet maintaining discriminative power at differentiating disease populations. Our topic modeling jointly exploits discretized image features and categorical genetic features. Diagnostic information - cognitively normal (CN), mild cognitive impairment (MCI) and AD - is introduced as a supervision variable. Experimental results on the ADNI cohort demonstrate that our model, while achieving competitive discriminative performance, can discover topics revealing both well-known and novel neuroanatomical patterns including temporal, parietal and frontal regions; as well as associations between genetic factors and neuroanatomical patterns.

Estimating brain age based on a uniform healthy population with deep learning and structural magnetic resonance imaging.

Numerous studies have established that estimated brain age constitutes a valuable biomarker that is predictive of cognitive decline and various neurological diseases. In this work, we curate a large-scale brain MRI data set of healthy individuals, on which we train a uniform deep learning model for brain age estimation. We demonstrate an age estimation accuracy on a hold-out test set (mean absolute error = 4.06 years, r = 0.970) and an independent life span evaluation data set (mean absolute error = 4.21 years, r = 0.960). We further demonstrate the utility of the estimated age in a life span aging analysis of cognitive functions. In summary, we achieve age estimation performance comparable to previous studies, but with a more heterogenous data set confirming the efficacy of this deep learning framework. We also evaluated training with varying age distributions. The analysis of regional contributions to our brain age predictions through multiple analyses, and confirmation of the association of divergence between the estimated and chronological brain age with neuropsychological measures, may be useful in the development and evaluation of similar imaging biomarkers.

Brain regions vulnerable and resistant to aging without Alzheimer's disease.

'Normal aging' in the brain refers to age-related changes that occur independent of disease, in particular Alzheimer's disease. A major barrier to mapping normal brain aging has been the difficulty in excluding the earliest preclinical stages of Alzheimer's disease. Here, before addressing this issue we first imaged a mouse model and learn that the best MRI measure of dendritic spine loss, a known pathophysiological driver of normal aging, is one that relies on the combined use of functional and structural MRI. In the primary study, we then deployed the combined functional-structural MRI measure to investigate over 100 cognitively-normal people from 20-72 years of age. Next, to cover the tail end of aging, in secondary analyses we investigated structural MRI acquired from cognitively-normal people, 60-84 years of age, who were Alzheimer's-free via biomarkers. Collectively, the results from the primary functional-structural study, and the secondary structural studies revealed that the dentate gyrus is a hippocampal region differentially affected by aging, and that the entorhinal cortex is a region most resistant to aging. Across the cortex, the primary functional-structural study revealed and that the inferior frontal gyrus is differentially affected by aging, however, the secondary structural studies implicated other frontal cortex regions. Together, the results clarify how normal aging may affect the brain and has possible mechanistic and therapeutic implications.

Hippocampal Pathology in Clinical High-Risk Patients and the Onset of Schizophrenia.

BACKGROUND: We examined neuroimaging-derived hippocampal biomarkers in subjects at clinical high risk (CHR) for psychosis to further characterize the pathophysiology of early psychosis. We hypothesized that glutamate hyperactivity, reflected by increased metabolic activity derived from functional magnetic resonance imaging in the CA1 hippocampal subregion and from proton magnetic resonance spectroscopy-derived hippocampal levels of glutamate/glutamine, represents early hippocampal dysfunction in CHR subjects and is predictive of conversion to syndromal psychosis. METHODS: We enrolled 75 CHR individuals with attenuated positive symptom psychosis-risk syndrome as defined by the Structured Interview for Psychosis-risk Syndromes. We used optimized magnetic resonance imaging techniques to measure 3 validated in vivo pathologies of hippocampal dysfunction-focal cerebral blood volume, focal atrophy, and evidence of elevated glutamate concentrations. All patients were imaged at baseline and were followed for up to 2 years to assess for conversion to psychosis. RESULTS: At baseline, compared with control subjects, CHR individuals had high glutamate/glutamine and elevated focal cerebral blood volume on functional magnetic resonance imaging, but only baseline focal hippocampal atrophy predicted progression to syndromal psychosis. CONCLUSIONS: These findings provide evidence that CHR patients with attenuated psychotic symptoms have glutamatergic abnormalities, although only CHR patients who develop syndromal psychosis exhibit focal hippocampal atrophy. Furthermore, these results support the growing evidence that hippocampal dysfunction is an early feature of schizophrenia and related psychotic disorders.

Amygdalar volume and violent ideation in a sample at clinical high-risk for psychosis.

We previously demonstrated that violent ideation predicts both violent acts and eventual progression to syndromal psychosis in individuals at clinical high-risk for psychosis (CHR). We performed amygdalar surface morphometry analysis on MRI scans from 70 CHR individuals, 21 of whom had violent ideation, 49 of whom did not. CHR individuals with violent ideation have abnormal and asymmetric amygdalar volumes. These data suggest some commonalities in the genesis of violence and aggression among clinical populations, as well as that there may be specific neurobiological links between violence and psychosis.

Temporal lobe epilepsy lateralization using retrospective cerebral blood volume MRI.

Steady-state cerebral blood volume (CBV) is tightly coupled to regional cerebral metabolism, and CBV imaging is a variant of MRI that has proven useful in mapping brain dysfunction. CBV derived from exogenous contrast-enhanced MRI can generate sub-millimeter functional maps. Higher resolution helps to more accurately interrogate smaller cortical regions, such as functionally distinct regions of the hippocampus. Many MRIs have fortuitously adequate sequences required for CBV mapping. However, these scans vary substantially in acquisition parameters. Here, we determined whether previously acquired contrast-enhanced MRI scans ordered in patients with unilateral temporal lobe epilepsy can be used to generate hippocampal CBV. We used intrinsic reference regions to correct for intensity scaling on a research CBV dataset to identify white matter as a robust marker for scaling correction. Next, we tested the technique on a sample of unilateral focal epilepsy patients using clinical MRI scans. We find evidence suggestive of significant hypometabolism in the ipsilateral-hippocampus of unilateral TLE subjects. We also highlight the subiculum as a potential driver of this effect. This study introduces a technique that allows CBV maps to be generated retrospectively from clinical scans, potentially with broad application for mapping dysfunction throughout the brain.

Discriminative Localization in CNNs for Weakly-Supervised Segmentation of Pulmonary Nodules.

Automated detection and segmentation of pulmonary nodules on lung computed tomography (CT) scans can facilitate early lung cancer diagnosis. Existing supervised approaches for automated nodule segmentation on CT scans require voxel-based annotations for training, which are labor- and time-consuming to obtain. In this work, we propose a weakly-supervised method that generates accurate voxel-level nodule segmentation trained with image-level labels only. By adapting a convolutional neural network (CNN) trained for image classification, our proposed method learns discriminative regions from the activation maps of convolution units at different scales, and identifies the true nodule location with a novel candidate-screening framework. Experimental results on the public LIDC-IDRI dataset demonstrate that, our weakly-supervised nodule segmentation framework achieves competitive performance compared to a fully-supervised CNN-based segmentation method.

Enhanced effective connectivity in mild occipital stroke patients with hemianopia.

Plasticity-based spontaneous recovery and rehabilitation intervention of stroke-induced hemianopia have drawn great attention in recent years. However, the underlying neural mechanism remains unknown. This study aims to investigate brain network disruption and reorganization in hemianopia patients due to mild occipital stroke. Resting-state networks were constructed from 12 hemianopia patients with right occipital infarct by partial directed coherence analysis of multi-channel electroencephalograms. Compared with control subjects, the patients presented enhanced connectivity owing to newly formed connections. Compensational connections mostly originated from the peri-infarct area and targeted contralesional frontal, central, and parietal cortices. These new ipsilesional-to-contralesional inter-hemispheric connections coordinately presented significant correlation with the extent of vision loss. The enhancement of connectivity might be the neural substrate for brain plasticity in stroke-induced hemianopia and may shed light on plasticity-based recovery or rehabilitation.