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. 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. A review of both models can be found in Horwitz &Husain (2007). A number of hypotheses relating to the functional organization of the auditory object processing pathway were employed in constructing the auditory model. One of the most important of these proposed that neurons in the more anterior parts of the pathway should respond better to more complex auditory objects than to simple tones and sweeps. To test this hypothesis, we (Kikuchi et al., 2010) recorded and analyzed auditory responses of single neurons in macaque monkeys from three different sectors distributed caudorostrally along the superior temporal plane (STP). We found that the majority of rostral STP neurons, unlike those in area A1, are driven best by complex acoustic features rather than simple ones, in agreement with the hypothesis of our large-scale neural model (Kikuchi et al., 2010). Another aspect of auditory object processing emerged in a human magnetoencephalography (MEG) study by Rong et al. (2011). During the experiments, we acquired whole-head MEG data while participants performed an auditory delayed-match-to-sample (DMS) task. We observed a significant DMS-specific suppression of the auditory evoked response to the second stimulus in a sound pair, with the center of the effect being located in the vicinity of the left auditory cortex, and found a DMS-specific enhanced functional interaction between the sources in left auditory cortex and those in left inferior frontal gyrus. Thus, early evoked cortical responses to incoming acoustic stimuli can be modulated by task-specific cognitive functions by means of frontaltemporal functional interactions. Our auditory processing neural modeling has been used for trying to understand the neural basis of tinnitus, which is the perception of sound in the absence of an external source (Husain, 2007). Tinnitus is often accompanied by hearing loss but not everyone with hearing loss experiences tinnitus. Models make it possible to evaluate the contribution of different neural mechanisms affecting tinnitus in a principled manner. To obtain experimental data that could be used to compare with our neural model, we first examined neuroanatomical alterations associated with hearing loss and tinnitus in three groups of subjects: those with hearing loss with tinnitus, those with hearing loss without tinnitus and normal hearing controls without tinnitus. To examine changes in gray matter we (Husain et al., 2011) used structural MRI scans and voxel-based morphometry (VBM), and to identify changes in white matter tract integrity we used diffusion tensor imaging (DTI). A major finding of our study was that there were both gray and white matter changes in the vicinity of the auditory cortex for subjects with hearing loss alone relative to those with tinnitus and those with normal hearing. We did not find significant changes in gray or white matter in subjects with tinnitus and hearing loss compared to normal hearing controls. Thus, in attempting to dissociate the effect of tinnitus from hearing loss, we observed that hearing loss rather than tinnitus had the greatest influence on gray and white matter alterations. To see if functional neural imaging was more sensitive than structural neural imaging with respect to tinnitus, we used fMRI to investigate differences among the three groups in auditory perception and cognitive processing. We employed pure tones and frequency-modulated sweeps as stimuli in two tasks: passive listening and active discrimination. Preliminary results suggest that a differential engagement of a putative auditory network, comprising regions in the frontal, parietal and temporal cortices and the anterior cingulate, may represent a key difference in the neural bases of chronic tinnitus accompanied by hearing loss relative to hearing loss alone. Our laboratory also has performed studies to elucidate the neural basis of speech production and its disorders. One central brain structure is the laryngeal motor cortex (LMC) and it is indispensable for the vocal motor control of speech and song production (see Simonyan and Horwitz, 2011, for a review). Other important speech network regions include premotor cortex and the left inferior frontal gyrus. An important disorder associated with aberrant speech production is stuttering. We examined functional and structural connectivity within corticocortical loops in adults who stutter and compared the findings with those obtained individuals who do not stutter (Chang et al., in press). Psychophysiological interaction (PPI) was used to find brain regions with heightened functional connectivity with the left and right inferior frontal gyri during speech and nonspeech tasks. Probabilistic tractography was used to track white matter tracts in each hemisphere using the same seed regions. Both PPI and tractrography supported connectivity deficits between the left inferior frontal gyrus and the left premotor regions, while connectivity among homologous right hemisphere structures was significantly increased in the stuttering group, providing support for deficient left hemisphere inferior frontal to premotor connectivity as a neural correlate of stuttering. Our laboratory has also continued to develop new methods for employing brain fMRI data to evaluate how different brain regions interact with one another during the performance of sensory, motor and cognitive tasks (i.e., brain network methods to calculate functional and effective connectivity). One potentially important application of these methods is for use as biomarkers for neurological and psychiatric disorders. An overview of the employment of such neuroimaging biomarkers for neurodegenerative disorders (Horwitz and Rowe, in press) discussed the various uses that functional neuroimaging biomarkers can play in detecting, diagnosing, assessing treatment response and investigating neurodegenerative disorders. We went on to explain why the emphasis of much recent work has shifted to network-based biomarkers, as opposed to those that examine individual brain regions. A number of examples were referenced that illustrate the points made.

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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
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
Banerjee, Arpan; Horwitz, Barry (2013) Can quantum probability help analyze the behavior of functional brain networks? Behav Brain Sci 36:278-9

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