Moral Distress: Its Impact on Nursing

Purpose: This study is designed to provide the learner with information to aid in identification of and intervention for moral distress in the practice environment. The purpose of this study is educational in nature. It is not intended to provide clinical or legal advice or to be a comprehensive compendium on evidence-based practice. For specific implementation information, please contact an appropriate professional, organization, legal resource, or facility policy. Objectives 1. Define moral distress. 2. Describe ways nurses can proactively address moral distress in the healthcare environment

Do Invariances in Deep Neural Networks Align with Human Perception?

An evaluation criterion for safe and trustworthy deep learning is how well the invariances captured by representations of deep neural networks (DNNs) are shared with humans. We identify challenges in measuring these invariances. Prior works used gradient-based methods to generate identically represented inputs (IRIs), i.e., inputs which have identical representations (on a given layer) of a neural network, and thus capture invariances of a given network. One necessary criterion for a network's invariances to align with human perception is for its IRIs look “similar” to humans. Prior works, however, have mixed takeaways; some argue that later layers of DNNs do not learn human-like invariances yet others seem to indicate otherwise. We argue that the loss function used to generate IRIs can heavily affect takeaways about invariances of the network and is the primary reason for these conflicting findings. We propose an adversarial regularizer on the IRI-generation loss that finds IRIs that make any model appear to have very little shared invariance with humans. Based on this evidence, we argue that there is scope for improving models to have human-like invariances, and further, to have meaningful comparisons between models one should use IRIs generated using the regularizer-free loss. We then conduct an in-depth investigation of how different components (e.g. architectures, training losses, data augmentations) of the deep learning pipeline contribute to learning models that have good alignment with humans. We find that architectures with residual connections trained using a (self-supervised) contrastive loss with `p ball adversarial data augmentation tend to learn invariances that are most aligned with humans. Code: github.com/nvedant07/Human-NN-Alignment. We strongly recommend reading the arxiv version of this paper: https://arxiv.org/abs/2111.14726

New insights into atypical Alzheimer's disease in the era of biomarkers

Most patients with Alzheimer's disease present with amnestic problems; however, a substantial proportion, over-represented in young-onset cases, have atypical phenotypes including predominant visual, language, executive, behavioural, or motor dysfunction. In the past, these individuals often received a late diagnosis; however, availability of CSF and PET biomarkers of Alzheimer's disease pathologies and incorporation of atypical forms of Alzheimer's disease into new diagnostic criteria increasingly allows them to be more confidently diagnosed early in their illness. This early diagnosis in turn allows patients to be offered tailored information, appropriate care and support, and individualised treatment plans. These advances will provide improved access to clinical trials, which often exclude atypical phenotypes. Research into atypical Alzheimer's disease has revealed previously unrecognised neuropathological heterogeneity across the Alzheimer's disease spectrum. Neuroimaging, genetic, biomarker, and basic science studies are providing key insights into the factors that might drive selective vulnerability of differing brain networks, with potential mechanistic implications for understanding typical late-onset Alzheimer's disease

Interpretation of Brain Morphology in Association to Alzheimer's Disease Dementia Classification Using Graph Convolutional Networks on Triangulated Meshes

We propose a mesh-based technique to aid in the classification of Alzheimer's disease dementia (ADD) using mesh representations of the cortex and subcortical structures. Deep learning methods for classification tasks that utilize structural neuroimaging often require extensive learning parameters to optimize. Frequently, these approaches for automated medical diagnosis also lack visual interpretability for areas in the brain involved in making a diagnosis. This work: (a) analyzes brain shape using surface information of the cortex and subcortical structures, (b) proposes a residual learning framework for state-of-the-art graph convolutional networks which offer a significant reduction in learnable parameters, and (c) offers visual interpretability of the network via class-specific gradient information that localizes important regions of interest in our inputs. With our proposed method leveraging the use of cortical and subcortical surface information, we outperform other machine learning methods with a 96.35% testing accuracy for the ADD vs. healthy control problem. We confirm the validity of our model by observing its performance in a 25-trial Monte Carlo cross-validation. The generated visualization maps in our study show correspondences with current knowledge regarding the structural localization of pathological changes in the brain associated to dementia of the Alzheimer's type.Comment: Accepted for the Shape in Medical Imaging (ShapeMI) workshop at MICCAI International Conference 202

Longitudinal grey and white matter changes in frontotemporal dementia and Alzheimer's disease

Behavioural variant frontotemporal dementia (bvFTD) and Alzheimer's disease (AD) dementia are characterised by progressive brain atrophy. Longitudinal MRI volumetry may help to characterise ongoing structural degeneration and support the differential diagnosis of dementia subtypes. Automated, observer-independent atlas-based MRI volumetry was applied to analyse 102 MRI data sets from 15 bvFTD, 14 AD, and 10 healthy elderly control participants with consecutive scans over at least 12 months. Anatomically defined targets were chosen a priori as brain structures of interest. Groups were compared regarding volumes at clinic presentation and annual change rates. Baseline volumes, especially of grey matter compartments, were significantly reduced in bvFTD and AD patients. Grey matter volumes of the caudate and the gyrus rectus were significantly smaller in bvFTD than AD. The bvFTD group could be separated from AD on the basis of caudate volume with high accuracy (79% cases correct). Annual volume decline was markedly larger in bvFTD and AD than controls, predominantly in white matter of temporal structures. Decline in grey matter volume of the lateral orbitofrontal gyrus separated bvFTD from AD and controls. Automated longitudinal MRI volumetry discriminates bvFTD from AD. In particular, greater reduction of orbitofrontal grey matter and temporal white matter structures after 12 months is indicative of bvFTD

Predicting progression of mild cognitive impairment to dementia using neuropsychological data: a supervised learning approach using time windows

Background: Predicting progression from a stage of Mild Cognitive Impairment to dementia is a major pursuit in current research. It is broadly accepted that cognition declines with a continuum between MCI and dementia. As such, cohorts of MCI patients are usually heterogeneous, containing patients at different stages of the neurodegenerative process. This hampers the prognostic task. Nevertheless, when learning prognostic models, most studies use the entire cohort of MCI patients regardless of their disease stages. In this paper, we propose a Time Windows approach to predict conversion to dementia, learning with patients stratified using time windows, thus fine-tuning the prognosis regarding the time to conversion. Methods: In the proposed Time Windows approach, we grouped patients based on the clinical information of whether they converted (converter MCI) or remained MCI (stable MCI) within a specific time window. We tested time windows of 2, 3, 4 and 5 years. We developed a prognostic model for each time window using clinical and neuropsychological data and compared this approach with the commonly used in the literature, where all patients are used to learn the models, named as First Last approach. This enables to move from the traditional question "Will a MCI patient convert to dementia somewhere in the future" to the question "Will a MCI patient convert to dementia in a specific time window". Results: The proposed Time Windows approach outperformed the First Last approach. The results showed that we can predict conversion to dementia as early as 5 years before the event with an AUC of 0.88 in the cross-validation set and 0.76 in an independent validation set. Conclusions: Prognostic models using time windows have higher performance when predicting progression from MCI to dementia, when compared to the prognostic approach commonly used in the literature. Furthermore, the proposed Time Windows approach is more relevant from a clinical point of view, predicting conversion within a temporal interval rather than sometime in the future and allowing clinicians to timely adjust treatments and clinical appointments.FCT under the Neuroclinomics2 project [PTDC/EEI-SII/1937/2014, SFRH/BD/95846/2013]; INESC-ID plurianual [UID/CEC/50021/2013]; LASIGE Research Unit [UID/CEC/00408/2013

The influence of semantic and phonological factors on syntactic decisions: An event-related brain potential study

During language production and comprehension, information about a word's syntactic properties is sometimes needed. While the decision about the grammatical gender of a word requires access to syntactic knowledge, it has also been hypothesized that semantic (i.e., biological gender) or phonological information (i.e., sound regularities) may influence this decision. Event-related potentials (ERPs) were measured while native speakers of German processed written words that were or were not semantically and/or phonologically marked for gender. Behavioral and ERP results showed that participants were faster in making a gender decision when words were semantically and/or phonologically gender marked than when this was not the case, although the phonological effects were less clear. In conclusion, our data provide evidence that even though participants performed a grammatical gender decision, this task can be influenced by semantic and phonological factors

Cortical microstructure in primary progressive aphasia: a multicenter study

Cortical mean diffusivity is a novel imaging metric sensitive to early changes in neurodegenerative syndromes. Higher cortical mean diffusivity values reflect microstructural disorganization and have been proposed as a sensitive biomarker that might antedate macroscopic cortical changes. We aimed to test the hypothesis that cortical mean diffusivity is more sensitive than cortical thickness to detect cortical changes in primary progressive aphasia (PPA).In this multicenter, case-control study, we recruited 120 patients with PPA (52 non-fluent, 31 semantic, and 32 logopenic variants; and 5 GRN-related PPA) as well as 89 controls from three centers. The 3-Tesla MRI protocol included structural and diffusion-weighted sequences. Disease severity was assessed with the Clinical Dementia Rating scale. Cortical thickness and cortical mean diffusivity were computed using a surface-based approach.The comparison between each PPA variant and controls revealed cortical mean diffusivity increases and cortical thinning in overlapping regions, reflecting the canonical loci of neurodegeneration of each variant. Importantly, cortical mean diffusivity increases also expanded to other PPA-related areas and correlated with disease severity in all PPA groups. Cortical mean diffusivity was also increased in patients with very mild PPA when only minimal cortical thinning was observed and showed a good correlation with measures of disease severity.Cortical mean diffusivity shows promise as a sensitive biomarker for the study of the neurodegeneration-related microstructural changes in PPA.© 2022. The Author(s)

Anatomical and Functional Deficits in Patients with Amnestic Mild Cognitive Impairment

Background: Anatomical and functional deficits have been studied in patients with amnestic mild cognitive impairment (MCI). However, it is unclear whether and how the anatomical deficits are related to the functional alterations. Present study aims to characterize the association between anatomical and functional deficits in MCI patients. Methods: Seventeen amnestic MCI patients and 18 healthy aging controls were scanned using a T1 Weighted MPRAGE sequence and a gradient-echo echo-planar imaging sequence. Clinical severity of MCI patients was evaluated by usin