Our laboratory studies the relationship between what is observed in functional neuroimaging studies and the underlying neural dynamics. To do this, we had previously constructed a large-scale computer model of neuronal dynamics that performs a visual object-matching task similar to those designed for PET/fMRI studies (reviewed in Horwitz &Husain 2007). We extended the model so that it could also simulate auditory processing, thus allowing us to investigate the neural basis of auditory object processing in the cerebral cortex. This model relates neuronal dynamics of cortical processing of auditory spectrotemporal patterns to fMRI data. Environmentally relevant auditory stimuli are often composed of long-duration tonal patterns (e.g., multisyllabic words, short sentences). Manipulation of those patterns by the brain requires working memory to temporarily store the pattern segments and integrate them into a percept. To understand the neural basis of this, we extended the model of auditory recognition of short-duration tonal patterns described above. A memory buffer and a gating module were added. The memory buffer increased the storage capacity;the gating module distributed the segments of the input pattern to separate locations of the memory buffer, allowing a subsequent comparison of the stored segments against the segments of a second pattern. Simulations show that the extended model performs match and mismatch of sequences of long-duration tonal patterns. We conducted an fMRI experiment using the same stimuli as employed in the simulations and found areas in the prefrontal cortex that are likely candidate brain areas for the new modules of the extended model (Ulloa et al., 2008). We acquired fMRI data from two studies related to understanding language production and comprehension. In one we investigated the neuroanatomical substrates of phonetic encoding and the generation of articulatory codes from phonological representations. Our focus was on the role of the left inferior frontal gyrus (LIFG) and in particular whether the LIFG plays a role in sublexical phonological processing such as syllabification or whether it is directly involved in phonetic encoding and the generation of articulatory codes. We contrasted the brain activation patterns elicited by pseudowords with high-- or low--sublexical frequency components, which we expected would reveal areas related to the generation of articulatory codes but not areas related to phonological encoding. We found significant activation of a premotor network consisting of the dorsal precentral gyrus, the inferior frontal gyrus bilaterally, and the supplementary motor area for low-- versus highsublexical frequency pseudowords. Based on our hypothesis, we concluded that these areas and in particular the LIFG are involved in phonetic and not phonological encoding. We further discuss our findings with respect to the mechanisms of phonetic encoding and provide evidence in support of a functional segregation of the posterior part of Brocas area, the pars opercularis (Papoutsi et al., 2009). Another study focused on sign language. Manual gestures occur on a continuum from co-speech gesticulations to conventionalized emblems to language signs. To understand the neural bases of the processing of gestures along such a continuum, we studied four types of gestures, varying along linguistic and semantic dimensions: linguistic and meaningful American Sign Language (ASL), non-meaningful pseudo-ASL, meaningful emblematic, and nonlinguistic, non-meaningful made-up gestures. Pre-lingually deaf, native signers of ASL participated in the fMRI study and performed two tasks while viewing videos of the gestures: a visuo-spatial (identity) discrimination task and a category discrimination task. We found that the categorization task activated left ventral middle and inferior frontal gyrus, among other regions, to a greater extent compared to the visual discrimination task, supporting the idea of semantic-level processing of the gestures (Husain et al., 2009). FMRI data can be used to assess how different brain regions interact during the performance of cognitive tasks. The quantities that characterize these interactions are called functional or effective connectivity, but their neurobiological substrates are, however, uncertain. Functional connectivity is computed as the correlation between interregional activities;effective connectivity investigates the influence that brain regions exert on one another. Structural equation modeling (SEM) has been the main approach to examine effective connectivity, and has been widely used to investigate brain disorders. Many brain disorders result from alterations in the strength of anatomical connectivity between different brain regions. This study investigated whether such alterations can be revealed by examining differences in interregional effective connectivity between patient and normal subjects. We applied SEM to simulated fMRI timeseries from a neurobiologically realistic network model in which the anatomical connectivity is known and can be manipulated. These timeseries were simulated for two task conditions, a delayed match-tosample (DMS) task and passive-viewing, and for normal subjects and patients who had one weakened anatomical connection in the neural network model. SEM results were compared between task conditions as well as between groups. A significantly reduced effective connectivity corresponding to the weakened anatomical connection during the DMS task was found. We also obtained a significantly reduced set of effective connections in the patient networks for anatomical connections downstream from the weakened linkage. However, some upstream effective connections were significantly larger in the patient group relative to normals. These results suggest that caution is necessary in applying effective connectivity methods to fMRI data obtained from non-normal populations, and emphasize that functional interactions among network elements can appear as abnormal even if only part of a network is damaged (Kim and Horwitz, 2009). We applied SEM to examine obesity. Exaggerated reactivity to food cues in obese women appears to be mediated in part by a hyperactive reward system that includes the nucleus accumbens, amygdala, and orbitofrontal cortex. The present study used fMRI to investigate whether differences between 12 obese and 12 normal-weightwomen in reward-related brain activation in response to food images can be explained by changes in the functional interactions between key reward network regions. A two-step path analysis/General Linear Model approachwas used to test whether there were group differences in network connections between nucleus accumbens, amygdala, and orbitofrontal cortex in response to high- and low-calorie food images. There was abnormal connectivity in the obese group in response to both highand low-calorie food cues compared to normal-weight controls. Compared to controls, the obese group had a relative deficiency in the amygdalas modulation of activation in both orbitofrontal cortex and nucleus accumbens, but excessive influence of orbitofrontal cortexs modulation of activation in nucleus accumbens. The deficient projections from the amygdala might relate to suboptimal modulation of the affective/emotional aspects of a foods reward value or an associated cues motivational salience, whereas increased orbitofrontal cortex to nucleus accumbens connectivity might contribute to a heightened drive to eat in response to a food cue. Thus, it is possible that not only greater activation of the reward system, but also differences in the interaction of regions in this networkmay contribute to the relatively increased motivational value of foods in obese individuals (Stoeckel et al., 2009).

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National Institute on Deafness and Other Communication Disorders
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Banerjee, Arpan; Kikuchi, Yukiko; Mishkin, Mortimer et al. (2018) Chronometry on Spike-LFP Responses Reveals the Functional Neural Circuitry of Early Auditory Cortex Underlying Sound Processing and Discrimination. eNeuro 5:
Corbitt, Paul T; Ulloa, Antonio; Horwitz, Barry (2018) Simulating laminar neuroimaging data for a visual delayed match-to-sample task. Neuroimage 173:199-222
Liu, Qin; Ulloa, Antonio; Horwitz, Barry (2017) Using a Large-scale Neural Model of Cortical Object Processing to Investigate the Neural Substrate for Managing Multiple Items in Short-term Memory. J Cogn Neurosci :1-17
Xu, Benjamin; Sandrini, Marco; Wang, Wen-Tung et al. (2016) PreSMA stimulation changes task-free functional connectivity in the fronto-basal-ganglia that correlates with response inhibition efficiency. Hum Brain Mapp 37:3236-49
Ulloa, Antonio; Horwitz, Barry (2016) Embedding Task-Based Neural Models into a Connectome-Based Model of the Cerebral Cortex. Front Neuroinform 10:32
Ard, Tyler; Carver, Frederick W; Holroyd, Tom et al. (2015) Detecting Functional Connectivity During Audiovisual Integration with MEG: A Comparison of Connectivity Metrics. Brain Connect 5:336-48
Fuertinger, Stefan; Horwitz, Barry; Simonyan, Kristina (2015) The Functional Connectome of Speech Control. PLoS Biol 13:e1002209
Horwitz, Barry (2014) The elusive concept of brain network. Comment on ""Understanding brain networks and brain organization"" by Luiz Pessoa. Phys Life Rev 11:448-51
Simonyan, Kristina; Herscovitch, Peter; Horwitz, Barry (2013) Speech-induced striatal dopamine release is left lateralized and coupled to functional striatal circuits in healthy humans: a combined PET, fMRI and DTI study. Neuroimage 70:21-32
Horwitz, Barry; Hwang, Chuhern; Alstott, Jeff (2013) Interpreting the effects of altered brain anatomical connectivity on fMRI functional connectivity: a role for computational neural modeling. Front Hum Neurosci 7:649

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