The Method of Moral Hypothesis

Moral philosophy has become interested again in particular, substantive questions of right and wrong. In an effort to divine answers to such questions, philosophers often employ the following method: general rules are floated as potential principles of morality; the principles are regarded as confirmed insofar as they match our pre-theoretical intuitions about particular cases; and otherwise infirmed. Such principles, if sufficiently confirmed, are then used to overturn other, ‘aberrant’ moral intuitions that do not square with the rule. The aim of this work is to indict this ‘method of moral hypothesis’, and with it the moral theory project which relies on it. I argue that the method trades on an unsustainable picture of moral epistemology; that the motivations for engaging in it are without merit; and that its attractions as a systematizing tool are illusory. In chapter one, I examine some recent ‘etiological’ skeptical challenges to moral knowledge; and argue that such challenges succeed only against a particular sort of moral epistemology—the kind to which the moral theory project is wedded. I conclude that we should reject this epistemology, and the project with it. Chapter two aims to vindicate the charges of Pessimists about moral testimony—those who claim that testimony cannot transmit moral knowledge. I argue that one barrier to moral-knowledge transmission by testimony is its inability to transfer moral-conceptual ‘know-how’; more generally that the ‘Humean reasons’ which support testimony are insufficient to support moral knowledge; and that, for parallel reasons, the theory project cannot produce moral knowledge. Chapter three attacks a picture of justification which makes the theory project seem pressing. In its place, I argue for an alternative picture, on which justification is infected with certain pragmatic, contextual factors. This alternative undermines one of the motivations for the theory project: finding an ultimate justification for our moral beliefs. In chapter four, I unify these arguments; and argue that, in general, we are correct to reject any summarizing principle which conflicts with a strongly held, pre-theoretical moral verdict. This negates one of the central ambitions of the theory project. Its other motivations are, I argue, equally misplaced

Automatic Spatial Estimation of White Matter Hyperintensities Evolution in Brain MRI using Disease Evolution Predictor Deep Neural Networks

Funds from the Indonesia Endowment Fund for Education (LPDP), Ministry of Finance, Republic of Indonesia (MFR); Row Fogo Charitable Trust (Grant No. BRO-D.FID3668413)(MCVH); Wellcome Trust (patient recruitment, scanning, primary study Ref No. WT088134/Z/09/A); Fondation Leducq (Perivascular Spaces Transatlantic Network of Excellence); EU Horizon 2020 (SVDs@Target); and the MRC UK Dementia Research Institute at the University of Edinburgh (Wardlaw programme) are gratefully acknowledged. The Titan Xp used for this research was donated by the NVIDIA Corporation.Peer reviewedPublisher PD

Reliability of an automatic classifier for brain enlarged perivascular spaces burden and comparison with human performance

pp. 1465-1481En el cerebro, los espacios perivasculares agrandados (PVS) se relacionan con la enfermedad de los vasos pequeños (SVD), mala cognición, inflamación e hipertensión. Proponemos un esquema totalmente automático que utiliza una máquina de vectores de soporte (SVM) para clasificar la carga de PVS en los ganglios basales (BG) como baja o alta. Evaluamos el rendimiento de tres tipos diferentes de descriptores extraídos de la región BG en imágenes de RMN ponderadas en T2: (I) estadísticas obtenidas de los coeficientes de la transformada de Wavelet, (II) patrones binarios locales y (III) bolsa de palabras visuales (BoW), descriptores basados en la caracterización de claves locales obtenidas de una rejilla densa con las características de transformación de la función de escala-invariante (SIFT). Cuando se utilizaron estos últimos, el SVM clasificador alcanzó la mejor precisión (81,16%). Lo obtenido del clasificador utilizando los descriptores del BoW se comparó con las calificaciones visuales realizadas por un neurorradiólogo experimentado (observador 1) y por un analista de imágenes entrenado (observador 2). El acuerdo y la correlación cruzada entre el clasificador y el observador 2 (κ = 0,67 (0,58 – 0,76)) fueron ligeramente más altos que entre el clasificador y el observador 1 (κ = 0,62 (0,53 – 0,72)) y entre ambos observadores (κ = 0,68 (0,61 – 0,75)). Por último, se construyeron tres modelos de regresión logística que utilizan variables clínicas como variable independiente y cada una de las clasificaciones de PVS como variable dependiente, para evaluar clínicamente lo significativas que resultan las predicciones del clasificador. El ajuste del modelo para el clasificador era bueno (área bajo la curva (AUC) valores: 0,93 (modelo 1), 0,90 (modelo 2) y 0,92 (modelo 3)) y un poco mejor (es decir, valores de AUC: 0,02 unidades superiores) que las del modelo para el observador 2. Estos resultados sugieren que, aunque se puede mejorar, un clasificador automático para evaluar la carga de PVS de la resonancia magnética del cerebro puede proporcionar resultados clínicamente significativos cercanos a los de un observador entrenado.S

Analysis of dynamic texture and spatial spectral descriptors of dynamic contrast-enhanced brain magnetic resonance images for studying small vessel disease

Cerebral small vessel disease (SVD) comprises various pathological processes affecting small brain vessels and damaging white and grey matter. In this paper, we propose a framework comprising region of interest sampling, dynamic spectral and texture description, functional principal component analysis, and statistical analysis to study exogenous contrast agent distribution over time in various brain regions in patients with recent mild stroke and SVD features.We compared our results against current semi-quantitative surrogates of dysfunction such as signal enhancement area and slope. Biological sex, stroke lesion type and overall burden of white matter hyperintensities (WMH) were significant predictors of intensity, spectral, and texture features extracted from the ventricular region (p-value < 0.05), explaining between a fifth and a fourth of the data variance (0.20 ≤Adj.R2 ≤ 0.25). We observed that spectral feature reflected more the dysfunction compared to other descriptors since the overall WMH burden explained consistently the power spectra variability in blood vessels, cerebrospinal fluid, deep grey matter and white matter. Our preliminary results show the potential of the framework for the analysis of dynamic contrast-enhanced brain magnetic resonance imaging acquisitions in SVD since significant variation in our metrics was related to the burden of SVD features. Therefore, our proposal may increase sensitivity to detect subtle features of small vessel dysfunction. A public version of the code will be released on our research website

Tracer kinetic assessment of blood–brain barrier leakage and blood volume in cerebral small vessel disease: Associations with disease burden and vascular risk factors

Funding Information: The authors disclosed receipt of the following financial support for the research, authorship and/or publication of this article: Wellcome Trust [grant number WT088134/Z/09/A ; SDJM, FC]; Row Fogo Charitable Trust (MCVH, FC, AKH, PAA); Scottish Funding Council Scottish Imaging Network A Platform for Scientific Excellence collaboration (JMW); NHS Lothian R + D Department (MJT); the UK Dementia Research Institute which receives its funding from DRI Ltd, funded by the UK MRC, Alzheimer’s Research UK and the Alzheimer’s Society (MS, FC, ES, JMW); the Fondation Leducq Transatlantic Network of Excellence for the Study of Perivascular Spaces in Small Vessel Disease [reference number 16 CVD 05] (MS); and European Union Horizon 2020 [project number 666881, SVDs@Target] (MS, FC). We acknowledge the participants, their relatives, and carers for their participation in this study, and the staff of NHS Lothian Stroke Services and Brain Research Imaging Centre Edinburgh for their assistance in recruiting and assessing the patients.Peer reviewedPublisher PD

Identification of the presence of ischaemic stroke lesions by means of texture analysis on brain magnetic resonance images

Study funding This work was funded by the Row Fogo Charitable Trust (MVH, VGC) grant no. BRO-D.FID3668413, and the Wellcome Trust (patient recruitment, scanning, primary study Ref No. 088134/Z/09). The study was conducted independently of the funders who do not hold the data and did not participate in the study design or analyses. The Lothian Birth Cohort 1936 is funded by Age UK (Disconnected Mind grant) and the Medical Research Council (MRC; MR/M01311/1, G1001245, 82800), and the latter supported BSA. IJD was supported by the Centre for Cognitive Ageing and Cognitive Epidemiology, which is funded by the MRC and the Biotechnology and Biological Sciences Research Council (MR/K026992/1). David Moratal acknowledges financial support from the Spanish Ministerio de Economía y Competitividad (MINECO) and FEDER funds under Grant BFU2015-64380-C2-2-R, and from the Conselleria d'Educació, Investigació, Cultura i Esport, Generalitat Valenciana (grants AEST/2017/013 and AEST/2018/021). Rafael Ortiz-Ramón was supported by grant ACIF/2015/078 and grant BEFPI/2017/004 from the Conselleria d’Educació, Investigació, Cultura i Esport of the Valencian Community (Spain).Peer reviewedPublisher PD

Integrity of normal-appearing white matter: influence of age, visible lesion burden and hypertension in patients with small vessel disease

White matter hyperintensities accumulate with age and occur in patients with stroke, but their pathogenesis is poorly understood. We measured multiple magnetic resonance imaging biomarkers of tissue integrity in normal-appearing white matter and white matter hyperintensities in patients with mild stroke, to improve understanding of white matter hyperintensities origins. We classified white matter into white matter hyperintensities and normal-appearing white matter and measured fractional anisotropy, mean diffusivity, water content (T1-relaxation time) and blood–brain barrier leakage (signal enhancement slope from dynamic contrast-enhanced magnetic resonance imaging). We studied the effects of age, white matter hyperintensities burden (Fazekas score) and vascular risk factors on each biomarker, in normal-appearing white matter and white matter hyperintensities, and performed receiver-operator characteristic curve analysis. Amongst 204 patients (34.3–90.9 years), all biomarkers differed between normal-appearing white matter and white matter hyperintensities (P < 0.001). In normal-appearing white matter and white matter hyperintensities, mean diffusivity and T1 increased with age (P < 0.001), all biomarkers varied with white matter hyperintensities burden (P < 0.001; P = 0.02 signal enhancement slope), but only signal enhancement slope increased with hypertension (P = 0.028). Fractional anisotropy showed complex age-white matter hyperintensities-tissue interactions; enhancement slope showed white matter hyperintensities-tissue interactions. Mean diffusivity distinguished white matter hyperintensities from normal-appearing white matter best at all ages. Blood–brain barrier leakage increases with hypertension and white matter hyperintensities burden at all ages in normal-appearing white matter and white matter hyperintensities, whereas water mobility and content increase as tissue damage accrues, suggesting that blood–brain barrier leakage mediates small vessel disease-related brain damage

Blood-brain barrier failure as a core mechanism in cerebral small vessel disease and dementia: evidence from a cohort study

Introduction: Small vessel disease (SVD) is a common contributor to dementia. Subtle blood-brain barrier (BBB) leakage may be important in SVD-induced brain damage. Methods: We assessed imaging, clinical variables, and cognition in patients with mild (i.e., nondisabling) ischemic lacunar or cortical stroke. We analyzed BBB leakage, interstitial fluid, and white matter integrity using multimodal tissue-specific spatial analysis around white matter hyperintensities (WMH). We assessed predictors of 1 year cognition, recurrent stroke, and dependency. Results: In 201 patients, median age 67 (range 34–97), BBB leakage, and interstitial fluid were higher in WMH than normal-appearing white matter; leakage in normal-appearing white matter increased with proximity to WMH (P , .0001), with WMH severity (P 5 .033), age (P 5 .03), and hypertension (P , .0001). BBB leakage in WMH predicted declining cognition at 1 year. Discussion: BBB leakage increases in normal-appearing white matter with WMH and predicts worsening cognition. Interventions to reduce BBB leakage may prevent SVD-associated dementia

Reliability of an automatic classifier for brain enlarged perivascular spaces burden and comparison with human performance

In the brain, enlarged perivascular spaces (PVS) relate to cerebral small vessel disease (SVD), poor cognition, inflammation and hypertension. We propose a fully automatic scheme that uses a support vector machine (SVM) to classify the burden of PVS in the basal ganglia (BG) region as low or high. We assess the performance of three different types of descriptors extracted from the BG region in T2-weighted MRI images: (i) statistics obtained from Wavelet transform’s coefficients, (ii) local binary patterns and (iii) bag of visual words (BoW) based descriptors characterizing local keypoints obtained from a dense grid with the scale-invariant feature transform (SIFT) characteristics. When the latter were used, the SVM classifier achieved the best accuracy (81.16%). The output from the classifier using the BoW descriptors was compared with visual ratings done by an experienced neuroradiologist (Observer 1) and by a trained image analyst (Observer 2). The agreement and cross-correlation between the classifier and Observer 2 (κ = 0.67 (0.58–0.76)) were slightly higher than between the classifier and Observer 1 (κ = 0.62 (0.53–0.72)) and comparable between both the observers (κ = 0.68 (0.61–0.75)). Finally, three logistic regression models using clinical variables as independent variable and each of the PVS ratings as dependent variable were built to assess how clinically meaningful were the predictions of the classifier. The goodness-of-fit of the model for the classifier was good (area under the curve (AUC) values: 0.93 (model 1), 0.90 (model 2) and 0.92 (model 3)) and slightly better (i.e. AUC values: 0.02 units higher) than that of the model for Observer 2. These results suggest that, although it can be improved, an automatic classifier to assess PVS burden from brain MRI can provide clinically meaningful results close to those from a trained observer