Our laboratory studies the relationship between what is observed in functional neuroimaging studies and the underlying neural dynamics. To do this, we use large-scale computer models of neuronal dynamics that perform either a visual or auditory object-matching task similar to those designed for PET/fMRI/MEG studies. A review of both models can be found in Horwitz et al (Phil. Trans. Roy. Soc. B, 2005). Recent efforts have used large-scale, biologically realistic, neural models to help understand the neural basis for the patterns of activity observed in both resting state and task-related functional neuroimaging data. An example of the former is The Virtual Brain (TVB) software platform, which allows one to apply large-scale neural modeling (LSNM) in a whole brain (connectome) framework (see Ulloa and Horwitz, Fron. Neuroinfomatics, 2016). We are using this framework to study the effect of task activity on non-task-related parts of the brain (Ulloa & Horwitz, in preparation). Currently, our laboratory is working on several projects that expand the combined LSNM/TVB model. These include incorporating MEG into the framework (Horwitz et al., in preparation), A second project extended our working memory model to enable distractor stimuli to be included (Liu et al., 2017). The original model consisted of arrays of Wilson-Cowan type neuronal populations representing primary and secondary visual cortices, inferior temporal cortex and prefrontal cortex (PFC). We added a module representing entorhinal cortex, which functions as a gating module. We successfully implemented multiple working memory tasks using the same model and produced neuronal patterns in visual cortex, IT and PFC that match experimental findings. These working memory tasks can include distractor stimuli, or can require that multiple items be retained in mind during a delay period (Sternbergs task). Besides electrophysiology data and behavioral data, we also generated fMRI BOLD timeseries from our simulation. Furthermore, we noticed during simulations of memorizing a list of objects, the first and the last item in the sequence were recalled best, which may implicate the neural mechanism behind this important psychological effect (i.e., the primacy and recency effect). Invasive electrophysiological and neuroanatomical studies in nonhuman mammalian experimental preparations have helped elucidate the lamina (layer) dependence of neural computations and interregional connections. Noninvasive functional neuroimaging can, in principle, resolve cortical laminae (layers), and thus provide insight into human neural computations and interregional connections. However human neuroimaging data are noisy and difficult to interpret; biologically realistic simulations can aid experimental interpretation by relating the neuroimaging data to simulated neural activity. We (Corbitt, Ulloa & Horiwtz, submitted) illustrated the potential of laminar neuroimaging by upgrading our existing largescale, multiregional neural model that simulates a visual delayed matchtosample task. The new laminarbased neural unit incorporates spiny stellate, pyramidal, and inhibitory neural populations which are divided among supragranular, granular, and infragranular laminae (layers). We simulated neural activity which is translated into local field potentiallike data used to simulate conventional and laminar fMRI activity. The hemodynamic model that we employed is a modified version of one due to Heinzle et al. (Neuroimage, 2016) that incorporates the effects of draining veins. We showed that the laminar version of the model replicates the findings of the existing model. The laminar model shows the finer structure in fMRI activity and functional connectivity. We illustrated differences between task and control conditions in the fMRI signal, and demonstrated differences in interregional laminar functional connectivity that reflected the underlying connectivity scheme. The organization of the auditory ventral stream, the neocortical auditory pattern recognition pathway, has been proposed to operate as a hierarchical feature network, wherein elemental features are hierarchically recombined into increasingly complex sensory representations. To probe the operation of this network, we constructed auditory word-form stimuli that contained equivalent lower-order features (phonemes) but which varied in their regularity with respect to the natural statistics of embedded higher-order feature combinations (di-, tri-, tetraphones). Under a strictly feedforward model, stimuli with embedded higher-order feature combinations that are inconsistent with the natural statistics of the sensory environment would be expected to elicit a diminished neural response, compared to stimuli with regular higher-order feature statistics. Conversely, models that incorporate feedback (e.g., predictive coding) posit stimuli with irregular higher-order feature statistics to elicit increased neural response, proportional to expectancy error. To observe neural sensitivity to phoneme sequence probabilities (phonotactics), we presented auditory word-form stimuli to healthy subjects in a functional MRI (fMRI) scanner (Experiment 1) and to temporal lobe epilepsy patients implanted with intracranial electroencephalography (iEEG) arrays (Experiment 2). Preliminary analyses of fMRI data, consistent with feedback models, found increased signal in anterior-lateral planum temporale (PT) in response to irregular higher-order feature statistics. Preliminary analyses of iEEG data similarly found increased high-gamma power response in mid superior temporal gyrus (STG). Together, our findings indicate the auditory ventral stream encodes sequence event probabilities extracted from the long-term natural statistics of the heard environment. Results support feedback-inclusive models, in which expectancy error is processed early in the ventral stream, at the transition from anterior-lateral PT to mid-STG (DeWitt et al., in preparation). We also examined how the brain processes complex sounds, specifically harmonics. Many speech sounds and animal vocalizations contain components consisting of a fundamental frequency (F0) and higher harmonics. Animals and humans rapidly detect such specific features of sounds, but the time course of the underlying neural decision processes is largely unknown. Moreover, multiple pathways of information processing are involved in complex auditory processing. However, the intricate functional organization of these pathways is poorly understood. To address this, we (Banerjee, Kikuchi, Mishkin, Rauschecker & Horwitz, submitted) computed neuronal response latencies from simultaneously recorded spike trains and local field potentials (LFPs) along the first two stages of cortical sound processing, primary auditory cortex (A1) and lateral belt (LB), of awake, behaving macaques. Two types of response latencies were measured for spike trains as well as LFPs: 1) onset latency, time-locked to onset of external auditory stimuli, and 2) discrimination latency, the time taken from stimulus onset to neuronal discrimination between different stimulus categories. Trial-by-trial LFP onset latencies always preceded spike onset latencies. In A1, simple sounds, such as pure tones, yielded shorter spike onset latencies compared to complex sounds, such as monkey vocalizations (coos, in which F0 was matched to a corresponding pure-tone stimulus). This trend was reversed in LB, indicating a hierarchical functional organization of auditory cortex in the macaque. LFP discrimination latencies in A1 were always shorter than those in LB reflecting the serial arrival of stimulus-specific information in these areas. Thus, chronometry on spike-LFP signals revealed some of the effective neural circuitry underlying complex sound discrimination.

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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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