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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.

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.

Numerous studies have established that estimated brain age, as derived from statistical models trained on healthy populations, constitutes a valuable biomarker that is predictive of cognitive decline and various neurological diseases. In this work, we curate a large-scale heterogeneous dataset (N = 10,158, age range 18 - 97) of structural brain MRIs in a healthy population from multiple publicly-available sources, upon which we train a deep learning model for brain age estimation. The availability of the large-scale dataset enables a more uniform age distribution across adult life-span for effective age estimation with no bias toward certain age groups. We demonstrate that the age estimation accuracy, evaluated with mean absolute error (MAE) and correlation coefficient (r), outperforms previously reported methods in both a hold-out test set reflective of the custom population (MAE = 4.06 years, r = 0.970) and an independent life-span evaluation dataset (MAE = 4.21 years, r = 0.960) on which a previous study has evaluated. We further demonstrate the utility of the estimated age in life-span aging analysis of cognitive functions. Furthermore, we conduct extensive ablation tests and employ feature-attribution techniques to analyze which regions contribute the most predictive value, demonstrating the prominence of the frontal lobe as well as pattern shift across life-span. In summary, we achieve age estimation performance comparable to previous studies but with a more heterogenous dataset confirming the efficacy of deep learning and the added utility of training with data both in larger number and more uniformly distributed than in previous studies. We demonstrate the regional contribution to our brain age predictions through multiple routes and confirm the association of divergence between estimated and chronological brain age with neuropsychological measures.

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.

Despite the growing body of evidence suggesting that alcohol consumption is associated with an increased risk of and poorer treatment outcomes from pneumonia, little is known about the association between alcohol control policy and pneumonia mortality. As such, this study aimed to assess the impact of three alcohol control policies legislated in 2008, 2017 and 2018 in Lithuania on sex-specific pneumonia mortality rates among individuals 15+ years of age. An interrupted time-series analysis using a generalised additive mixed model was performed for each policy. Of the three policies, only the 2008 policy resulted in a significant slope change (i.e. decline) in pneumonia mortality rates among males; no significant slope change was observed among females. The low R2 values for all sex-specific models suggest that other external factors are likely also influencing the sex-specific pneumonia mortality rates in Lithuania. Overall, the findings from this study suggest alcohol control policy's targeting affordability may be an effective way to reduce pneumonia mortality rates, among males in particular. However, further research is needed to fully explore their impact.

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).

Detecting abnormal activities in real-world surveillance videos is an important yet challenging task as the prior knowledge about video anomalies is usually limited or unavailable. Despite that many approaches have been developed to resolve this problem, few of them can capture the normal spatio-temporal patterns effectively and efficiently. Moreover, existing works seldom explicitly consider the local consistency at frame level and global coherence of temporal dynamics in video sequences. To this end, we propose Convolutional Transformer based Dual Discriminator Generative Adversarial Networks (CT-D2GAN) to perform unsupervised video anomaly detection. Specifically, we first present a convolutional transformer to perform future frame prediction. It contains three key components, i.e., a convolutional encoder to capture the spatial information of the input video clips, a temporal self-attention module to encode the temporal dynamics, and a convolutional decoder to integrate spatio-temporal features and predict the future frame. Next, a dual discriminator based adversarial training procedure, which jointly considers an image discriminator that can maintain the local consistency at frame-level and a video discriminator that can enforce the global coherence of temporal dynamics, is employed to enhance the future frame prediction. Finally, the prediction error is used to identify abnormal video frames. Thoroughly empirical studies on three public video anomaly detection datasets, i.e., UCSD Ped2, CUHK Avenue, and Shanghai Tech Campus, demonstrate the effectiveness of the proposed adversarial spatio-temporal modeling framework., Accepted for publication in the 29th ACM International Conference on Multimedia (ACMMM '21)

Introduction & Objective: Given that the impact of regulatory and public policy initiatives cannot usually be tested through traditional randomized controlled trial designs, well-selected, -designed, and -analyzed natural experiments are the method of choice when examining the effects of such enactments on a variety of outcomes. The classic methodology for such evaluations is interrupted time-series (ITS) analysis, which is considered as one of the quasi-experimental designs that use both pre- and post-policy data without randomization. This study tests the impact of alcohol control interventions implemented in different period of times on suicide mortality rates among people 25-74 years of age using ITS. Methods: We mainly use the generalized additive mixed model (GAMM) to capture trend and seasonality in suicide mortality rates while controlling for unemployment rates, financial crisis during 2007-2008, and legal alcohol consumption records. Given the notable differences in alcohol consumption and suicide mortality between males and females, all analyses are conducted gender-specifically. Results: The ITS shows that the intervention introduced in 2017 has a significant effect on reducing suicide mortality rates for males between 25 and 74. Following the implementation of the intervention, suicide mortality rates decreased by 23.8% (95% CI: 10.2% - 35.4%) on average. Conclusion: The alcohol control intervention that strictly increased the excise tax on alcohol products has been shown to have a strong impact on reducing suicide mortality rates among male adults 25-74 years of age. ITS analyses are one of the strongest evaluative designs and allow a more detailed assessment of the longitudinal impact of an intervention than may be possible with a randomized control trial.

Current neuroimaging techniques provide paths to investigate the structure and function of the brain in vivo and have made great advances in understanding Alzheimer's disease (AD). However, the group-level analyses prevalently used for investigation and understanding of the disease are not applicable for diagnosis of individuals. More recently, deep learning, which can efficiently analyze large-scale complex patterns in 3D brain images, has helped pave the way for computer-aided individual diagnosis by providing accurate and automated disease classification. Great progress has been made in classifying AD with deep learning models developed upon increasingly available structural MRI data. The lack of scale-matched functional neuroimaging data prevents such models from being further improved by observing functional changes in pathophysiology. Here we propose a potential solution by first learning a structural-to-functional transformation in brain MRI, and further synthesizing spatially matched functional images from large-scale structural scans. We evaluated our approach by building computational models to discriminate patients with AD from healthy normal subjects and demonstrated a performance boost after combining the structural and synthesized functional brain images into the same model. Furthermore, our regional analyses identified the temporal lobe to be the most predictive structural-region and the parieto-occipital lobe to be the most predictive functional-region of our model, which are both in concordance with previous group-level neuroimaging findings. Together, we demonstrate the potential of deep learning with large-scale structural and synthesized functional MRI to impact AD classification and to identify AD's neuroimaging signatures., Published in IEEE ISBI 2021. Available at https://ieeexplore.ieee.org/document/9433808

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.

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.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.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 Aβ = 0.702) or tau pathology (CSF tau = 0.682), and the findings are interpreted in the context of AD's known anatomical biology.The advantages of using deep learning to extract biomarker information from conventional MRIs extend practically, potentially reducing patient burden, risk, and cost.

While MRI contrast agents such as those based on Gadolinium are needed to enhance the detection of structural and functional brain lesions, there are rising concerns over their safety. Here, we hypothesize that a deep learning model, trained using quantitative steady-state contrast-enhanced MRI datasets in mice and humans, could generate contrast-equivalent information from a single non-contrast MRI scan. The model was first trained, optimized, and validated in mice. It was then transferred and adapted to human data, and we find that it can substitute Gadolinium-based contrast agents for detecting functional lesions caused by aging, Schizophrenia, or Alzheimer’s disease, and, for enhancing structural lesions caused by brain or breast tumors. Since derived from a commonly-acquired MRI, this framework has the potential for broad clinical utility and can be applied retrospectively to research scans across a host of diseases.

In neuroimaging studies, the human cortex is commonly modelled as a sphere to preserve the topological structure of the cortical surface. In this work, we explore the analysis of the human cortex using spherical CNN in an Alzheimer's disease (AD) classification task, using morphometric measures derived from T1-weighted structural MRI. Our results show superior performance in classifying AD versus cognitively normal subjects and in predicting mild cognitive impairment progression within two years. This work demonstrates the feasibility and superiority of the spherical CNN directly applied on the spherical representation in the discriminative analysis of the human cortex and could be readily extended to other imaging modalities and other neurological diseases.

Cerebral blood volume (CBV) is a hemodynamic correlate of oxygen metabolism and reflects brain activity and function. High-resolution CBV maps can be generated using the steady-state gadolinium-enhanced MRI technique. Such a technique requires an intravenous injection of exogenous gadolinium based contrast agent (GBCA) and recent studies suggest that the GBCA can accumulate in the brain after frequent use. We hypothesize that endogenous sources of contrast might exist within the most conventional and commonly acquired structural MRI, potentially obviating the need for exogenous contrast. Here, we test this hypothesis by developing and optimizing a deep learning algorithm, which we call DeepContrast, in mice. We find that DeepContrast performs equally well as exogenous GBCA in mapping CBV of the normal brain tissue and enhancing glioblastoma. Together, these studies validate our hypothesis that a deep learning approach can potentially replace the need for GBCAs in brain MRI.

In this paper, we give some characterizations of 𝓠-regular semigroups and show that the class of 𝓠-regular semigroups is closed under the direct product and homomorphic images. Furthermore, we characterize the 𝓠-subdirect products of this class of semigroups and study the E-unitary 𝓠-regular covers for 𝓠-regular semigroups, in particular for those whose maximum group homomorphic image is a given group. As an application of these results, we claim that the similar results on V-regular semigroups also hold.

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., Highlights • Clinically obtained structural MRI parameters overlap with contrast enhanced CBV MRI. • Intensity differences can be corrected using white matter signal. • CBV in unilateral TLE suggest metabolic but not structural ipsilateral changes. • Subiculum implicated as potential driver of unilateral TLE metabolic deficit. • Functional metrics can be potentially extracted from millions of clinical brain MRIs.

In this paper, we study the ordered regular equivalence relations on ordered semihypergroups in detail. To begin with, we introduce the concept of weak pseudoorders on an ordered semihypergroup, and investigate several related properties. In particular, we construct an ordered regular equivalence relation on an ordered semihypergroup by a weak pseudoorder. As an application of the above result, we completely solve the open problem on ordered semihypergroups introduced in [B. Davvaz, P. Corsini and T. Changphas, Relationship between ordered semihypergroups and ordered semigroups by using pseuoorders, European J. Combinatorics 44 (2015), 208–217]. Furthermore, we establish the relationships between ordered regular equivalence relations and weak pseudoorders on an ordered semihypergroup, and give some homomorphism theorems of ordered semihypergroups, which are generalizations of similar results in ordered semigroups.

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.

SUMMARY 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., Graphical abstract, In brief Trans-entorhinal cortex neurons are most vulnerable to Alzheimer’s disease. Simoes et al. explain this vulnerability by showing that these neurons are dependent on a distinct VPS26b-retromer core differentially dedicated to endosomal recycling. VPS26b is highly expressed in these neurons, where they regulate synaptic function, GluA1/SORL1 recycling, and disease-associated pathologies

In this paper, we study the ordered *-semigroups in terms of fuzzy subsets in detail and define a unary operation *on the set of all the fuzzy subsets of an ordered *-semigroup S, which is a key notion to introduce the concepts of prime, weakly prime and semiprime fuzzy ideals of S. Furthermore, we establish the relationships among these three types of fuzzy ideals and give some characterizations of intra-regular ordered *-semigroups in terms of fuzzy ideals. Finally, we define and study the fuzzy filters of an ordered *-semigroup. In particular, we discuss the relationships between the filters and the fuzzy filters in ordered *-semigroups.

Deep learning applied to MRI for Alzheimer’s classification is hypothesized to improve if the deep learning model implicates disease’s pathophysiology. The challenge in testing this hypothesis is that large-scale data are required to train this type of model. Here, we overcome this challenge by using a novel data augmentation strategy and show that our MRI-based deep learning model classifies Alzheimer’s dementia with high accuracy. Moreover, a class activation map was found dominated by signal from the hippocampal formation, a site where Alzheimer’s pathophysiology begins. Next, we tested the model’s performance in prodromal Alzheimer’s when patients present with mild cognitive impairment (MCI). We retroactively dichotomized a large cohort of MCI patients who were followed for up to 10 years into those with and without prodromal Alzheimer’s at baseline and used the dementia-derived model to generate individual ‘deep learning MRI’ scores. We compared the two groups on these scores, and on other biomarkers of amyloid pathology, tau pathology, and neurodegeneration. The deep learning MRI scores outperformed nearly all other biomarkers, including—unexpectedly—biomarkers of amyloid or tau pathology, in classifying prodromal disease and in predicting clinical progression. Providing a mechanistic explanation, the deep learning MRI scores were found to be linked to regional tau pathology, through investigations using cross-sectional, longitudinal, premortem and postmortem data. Our findings validate that a disease’s known pathophysiology can improve the design and performance of deep learning models. Moreover, by showing that deep learning can extract useful biomarker information from conventional MRIs, the advantages of this model extend practically, potentially reducing patient burden, risk, and cost.

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.

Whether and how endosomal trafficking, a pathogenic pathway in Alzheimer’s, is linked to the disease’s anatomical vulnerability remains unknown. Here, we address these questions by focusing on retromer, a protein complex central to endosomal trafficking and associated with the disease. VPS26 is the only protein of retromer’s core with two paralogs, VPS26a and VPS26b. Using neuronal cultures, we first find that each paralog forms a separate retromer core, and show that VPS26b differentially localizes to, and functions in, the recycling endosome. Next, we used neuroimaging to show that the trans-entorhinal cortex, a subregion most vulnerable to Alzheimer’s, is differentially affected by VPS26b depletion, a finding validated by electrophysiology, glutamate-receptor immunostaining, and behavior. Finally, VPS26b is found differentially enriched in the human trans-entorhinal cortex and deficient in the disease. These results suggest a general mechanism in explaining Alzheimer’s regional vulnerability and clarifies how best to therapeutically target the disease’s trafficking defects.

It is difficult for the current extraction and comparison target-hunting mode to be suitable for the selection of multiple targets in the offshore combat of anti-ship missiles, so the solution is hence put forward that applies the nearest-distance selector to target-hunting. Typical target-hunting situations are set to establish the target-hunting model. Explicit expression of capture probability of this nearest-distance selector is derived on the basis of reasonable simplification. The numerical simulation in typical situations demonstrates that, the performance of predetermined target-hunting is greatly improved by the distance criterion adopted in this solution, which sets the extracted multiple targets in order in terms of priority. Finally, impact extents of such factors as area of search, number of extracted objects, and target dispersion error are quantitatively evaluated. The insufficiency of using alone the method of selection of multiple nearest-distance targets is indicated with appropriate improvement actions proposed correspondingly.

In this paper, we introduce GP-po-flatness property of S-posets over a pomonoid S, which lies strictly between principal weak po-flatness and po-torsion freeness. Furthermore, we investigate the homological classification problems of pomonoids by using this new property. Finally, we consider direct products of GP-po-flat S-posets. As an application, characterizations of pomonoids over which direct products of nonempty families of principally weakly po-flat S-posets are principally weakly po-flat are obtained, and some results of Khosravi, R. in a certain extent are generalized.