Interpretation Of Physiological Data Research Articles

A new era for mind studies: training investigators in both scientific and contemplative methods of inquiry

The study of the human mind has evolved over the course of many centuries. While modern neuroscience relies on objective, quantitative methods for measuring how mental events manifest as brain activity, ancient contemplative traditions have used first-person introspective practices to gain a greater understanding of the mind. It is now possible to combine these different approaches, hopefully in a mutually enriching, synergistic way. As proposed by the late Francisco J. Varela, studying the conscious mind could greatly benefit from bringing together the “first-person” perspective of a well-trained experimental subject with the “third-person” perspective of an outside observer (i.e., a scientist measuring brain activity). Varela called this approach neurophenomenology, referring to the combination of neural measurements with the style of inquiry of classic phenomenology (Varela, 1996). In neurophenomenology experiments, the subject is actively involved in describing his moment-by-moment conscious experience and is sometimes asked to generate specific mind states, while the experimenter is guided by these first-person data in the analysis and interpretation of physiological data (Lutz and Thompson, 2003). This methodology can be used to better account for seemingly random fluctuations in brain activity, which are usually discarded as “noise” but may reveal key insights into ongoing variations in the subject's inner experience (Lutz et al., 2002; Lachaux, 2011).

Magnetic resonance imaging to investigate the influence of posture on gastric physiology

Magnetic resonance imaging to investigate the influence of posture on gastric physiology

Analytical description of the evolution of neural networks: learning rules and complexity.

We present the foundation of a physical formalism that allows us to characterize the dynamics of the evolution of neural networks both in regard to the network configuration and to network performance. Model runs were performed on a simple network consisting of six neurons, allowing complete analytical description of the network's behaviour. Order parameters are characterized that allow an analytical description of critical periods in network evolution. Thus, correlations of the local dynamics and the system's global behaviour could be computed. It is shown that local learning rules are sufficient to model complex dynamical aspects of the evolution of networks. It is demonstrated in how far novel statistical formalisms, e.g. neural complexity, can be employed to evaluate the system's dynamics. The introduction of order parameters allows an analytical characterization of transient phases in the network's behaviour, correlating network connectivity with neuronal firing patterns. The relevance of this approach for the interpretation of physiological data is discussed.

Problems associated with the interpretation of physiological data in the assessment of animal welfare

Claims about animal welfare based on data regarding the pituitary-adrenocortical axis should be viewed with scepticism because of the lack of consistency between the results of different studies. Occasional sampling of blood does not give an accurate description of the episodic nature of corticosteroid secretion. Too little is known about how chronic stress affects the activity of the pituitary-adrenocortical axis, or whether changes in mean daily level or in the nature of the secretory episodes are most important. More attention should be paid to basic research to understand the nature of this biological system rather than to premature attempts to “measure” animal welfare by corticosteroid values.