Toward biophysical markers of depression vulnerability.

A major difficulty with treating psychiatric disorders is their heterogeneity: different neural causes can lead to the same phenotype. To address this, we propose describing the underlying pathophysiology in terms of interpretable, biophysical parameters of a neural model derived from the electroencephalogram. We analyzed data from a small patient cohort of patients with depression and controls. Using DCM, we constructed biophysical models that describe neural dynamics in a cortical network activated during a task that is used to assess depression state. We show that biophysical model parameters are biomarkers, that is, variables that allow subtyping of depression at a biological level. They yield a low dimensional, interpretable feature space that allowed description of differences between individual patients with depressive symptoms. They could capture internal heterogeneity/variance of depression state and achieve significantly better classification than commonly used EEG features. Our work is a proof of concept that a combination of biophysical models and machine learning may outperform earlier approaches based on classical statistics and raw brain data.

Deep brain stimulation of the internal capsule enhances human cognitive control and prefrontal cortex function.

Deep brain stimulation (DBS) is a circuit-oriented treatment for mental disorders. Unfortunately, even well-conducted psychiatric DBS clinical trials have yielded inconsistent symptom relief, in part because DBS' mechanism(s) of action are unclear. One clue to those mechanisms may lie in the efficacy of ventral internal capsule/ventral striatum (VCVS) DBS in both major depression (MDD) and obsessive-compulsive disorder (OCD). MDD and OCD both involve deficits in cognitive control. Cognitive control depends on prefrontal cortex (PFC) regions that project into the VCVS. Here, we show that VCVS DBS' effect is explained in part by enhancement of PFC-driven cognitive control. DBS improves human subjects' performance on a cognitive control task and increases theta (5-8Hz) oscillations in both medial and lateral PFC. The theta increase predicts subjects' clinical outcomes. Our results suggest a possible mechanistic approach to DBS therapy, based on tuning stimulation to optimize these neurophysiologic phenomena.

Negotiating boundaries: Accessing donor gametes in India.

BACKGROUND: This paper documents how couples and providers access donor materials for conception in the Indian context and perceptions about using them. The objective is to facilitate understanding of critical issues and relevant concerns. METHODS: A postal survey was conducted with a sample of 6000 gynaecologists and in-depth interviews were -conducted with 39 gynaecologists in four cities. RESULTS: Donor gametes are relatively more acceptable than a few years ago, especially if confidentiality can be -maintained, though lack of availability of donor materials is sometimes an impediment to infertility treatment. Donor sperms are usually accessed from in-house or commercial sperm banks, pathology laboratories, IVF centres, -professional donors, relatives or friends. There is scepticism about screening procedures of sperm banks. Donor eggs are usually accessed from voluntary donors, friends, relatives, egg sharing programmes, donation from other patients, advertising and commercial donors. There are several concerns regarding informed consent for using donated gametes, using -relatives and friends gametes, the unregulated use of gametes and embryos, record keeping and documentation, -unethical and corrupt practices and commercialisation. CONCLUSION: These issues need to be addressed by patients, providers and regulatory authorities by providing -information, counselling, ensuring informed consent, addressing exploitation and commercialisation, ensuring -monitoring, proper documentation and transparency.

The NOESY jigsaw: automated protein secondary structure and main-chain assignment from sparse, unassigned NMR data.

High-throughput, data-directed computational protocols for Structural Genomics (or Proteomics) are required in order to evaluate the protein products of genes for structure and function at rates comparable to current gene-sequencing technology. This paper presents the JIGSAW algorithm, a novel high-throughput, automated approach to protein structure characterization with nuclear magnetic resonance (NMR). JIGSAW applies graph algorithms and probabilistic reasoning techniques, enforcing first-principles consistency rules in order to overcome a 5-10% signal-to-noise ratio. It consists of two main components: (1) graph-based secondary structure pattern identification in unassigned heteronuclear NMR data, and (2) assignment of spectral peaks by probabilistic alignment of identified secondary structure elements against the primary sequence. Deferring assignment eliminates the bottleneck faced by traditional approaches, which begin by correlating peaks among dozens of experiments. JIGSAW utilizes only four experiments, none of which requires 13C-labeled protein, thus dramatically reducing both the amount and expense of wet lab molecular biology and the total spectrometer time. Results for three test proteins demonstrate that JIGSAW correctly identifies 79-100% of alpha-helical and 46-65% of beta-sheet NOE connectivities and correctly aligns 33-100% of secondary structure elements. JIGSAW is very fast, running in minutes on a Pentium-class Linux workstation. This approach yields quick and reasonably accurate (as opposed to the traditional slow and extremely accurate) structure calculations. It could be useful for quick structural assays to speed data to the biologist early in an investigation and could in principle be applied in an automation-like fashion to a large fraction of the proteome.