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author:("tagamet, M-A.")
1.  Word and Letter String Processing Networks in Schizophrenia: Evidence for Anomalies and Compensation 
Brain and language  2008;107(2):158-166.
Imaging studies show that in normal language correlated activity between anterior and posterior brain regions increases as the linguistic and semantic content (i.e., from false fonts, letter strings, pseudo words, to words) of stimuli increase. In schizophrenia however, disrupted functional connectivity between frontal and posterior brain regions has been frequently reported and these disruptions may change the nature of language organization. We characterized basic linguistic operations in word and letter string processing in a region-of-interest network using structural equation modeling (SEM). Healthy volunteers and volunteers with schizophrenia performed an fMRI one-back matching task with real words and consonant letter strings. We hypothesized that left hemisphere network dysfunction in schizophrenia would be present during processes dealing with linguistic/semantic content. The modeling results suggest aberrant left hemisphere function in schizophrenia, even in tasks requiring minimal access to language. Alternative mechanisms included increases in right hemisphere involvement and increased top-down influence from frontal to posterior regions.
doi:10.1016/j.bandl.2008.04.001
PMCID: PMC2599869  PMID: 18829095
Schizophrenia and language; Lateralization; Lexical-semantic processing; Imaging; Effective Connectivity; Modeling
2.  Functional Connectivity in fMRI: A Modeling Approach for Estimation and for Relating to Local Circuits 
NeuroImage  2006;34(3):1093-1107.
Although progress has been made in relating neuronal events to changes in brain metabolism and blood flow, the interpretation of functional neuroimaging data in terms of the underlying brain circuits is still poorly understood. Computational modeling of connection patterns both among and within regions can be helpful in this interpretation. We present a neural network model of the ventral visual pathway and its relevant functional connections. This includes a new learning method that adjusts the magnitude of interregional connections in order to match experimental results of an arbitrary functional magnetic resonance imaging (fMRI) data set. We demonstrate that this method finds the appropriate connection strengths when trained on a model system with known, randomly chosen connection weights. We then use the method for examining fMRI results from a one-back matching task in human subjects, both healthy and those with schizophrenia. The results discovered by the learning method support previous findings of a disconnection between left temporal and frontal cortices in the group with schizophrenia, and a concomitant increase of right-sided temporo-frontal connection strengths. We then demonstrate that the disconnection may be explained by reduced local recurrent circuitry in frontal cortex. This method extends currently available methods for estimating functional connectivity from human imaging data by including both local circuits and features of inter-regional connections, such as topography and sparseness, in addition to total connection strengths. Furthermore, our results suggest how fronto-temporal functional disconnection in schizophrenia can result from reduced local synaptic connections within frontal cortex rather than compromised inter-regional connections.
doi:10.1016/j.neuroimage.2006.10.008
PMCID: PMC1866913  PMID: 17134917
fMRI; computational models; connectivity; neural networks
3.  Investigating the neural basis for functional and effective connectivity. Application to fMRI 
Viewing cognitive functions as mediated by networks has begun to play a central role in interpreting neuroscientific data, and studies evaluating interregional functional and effective connectivity have become staples of the neuroimaging literature. The neurobiological substrates of functional and effective connectivity are, however, uncertain. We have constructed neurobiologically realistic models for visual and auditory object processing with multiple interconnected brain regions that perform delayed match-to-sample (DMS) tasks. We used these models to investigate how neurobiological parameters affect the interregional functional connectivity between functional magnetic resonance imaging (fMRI) time-series. Variability is included in the models as subject-to-subject differences in the strengths of anatomical connections, scan-to-scan changes in the level of attention, and trial-to-trial interactions with non-specific neurons processing noise stimuli. We find that time-series correlations between integrated synaptic activities between the anterior temporal and the prefrontal cortex were larger during the DMS task than during a control task. These results were less clear when the integrated synaptic activity was haemodynamically convolved to generate simulated fMRI activity. As the strength of the model anatomical connectivity between temporal and frontal cortex was weakened, so too was the strength of the corresponding functional connectivity. These results provide a partial validation for using fMRI functional connectivity to assess brain interregional relations.
doi:10.1098/rstb.2005.1647
PMCID: PMC1854930  PMID: 16087450
brain; human; functional magnetic resonance imaging; positron emission tomography; neural modelling; object processing

Results 1-3 (3)