fMRI correlates of state and trait effects in subjects at genetically enhanced risk of schizophrenia.

Schizophrenia is a highly heritable disorder that typically develops in early adult life. Structural imaging studies have indicated that patients with the illness, and to some extent their unaffected relatives, have subtle deficits in several brain regions, including prefrontal and temporal lobes. It is, however, not known how this inherited vulnerability leads to psychosis. This study used a covert verbal initiation fMRI task previously shown to elicit frontal and temporal activity (the Hayling sentence completion task) to examine this issue. A large (n = 69) number of young participants at high risk of developing schizophrenia for genetic reasons took part, together with a matched group of healthy controls (n = 21). At the time of investigation, none had any psychotic disorder, but on detailed interview some of the high-risk participants (n = 27) reported isolated psychotic symptoms. The study aimed to determine: (i) whether there were activation differences that occurred in all subjects with a genetic risk of schizophrenia (i.e. 'trait' effects); and (ii) whether there were activation differences that only occurred in those at high risk who had isolated psychotic symptoms ('state' effects). No activation differences were found in regions commonly reported to be abnormal in the established illness, namely the dorsolateral prefrontal cortex or in the temporal lobes, but group differences of apparent genetic cause were evident in medial prefrontal, thalamic and cerebellar regions. In addition, differences in activation in those with symptoms were found in the intraparietal sulcus. No significant differences in performance were found between the groups, and all subjects were antipsychotic naïve. These findings therefore suggest that vulnerability to schizophrenia may be inherited as a disruption in a fronto-thalamic-cerebellar network, and the earliest changes specific to the psychotic state may be related to hyperactivation in the parietal lobe.

Non-curated distributed databases for experimental data and models in neuroscience.

Neuroscience is generating vast amounts of highly diverse data which is of potential interest to researchers beyond the laboratories in which it is collected. In particular, quantitative neuroanatomical data is relevant to a wide variety of areas, including studies of development, aging, pathology and in biophysically oriented computational modelling. Moreover, the relatively discrete and well-defined nature of the data make it an ideal application for developing systems designed to facilitate data archiving, sharing and reuse. At present, the only widely used forms of dissemination are figures and tables in published papers which suffer from inaccessibility and the loss of machine readability. They may also present only an averaged or otherwise selected subset of the available data. Numerous database projects are in progress to address these shortcomings. They employ a variety of architectures and philosophies, each with its own merits and disadvantages. One axis on which they may be distinguished is the degree of top-down control, or curation, involved in data entry. Here we consider one extreme of this scale in which there is no curation, minimal standardization and a wide degree of freedom in the form of records used to document data. Such a scheme has advantages in the ease of database creation and in the equitable assignment of perceived intellectual property by keeping the control of data in the hands of the experts who collected it. It does, however, require a more sophisticated infrastructure than conventional databases since the software must be capable of organizing diverse and differently documented data sets in an effective way. Several components of a software system to provide this infrastructure are now in place. Examples are presented, showing how these tools can be used to archive and publish neuronal morphology data, and how they can give an integrated view of data stored at many different sites.

Towards NeuroML: model description methods for collaborative modelling in neuroscience.

Biological nervous systems and the mechanisms underlying their operation exhibit astonishing complexity. Computational models of these systems have been correspondingly complex. As these models become ever more sophisticated, they become increasingly difficult to define, comprehend, manage and communicate. Consequently, for scientific understanding of biological nervous systems to progress, it is crucial for modellers to have software tools that support discussion, development and exchange of computational models. We describe methodologies that focus on these tasks, improving the ability of neuroscientists to engage in the modelling process. We report our findings on the requirements for these tools and discuss the use of declarative forms of model description--equivalent to object-oriented classes and database schema--which we call templates. We introduce NeuroML, a mark-up language for the neurosciences which is defined syntactically using templates, and its specific component intended as a common format for communication between modelling-related tools. Finally, we propose a template hierarchy for this modelling component of NeuroML, sufficient for describing models ranging in structural levels from neuron cell membranes to neural networks. These templates support both a framework for user-level interaction with models, and a high-performance framework for efficient simulation of the models.

Considerations arising from a complementary learning systems perspective on hippocampus and neocortex.

We discuss a framework for the organization of learning systems in the mammalian brain, in which the hippocampus and related areas form a memory system complementary to learning mechanisms in neocortex and other areas. The hippocampal system stores new episodes and "replays" them to the neocortical system, interleaved with ongoing experience, allowing generalization as cortical memories form. The data to account for include: 1) neurophysiological findings concerning representations in hippocampal areas, 2) behavioral evidence demonstrating a spatial role for hippocampus, 3) and effects of surgical and pharmacological manipulations on neuronal firing in hippocampal regions in behaving animals. We hypothesize that the hippocampal memory system consists of three major modules: 1) an invertible encoder subsystem supported by the pathways between neocortex and entorhinal cortex, which provides a stable, compressed, invertible encoding in entorhinal cortex (EC) of cortical activity patterns, 2) a memory separation, storage, and retrieval subsystem, supported by pathways between EC, dentate gyrus and area CA3, including the CA3 recurrent collaterals, which facilitates encoding and storage in CA3 of individual EC patterns, and retrieval of those CA3 encodings, in a manner that minimizes interference, and 3) a memory decoding subsystem, supported by the Shaffer collaterals from area CA1 to area CA3 and the bi-directional pathways between EC and CA3, which provides the means by which a retrieved CA3 coding of an EC pattern can reinstate that pattern on EC. This model has shown that 1) there is a trade-off between the need for information-preserving, structure-extracting encoding of cortical traces and the need for effective storage and recall of arbitrary traces, 2) long-term depression of synaptic strength in the pathways subject to long-term potentiation is crucial in preserving information, 3) area CA1 must be able to exploit correlations in EC patterns in the direct perforant path synapses.