Nigel Goddard

The functional anatomy of inspection time: an event-related fMRI study.

Twenty healthy young adults underwent functional magnetic resonance imaging (fMRI) of the brain while performing a visual inspection time task. Inspection time is a forced-choice, two-alternative visual backward-masking task in which the subject is briefly shown two parallel vertical lines of markedly different lengths and must decide which is longer. As stimulus duration decreases, performance declines to chance levels. Individual differences in inspection time correlate with higher cognitive functions. An event-related design was used. The hemodynamic (blood oxygenation level-dependent; BOLD) response was computed as both a function of the eight levels of stimulus duration, from 6 ms (where performance is almost at chance) to 150 ms (where performance is nearly perfect), and a function of the behavioral responses. Random effects analysis showed that the difficulty of the visual discrimination was related to bilateral activation in the inferior fronto-opercular cortex, superior/medial frontal gyrus, and anterior cingulate gyrus, and bilateral deactivation in the posterior cingulate gyrus and precuneus. Examination of the time courses of BOLD responses showed that activation was related specifically to the more difficult, briefer stimuli and that deactivation was found across most stimulus levels. Functional connectivity suggested the existence of two networks. One comprised the fronto-opercular area, intrasylvian area, medial frontal gyrus, and the anterior cingulate cortex (ACC), possibly associated with processing of visually degraded percepts. A posterior network of sensory-related and associative regions might subserve processing of a visual discrimination task that has high processing demands and combines several fundamental cognitive domains. fMRI can thus reveal information about the neural correlates of mental events which occur over very short durations.

Functional Imaging as a Predictor of Schizophrenia

BACKGROUND Prospective studies of young individuals at high risk of schizophrenia allow the investigation of whether neural abnormalities predate development of illness and, if present, have the potential to identify those who may become ill. METHODS We studied young individuals with at least two relatives with the disorder. At baseline functional magnetic resonance imaging (fMRI) scan, none met criteria for any psychiatric disorder, but four subjects subsequently developed schizophrenia. We report the baseline functional imaging findings in these subjects performing a sentence completion task compared with normal control subjects (n = 21) and those at high risk with (n = 21) and without (n = 41) psychotic symptoms who have not developed the disorder. RESULTS High-risk subjects who became ill demonstrated increased activation of the parietal lobe, decreased activation of the anterior cingulate, and smaller increases in activation with increasing task difficulty in the right lingual gyrus and bilateral temporal regions. The hypothesized predictive power of parietal activation was supported only in combination with lingual gyrus activity, which gave a positive predictive value in this sample of .80. CONCLUSIONS Although these findings should be considered cautiously, as only four subjects who had an fMRI scan subsequently became ill, they suggest functional abnormalities are present in high-risk subjects who later became ill, which distinguish them not only from normal control subjects but also those at high risk who had not developed the disorder. These differences are detectable with fMRI and may have clinical utility.

Towards effective and rewarding data sharing.

Recently issued NIH policy statement and implementation guidelines (National Institutes of Health, 2003) promote the sharing of research data. While urging that “all data should be considered for data sharing” and “data should be made as widely and freely available as possible” the current policy requires only high-direct-cost (>US

NEUROINFORMATICS: THE INTEGRATION OF SHARED DATABASES AND TOOLS TOWARDS INTEGRATIVE NEUROSCIENCE

500,000/yr) grantees to share research data, starting 1 October 2003. Data sharing is central to science, and we agree that data should be made available.

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

There is significant interest amongst neuroscientists in sharing neuroscience data and analytical tools. The exchange of neuroscience data and tools between groups affords the opportunity to differently re-analyze previously collected data, encourage new neuroscience interpretations and foster otherwise uninitiated collaborations, and provide a framework for the further development of theoretically based models of brain function. Data sharing will ultimately reduce experimental and analytical error. Many small Internet accessible database initiatives have been developed and specialized analytical software and modeling tools are distributed within different fields of neuroscience. However, in addition large-scale international collaborations are required which involve new mechanisms of coordination and funding. Provided sufficient government support is given to such international initiatives, sharing of neuroscience data and tools can play a pivotal role in human brain research and lead to innovations in neuroscience, informatics and treatment of brain disorders. These innovations will enable application of theoretical modeling techniques to enhance our understanding of the integrative aspects of neuroscience. This article, authored by a multinational working group on neuroinformatics established by the Organization for Economic Co-operation and Development (OECD), articulates some of the challenges and lessons learned to date in efforts to achieve international collaborative neuroscience.

NEOSIM: Portable large-scale plug and play modelling☆

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.

Episodic and semantic memory tasks activate different brain regions in Alzheimer disease.

NEOSIM is a new simulation framework addressed at building large scale and detailed models of the nervous system. Its essence is a set of interfaces and protocols that enable a plug and play architecture for incorporating existing simulation modules such as NEURON [4] and GENESIS [1] as well as future visualisation and data analysis modules. From the start it has been designed to exploit parallel and distributed computers to reduce simulation run times to manageable levels, without the additional modelling e!ort required for earlier publicly-available parallel simulation tools. In this paper, we present the design of the NEOSIM framework, and discuss its applicability to a range of modelling studies. 2001 Published by Elsevier Science B.V.

The functional anatomy of inspection time: a pilot fMRI study

Objective: To compare brain activity identified by fMRI in subjects with Alzheimer disease (AD) and older healthy controls (HCs) performing an episodic/working memory (EWM) and semantic memory (SM) task. Methods: Nine AD (mean age 73.6) and 10 HC (mean age 71.8) subjects underwent an fMRI memory paradigm. Tasks comprised 1) baseline (recognizing a single digit presented for 1 second), 2) SM (addition of two single digits, always producing a single digit answer), and 3) EWM (recall of the previous single digit on the stimulus of the next digit). Each condition was presented in 2-minute blocks with a shorter and longer time interval for the first and second minute within blocks. Results: Comparing AD and HC subjects, there were no activated brain regions in common for EWM > SM, but left anterior cingulate (Brodmann area [BA] 24, 0, 31, 4) and left medial frontal lobe gyrus (BA 25, -6, 23, -15) were activated by both groups for SM > EWM. Key differences were that for EWM > SM, HC subjects activated the right parahippocampal gyrus, whereas subjects with AD activated the right superior frontal gyrus and left uncus. Conclusions: Subjects with Alzheimer disease (AD) recruited brain regions for easier episodic/working memory (EWM) tasks used by healthy controls (HCs) for more difficult EWM tasks. AD subjects recruited brain regions for semantic memory tasks used by HCs for more difficult EWM tasks. The authors propose a functional “memory reserve” model of compensatory recruitment according to task difficulty and underlying neuropathology.

Axiope tools for data management and data sharing.

Seven healthy subjects underwent functional magnetic resonance imaging (fMRI) of the brain while performing an inspection time task. Employing a block-type design, the task had three difficulty levels: a control condition, an easy (200 ms stimulus duration), and a more difficult (40 ms) discrimination. Based on group results, there were widespread significant areas of difference in brain activation and deactivation when pairwise comparisons were conducted among the three task conditions. When the difficult condition was compared with the easy condition, there was relative activation in areas of the following brain regions: cingulate gyrus and some frontal and parietal lobe areas. Areas within the following regions showed relative deactivation (greater blood oxygenation level-dependent, BOLD, signal in the easy condition): frontal, temporal, and parietal lobe. There were overlaps between these areas and those found to be active while performing higher cognitive tasks in other functional brain imaging studies. These pilot data encourage future studies of the functional anatomy of inspection time and its relevance to psychometric intelligence.

Linking computational neuroscience simulation tools—a pragmatic approach to component-based development☆

Many areas of biological research generate large volumes of very diverse data. Managing this data can be a difficult and time-consuming process, particularly in an academic environment where there are very limited resources for IT support staff such as database administrators. The most economical and efficient solutions are those that enable scientists with minimal IT expertise to control and operate their own desktop systems. Axiope provides one such solution, Catalyzer, which acts as flexible cataloging system for creating structured records describing digital resources. The user is able specify both the content and structure of the information included in the catalog. Information and resources can be shared by a variety of means, including automatically generated sets of web pages. Federation and integration of this information, where needed, is handled by Axiope’s Mercat server. Where there is a need for standardization or compatibility of the structures used by different researchers this can be achieved later by applying user-defined mappings in Mercat. In this way, large-scale data sharing can be achieved without imposing unnecessary constraints or interfering with the way in which individual scientists choose to record and catalog their work. We summarize the key technical issues involved in scientific data management and data sharing, describe the main features and functionality of Axiope Catalyzer and Axiope Mercat, and discuss future directions and requirements for an information infrastructure to support large-scale data sharing and scientific collaboration.