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 (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 in a whole brain (connectome) framework. This year, we demonstrated how to embed a previously or newly constructed task-based large-scale neural model into the TVB platform (Ulloa and Horwitz, 2016). We tested our method on a previously constructed large-scale neural model (LSNM) of visual object processing that consisted of interconnected neural populations that represent, primary and secondary visual, inferotemporal, and prefrontal cortex. We embedded our LSNM of visual object processing into corresponding nodes within the TVB connectome. Reciprocal connections between TVB nodes and our task-based modules were included in this framework. Our framework thus presents a way to embed task-based neural models into the TVB platform, enabling a better comparison between empirical and computational data, which in turn can lead to a better understanding of how interacting neural populations give rise to human cognitive behaviors. Currently, our laboratory is working on several projects that expand upon the combined LSNM/TVB model discussed above. These include incorporating MEG into the framework (Horwitz et al., in preparation), expanding our model to cover more complicated tasks such as a short-term memory task with distractors (Liu et al., in preparation), and modifying the framework so that cortical lamina specific neural activity can be simulated (Corbitt et al., in preparation). Although the neural substrates of auditory word recognition have been a topic of inquiry since the heyday of classical neurology, they remain poorly understood. A member of our laboratory discussed how some recent lesion studies provide compelling evidence for the causal involvement of anterior superior temporal gyrus (STG) in auditory single-word comprehension (DeWitt and Rauschecker, 2016). 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 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 have 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 and 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. Previous work using transcranial magnetic stimulation (TMS) demonstrated that the right presupplementary motor area (preSMA), a node in the fronto-basal-ganglia network, is critical for response inhibition. However, TMS influences interconnected regions, raising the possibility of a link between the preSMA activity and the functional connectivity within the network. We collaborated with an NINDS lab to understand this relationship. Single-pulse TMS was applied to the right preSMA during fMRI when subjects were at rest to examine changes in neural activity and functional connectivity within the network in relation to the efficiency of response inhibition evaluated with a stop-signal task. The results showed that preSMA-TMS increased activation in the right inferior-frontal cortex (rIFC) and basal ganglia and modulated their task-free functional connectivity. Both the TMS-induced changes in the basal-ganglia activation and the functional connectivity between rIFC and left striatum, and of the overall network correlated with the efficiency of response inhibition and with the white matter microstructure along the preSMArIFC pathway. These results suggest that the task-free functional and structural connectivity between the rIFC opercularis and basal ganglia are critical to the efficiency of response inhibition (Xu et al., 2016).
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