A Bayesian Spatial Model to Predict Disease Status Using Imaging Data From Various Modalities

The neurobiology of functional neurological disorders characterised by impaired awareness

We review the neurobiology of Functional Neurological Disorders (FND), i.e., neurological disorders not explained by currently identifiable histopathological processes, in order to focus on those characterised by impaired awareness (functionally impaired awareness disorders, FIAD), and especially, on the paradigmatic case of Resignation Syndrome (RS). We thus provide an improved more integrated theory of FIAD, able to guide both research priorities and the diagnostic formulation of FIAD. We systematically address the diverse spectrum of clinical presentations of FND with impaired awareness, and offer a new framework for understanding FIAD. We find that unraveling the historical development of neurobiological theory of FIAD is of paramount importance for its current understanding. Then, we integrate contemporary clinical material in order to contextualise the neurobiology of FIAD within social, cultural, and psychological perspectives. We thus review neuro-computational insights in FND in general, to arrive at a more coherent account of FIAD. FIAD may be based on maladaptive predictive coding, shaped by stress, attention, uncertainty, and, ultimately, neurally encoded beliefs and their updates. We also critically appraise arguments in support of and against such Bayesian models. Finally, we discuss implications of our theoretical account and provide pointers towards an improved clinical diagnostic formulation of FIAD. We suggest directions for future research towards a more unified theory on which future interventions and management strategies could be based, as effective treatments and clinical trial evidence remain limited.

Geostatistical modeling of positive-definite matrices: An application to diffusion tensor imaging

Geostatistical modeling for continuous point-referenced data has extensively been applied to neuroimaging because it produces efficient and valid statistical inference. However, diffusion tensor imaging (DTI), a neuroimaging technique characterizing the brain's anatomical structure, produces a positive-definite (p.d.) matrix for each voxel. Currently, only a few geostatistical models for p.d. matrices have been proposed because introducing spatial dependence among p.d. matrices properly is challenging. In this paper, we use the spatial Wishart process, a spatial stochastic process (random field), where each p.d. matrix-variate random variable marginally follows a Wishart distribution, and spatial dependence between random matrices is induced by latent Gaussian processes. This process is valid on an uncountable collection of spatial locations and is almost-surely continuous, leading to a reasonable way of modeling spatial dependence. Motivated by a DTI data set of cocaine users, we propose a spatial matrix-variate regression model based on the spatial Wishart process. A problematic issue is that the spatial Wishart process has no closed-form density function. Hence, we propose an approximation method to obtain a feasible Cholesky decomposition model, which we show to be asymptotically equivalent to the spatial Wishart process model. A local likelihood approximation method is also applied to achieve fast computation. The simulation studies and real data application demonstrate that the Cholesky decomposition process model produces reliable inference and improved performance, compared to other methods.

Pathways of Prevention: A Scoping Review of Dietary and Exercise Interventions for Neurocognition

Alzheimer's disease and related dementias (ADRD) represent an increasingly urgent public health concern, with an increasing number of baby boomers now at risk. Due to a lack of efficacious therapies among symptomatic older adults, an increasing emphasis has been placed on preventive measures that can curb or even prevent ADRD development among middle-aged adults. Lifestyle modification using aerobic exercise and dietary modification represents one of the primary treatment modalities used to mitigate ADRD risk, with an increasing number of trials demonstrating that exercise and dietary change, individually and together, improve neurocognitive performance among middle-aged and older adults. Despite several optimistic findings, examination of treatment changes across lifestyle interventions reveals a variable pattern of improvements, with large individual differences across trials. The present review attempts to synthesize available literature linking lifestyle modification to neurocognitive changes, outline putative mechanisms of treatment improvement, and discuss discrepant trial findings. In addition, previous mechanistic assumptions linking lifestyle to neurocognition are discussed, with a focus on potential solutions to improve our understanding of individual neurocognitive differences in response to lifestyle modification. Specific recommendations include integration of contemporary causal inference approaches for analyzing parallel mechanistic pathways and treatment-exposure interactions. Methodological recommendations include trial multiphase optimization strategy (MOST) design approaches that leverage individual differences for improved treatment outcomes.