Logopenic and nonfluent variants of primary progressive aphasia are differentiated by acoustic measures of speech production

Differentiation of logopenic (lvPPA) and nonfluent/agrammatic (nfvPPA) variants of Primary Progressive Aphasia is important yet remains challenging since it hinges on expert based evaluation of speech and language production. In this study acoustic measures of speech in conjunction with voxel-based morphometry were used to determine the success of the measures as an adjunct to diagnosis and to explore the neural basis of apraxia of speech in nfvPPA. Forty-one patients (21 lvPPA, 20 nfvPPA) were recruited from a consecutive sample with suspected frontotemporal dementia. Patients were diagnosed using the current gold-standard of expert perceptual judgment, based on presence/absence of particular speech features during speaking tasks. Seventeen healthy age-matched adults served as controls. MRI scans were available for 11 control and 37 PPA cases; 23 of the PPA cases underwent amyloid ligand PET imaging. Measures, corresponding to perceptual features of apraxia of speech, were periods of silence during reading and relative vowel duration and intensity in polysyllable word repetition. Discriminant function analyses revealed that a measure of relative vowel duration differentiated nfvPPA cases from both control and lvPPA cases (r2 = 0.47) with 88% agreement with expert judgment of presence of apraxia of speech in nfvPPA cases. VBM analysis showed that relative vowel duration covaried with grey matter intensity in areas critical for speech motor planning and programming: precentral gyrus, supplementary motor area and inferior frontal gyrus bilaterally, only affected in the nfvPPA group. This bilateral involvement of frontal speech networks in nfvPPA potentially affects access to compensatory mechanisms involving right hemisphere homologues. Measures of silences during reading also discriminated the PPA and control groups, but did not increase predictive accuracy. Findings suggest that a measure of relative vowel duration from of a polysyllable word repetition task may be sufficient for detecting most cases of apraxia of speech and distinguishing between nfvPPA and lvPPA

Associations of awareness of age-related change with emotional and physical well-being: a systematic review and meta-analysis

This is the final version. Available on open access from OUP via the DOI in this recordBACKGROUND AND OBJECTIVES: This systematic review aimed to synthesize and quantify the associations of awareness of age-related change (AARC) with emotional well-being, physical well-being, and cognitive functioning. RESEARCH DESIGN AND METHODS: We conducted a systematic review with a correlational random effects meta-analysis. We included quantitative studies, published from January 1, 2009 to October 3, 2018, exploring associations between AARC and one or more of the following outcomes: emotional well-being, physical well-being, and cognitive functioning. We assessed heterogeneity (I2) and publication bias. RESULTS: We included 12 studies in the review, 9 exploring the association between AARC and emotional well-being and 11 exploring the association between AARC and physical well-being. No study explored the association between AARC and cognitive functioning. Six articles were included in the meta-analysis. We found a moderate association between a higher level of AARC gains and better emotional well-being (r = .33; 95% CI 0.18, 0.47; p <.001; I2 = 76.01) and between a higher level of AARC losses and poorer emotional (r = -.31; 95% CI -0.38, -0.24; p < .001; I2 = 0.00) and physical well-being (r = -.38; 95% CI -0.51, -0.24; p < .001; I2 = 83.48). We found a negligible association between AARC gains and physical well-being (r = .08; 95% CI 0.02, 0.14; p < .122; I2 = 0.00). Studies were of medium to high methodological quality. DISCUSSION AND IMPLICATIONS: There is some indication that AARC gains and losses can play a role in emotional well-being and that AARC losses are associated with physical well-being. However, the number of included studies is limited and there was some indication of heterogeneity. PROSPERO REGISTRATION: CRD42018111472.University of Exeter College of Life and Environmental Sciences (School of Psychology)University of Exeter College of Medicine and Healt

A review of RCTs in four medical journals to assess the use of imputation to overcome missing data in quality of life outcomes

Background: Randomised controlled trials (RCTs) are perceived as the gold-standard method for evaluating healthcare interventions, and increasingly include quality of life (QoL) measures. The observed results are susceptible to bias if a substantial proportion of outcome data are missing. The review aimed to determine whether imputation was used to deal with missing QoL outcomes. Methods: A random selection of 285 RCTs published during 2005/6 in the British Medical Journal, Lancet, New England Journal of Medicine and Journal of American Medical Association were identified. Results: QoL outcomes were reported in 61 (21%) trials. Six (10%) reported having no missing data, 20 (33%) reported ≤ 10% missing, eleven (18%) 11%–20% missing, and eleven (18%) reported >20% missing. Missingness was unclear in 13 (21%). Missing data were imputed in 19 (31%) of the 61 trials. Imputation was part of the primary analysis in 13 trials, but a sensitivity analysis in six. Last value carried forward was used in 12 trials and multiple imputation in two. Following imputation, the most common analysis method was analysis of covariance (10 trials). Conclusion: The majority of studies did not impute missing data and carried out a complete-case analysis. For those studies that did impute missing data, researchers tended to prefer simpler methods of imputation, despite more sophisticated methods being available.The Health Services Research Unit is funded by the Chief Scientist Office of the Scottish Government Health Directorate. Shona Fielding is also currently funded by the Chief Scientist Office on a Research Training Fellowship (CZF/1/31)

A review of RCTs in four medical journals to assess the use of imputation to overcome missing data in quality of life outcomes

Peer reviewedPublisher PD

Reinforcement learning or active inference?

This paper questions the need for reinforcement learning or control theory when optimising behaviour. We show that it is fairly simple to teach an agent complicated and adaptive behaviours using a free-energy formulation of perception. In this formulation, agents adjust their internal states and sampling of the environment to minimize their free-energy. Such agents learn causal structure in the environment and sample it in an adaptive and self-supervised fashion. This results in behavioural policies that reproduce those optimised by reinforcement learning and dynamic programming. Critically, we do not need to invoke the notion of reward, value or utility. We illustrate these points by solving a benchmark problem in dynamic programming; namely the mountain-car problem, using active perception or inference under the free-energy principle. The ensuing proof-of-concept may be important because the free-energy formulation furnishes a unified account of both action and perception and may speak to a reappraisal of the role of dopamine in the brain

Refractive Status at Birth: Its Relation to Newborn Physical Parameters at Birth and Gestational Age

Refractive status at birth is related to gestational age. Preterm babies have myopia which decreases as gestational age increases and term babies are known to be hypermetropic. This study looked at the correlation of refractive status with birth weight in term and preterm babies, and with physical indicators of intra-uterine growth such as the head circumference and length of the baby at birth.All babies delivered at St. Stephens Hospital and admitted in the nursery were eligible for the study. Refraction was performed within the first week of life. 0.8% tropicamide with 0.5% phenylephrine was used to achieve cycloplegia and paralysis of accommodation. 599 newborn babies participated in the study. Data pertaining to the right eye is utilized for all the analyses except that for anisometropia where the two eyes were compared. Growth parameters were measured soon after birth. Simple linear regression analysis was performed to see the association of refractive status, (mean spherical equivalent (MSE), astigmatism and anisometropia) with each of the study variables, namely gestation, length, weight and head circumference. Subsequently, multiple linear regression was carried out to identify the independent predictors for each of the outcome parameters.Simple linear regression showed a significant relation between all 4 study variables and refractive error but in multiple regression only gestational age and weight were related to refractive error. The partial correlation of weight with MSE adjusted for gestation was 0.28 and that of gestation with MSE adjusted for weight was 0.10. Birth weight had a higher correlation to MSE than gestational age.This is the first study to look at refractive error against all these growth parameters, in preterm and term babies at birth. It would appear from this study that birth weight rather than gestation should be used as criteria for screening for refractive error, especially in developing countries where the incidence of intrauterine malnutrition is higher

Cross-sectional association between objective cognitive performance and perceived age-related gains and losses in cognition

This is the final version. Available on open access from Cambridge University Press via the DOI in this recordAvailability of data and materials: This study was conducted using secondary data collected as part of the UK version of the PROTECT ongoing study. PROTECT data are available to investigators outside the PROTECT team after request and approval by the PROTECT Steering Committee. Data for the AARC questionnaires will be available from May 2022.Objectives: Evidence linking subjective concerns about cognition with poorer objective cognitive performance is limited by reliance on unidimensional measures of self-perceptions of aging. We used the awareness of age-related change construct to assess self-perceptions of both positive and negative age-related changes (AARC gains and losses). We tested whether AARC has greater utility in linking self-perceptions to objective cognition compared to well-established measures of self-perceptions of cognition and aging. We examined the associations of AARC with objective cognition, several psychological variables, and engagement in cognitive training. Design: Cross-sectional observational study. Participants: The sample comprised 6,056 cognitively healthy participants (Mean(SD) age= 66.0(7.0) years); divided into sub-groups representing middle, early old, and advanced old age. Measurements: We used an online cognitive battery and measures of global AARC, AARC specific to the cognitive domain, subjective cognitive change, attitudes toward own aging, subjective age, depression, anxiety, self-rated health. Results: Scores on the AARC measures showed stronger associations with objective cognition compared to other measures of self-perceptions of cognition and aging. Higher AARC gains were associated with poorer cognition in middle and early old age. Higher AARC losses and poorer cognition were associated across all sub-groups. Higher AARC losses were associated with greater depression and anxiety, more negative self-perceptions of aging, poorer self-rated health, but not with engagement in cognitive training. Conclusions: Assessing both positive and negative self-perceptions of cognition and aging is important when linking self-perceptions to cognitive functioning. Objective cognition is one of the many variables -alongside psychological variables- related to perceived cognitive lossesUniversity of ExeterNational Institute for Health Research (NIHR

Hierarchical Models in the Brain

This paper describes a general model that subsumes many parametric models for continuous data. The model comprises hidden layers of state-space or dynamic causal models, arranged so that the output of one provides input to another. The ensuing hierarchy furnishes a model for many types of data, of arbitrary complexity. Special cases range from the general linear model for static data to generalised convolution models, with system noise, for nonlinear time-series analysis. Crucially, all of these models can be inverted using exactly the same scheme, namely, dynamic expectation maximization. This means that a single model and optimisation scheme can be used to invert a wide range of models. We present the model and a brief review of its inversion to disclose the relationships among, apparently, diverse generative models of empirical data. We then show that this inversion can be formulated as a simple neural network and may provide a useful metaphor for inference and learning in the brain