From an embodied and enactive point of view, the mind–body problem has been reformulated as the relation between the lived or subject body on the one hand and the physiological or object body on the ...other (“body–body problem”). The aim of the paper is to explore the concept of circularity as a means of explaining the relation between the phenomenology of lived experience and the dynamics of organism–environment interactions. This concept of circularity also seems suitable for connecting enactive accounts with ecological psychology. It will be developed in a threefold way:
(1) As the
circular structure of embodiment
, which manifests itself (a) in the homeostatic cycles between the brain and body and (b) in the sensorimotor cycles between the brain, body, and environment. This includes the interdependence of an organism’s dispositions of sense-making and the affordances of the environment.
(2) As the
circular causality
, which characterizes the relation between parts and whole within the living organism as well as within the organism–environment system.
(3) As the
circularity of process and structure
in development and learning. Here, it will be argued that subjective experience constitutes a process of sense-making that implies (neuro-)physiological processes so as to form modified neuronal structures, which in turn enable altered future interactions.
On this basis, embodied experience may ultimately be conceived as the integration of brain–body and body–environment interactions, which has a top-down, formative, or ordering effect on physiological processes. This will serve as an approach to a solution of the body–body problem.
The development of decision support systems for pathology and their deployment in clinical practice have been hindered by the need for large manually annotated datasets. To overcome this problem, we ...present a multiple instance learning-based deep learning system that uses only the reported diagnoses as labels for training, thereby avoiding expensive and time-consuming pixel-wise manual annotations. We evaluated this framework at scale on a dataset of 44,732 whole slide images from 15,187 patients without any form of data curation. Tests on prostate cancer, basal cell carcinoma and breast cancer metastases to axillary lymph nodes resulted in areas under the curve above 0.98 for all cancer types. Its clinical application would allow pathologists to exclude 65-75% of slides while retaining 100% sensitivity. Our results show that this system has the ability to train accurate classification models at unprecedented scale, laying the foundation for the deployment of computational decision support systems in clinical practice.
•Positron Emission Tomography (PET) has become a key imaging method in cancer care.•DeepPET is a deep neural network to reconstruct images directly from PET sinograms.•Reconstruction speed is 108 ...times faster compared to conventional techniques.•Image quality is improved over conventional techniques.•Improved image quality and speed can lead to improved workflow and cancer care.
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The purpose of this research was to implement a deep learning network to overcome two of the major bottlenecks in improved image reconstruction for clinical positron emission tomography (PET). These are the lack of an automated means for the optimization of advanced image reconstruction algorithms, and the computational expense associated with these state-of-the art methods.
We thus present a novel end-to-end PET image reconstruction technique, called DeepPET, based on a deep convolutional encoder–decoder network, which takes PET sinogram data as input and directly and quickly outputs high quality, quantitative PET images. Using simulated data derived from a whole-body digital phantom, we randomly sampled the configurable parameters to generate realistic images, which were each augmented to a total of more than 291,000 reference images. Realistic PET acquisitions of these images were simulated, resulting in noisy sinogram data, used for training, validation, and testing the DeepPET network.
We demonstrated that DeepPET generates higher quality images compared to conventional techniques, in terms of relative root mean squared error (11%/53% lower than ordered subset expectation maximization (OSEM)/filtered back-projection (FBP), structural similarity index (1%/11% higher than OSEM/FBP), and peak signal-to-noise ratio (1.1/3.8 dB higher than OSEM/FBP). In addition, we show that DeepPET reconstructs images 108 and 3 times faster than OSEM and FBP, respectively. Finally, DeepPET was successfully applied to real clinical data. This study shows that an end-to-end encoder–decoder network can produce high quality PET images at a fraction of the time compared to conventional methods.
While the concept of disorders of basic self-experience as the clinical core of schizophrenia spectrum disorders has gained increasing significance and empirical support, several questions remain ...still unresolved. One major problem is to understand how the basic and prodromal self-disturbances are related to Schneider's first rank symptoms, in particular to the so-called 'ego disorders' found in acute psychotic episodes. The study of the transition from prodromal to first rank symptoms, for example from alienated thoughts to thoughts aloud or thought insertions, is of particular importance for understanding the nature and course of schizophrenia. The paper analyses the emergence of ego disorders from basic self-disorders in phenomenological terms, taking the examples of motor passivity experiences and thought insertion. It is argued that full-blown delusions of alien control are ultimately based on a disturbance of the intentionality of thinking, feeling and acting. This disturbance, for its part, may be traced back to anomalies of self-experience in prodromal stages of schizophrenia.
Current theories of social cognition are mainly based on a representationalist view. Moreover, they focus on a rather sophisticated and limited aspect of understanding others, i.e. on how we predict ...and explain others’ behaviours through representing their mental states. Research into the ‘social brain’ has also favoured a third-person paradigm of social cognition as a passive observation of others’ behaviour, attributing it to an inferential, simulative or projective process in the individual brain. In this paper, we present a concept of social understanding as an ongoing, dynamical process of participatory sense-making and mutual incorporation. This process may be described (1) from a dynamical agentive systems point of view as an
interaction and coordination of two embodied agents
; (2) from a phenomenological approach as a
mutual incorporation
, i.e. a process in which the lived bodies of both participants extend and form a common intercorporality. Intersubjectivity, it is argued, is not a solitary task of deciphering or simulating the movements of others but means entering a process of embodied interaction and generating common meaning through it. This approach will be further illustrated by an analysis of primary dyadic interaction in early childhood.
Proper developmental, neural cell-type-specific, and activity-dependent regulation of GABAergic transmission is essential for virtually all aspects of CNS function. The number of GABA(A) receptors in ...the postsynaptic membrane directly controls the efficacy of GABAergic synaptic transmission. Thus, regulated trafficking of GABA(A) receptors is essential for understanding brain function in both health and disease. Here we summarize recent progress in the understanding of mechanisms that allow dynamic adaptation of cell surface expression and postsynaptic accumulation and function of GABA(A) receptors. This includes activity-dependent and cell-type-specific changes in subunit gene expression, assembly of subunits into receptors, as well as exocytosis, endocytic recycling, diffusion dynamics, and degradation of GABA(A) receptors. In particular, we focus on the roles of receptor-interacting proteins, scaffold proteins, synaptic adhesion proteins, and enzymes that regulate the trafficking and function of receptors and associated proteins. In addition, we review neuropeptide signaling pathways that affect neural excitability through changes in GABA(A)R trafficking.
Abstract The histological assessment of human tissue has emerged as the key challenge for detection and treatment of cancer. A plethora of different data sources ranging from tissue microarray data ...to gene expression, proteomics or metabolomics data provide a detailed overview of the health status of a patient. Medical doctors need to assess these information sources and they rely on data driven automatic analysis tools. Methods for classification, grouping and segmentation of heterogeneous data sources as well as regression of noisy dependencies and estimation of survival probabilities enter the processing workflow of a pathology diagnosis system at various stages. This paper reports on state-of-the-art of the design and effectiveness of computational pathology workflows and it discusses future research directions in this emergent field of medical informatics and diagnostic machine learning.
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