Biomedical subjects

Donovan Rebbechi

Publications and source records attributed to Donovan Rebbechi.

3 recordsLinked to original sources

PubMed2006

Evidence for multiple manipulation processes in prefrontal cortex.

The prefrontal cortex (PFC) is known to subserve working memory (WM) processes. Brain imaging studies of WM using delayed response tasks (DRTs) have shown memory-load-dependent activation increases in dorsal prefrontal cortex (PFC) regions. These activation increases are believed to reflect manipulation of to-be-remembered information in the service of memory-consolidation. This speculation has been based on observations of similar activation increases in tasks that overtly require manipulation by instructing participants to reorder to-be-remembered list items. In this study, we tested the assumption of functional equivalence between these two types of WM tasks. Participants performed a DRT under two conditions with memory loads ranging from 3 to 6 letters. In an "item-order" condition, participants were required to remember letters in the order in which they were presented. In a "reordering" condition, participants were required to remember the letters in alphabetical order. Load-related activation increases were observed during the encoding and maintenance periods of the order maintenance condition, whereas load-related activation decreases were observed in the same periods of the reordering condition. These results suggest that (1) the neural substrates associated with long-list retention and those associated with reordering are not equivalent, (2) cognitive processes associated with long-list retention may be more closely approximated by item-order maintenance than by reordering, and (3) multiple forms of WM manipulation are dissociable on the basis of fMRI data.

Adolescent↗

PubMed2005

Dissociating age-related changes in cognitive strategy and neural efficiency using event-related fMRI.

We used event-related fMRI to measure brain activity while younger and older adults performed an item-recognition task in which the memory-set size varied between 1 and 8 letters. Each trial was composed of a 4-second encoding period in which subjects viewed random letter strings, a 12-second retention period and a 2-second retrieval period in which subjects decided whether a single probe letter was or was not part of the memory set. For both groups, reaction time increased and accuracy decreased with increasing memory set-size. There were minimal age-related differences in activation patterns with increasing memory set-size in prefrontal cortex (PFC). Regression analyses of individual subjects' performance and cortical activity indicated that speed and accuracy accounted for considerable variance in dorsal and ventral PFC activity during encoding and retrieval. These results suggest that younger and older adults utilize similar working memory (WM) strategies to accommodate increasing memory demand. They support a model of cognitive slowing in which processing rate is related to neural efficiency.

Adult↗

PubMed2004

The RUMBA software: tools for neuroimaging data analysis.

The enormous scale and complexity of data sets in functional neuroimaging makes it crucial to have well-designed and flexible software for image processing, modeling, and statistical analysis. At present, researchers must choose between general purpose scientific computing environments (e.g., Splus and Matlab), and specialized human brain mapping packages that implement particular analysis strategies (e.g., AFNI, SPM, VoxBo, FSL or FIASCO). For the vast majority of users in Human Brain Mapping and Cognitive Neuroscience, general purpose computing environments provide an insufficient framework for a complex data-analysis regime. On the other hand, the operational particulars of more specialized neuroimaging analysis packages are difficult or impossible to modify and provide little transparency or flexibility to the user for approaches other than massively multiple comparisons based on inferential statistics derived from linear models. In order to address these problems, we have developed open-source software that allows a wide array of data analysis procedures. The RUMBA software includes programming tools that simplify the development of novel methods, and accommodates data in several standard image formats. A scripting interface, along with programming libraries, defines a number of useful analytic procedures, and provides an interface to data analysis procedures. The software also supports a graphical functional programming environment for implementing data analysis streams based on modular functional components. With these features, the RUMBA software provides researchers programmability, reusability, modular analysis tools, novel data analysis streams, and an analysis environment in which multiple approaches can be contrasted and compared. The RUMBA software retains the flexibility of general scientific computing environments while adding a framework in which both experts and novices can develop and adapt neuroimaging-specific analyses.

Algorithms↗