Simulating laminar neuroimaging data for a visual delayed match-to-sample task.

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 illustrate the potential of laminar neuroimaging by upgrading an existing large-scale, multiregion neural model that simulates a visual delayed match-to-sample (DMS) task. The new laminar-based 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 potential-like data used to simulate conventional and laminar fMRI activity. We implemented the laminar connectivity schemes proposed by Felleman and Van Essen (Cerebral Cortex, 1991) for interregional connections. The hemodynamic model that we employ is a modified version of one due to Heinzle et al. (Neuroimage, 2016) that incorporates the effects of draining veins. We show 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. Laminar differences in the magnitude of neural activities are a prominent finding; these are also visible in the simulated fMRI. We illustrate differences between task and control conditions in the fMRI signal, and demonstrate differences in interregional laminar functional connectivity that reflect the underlying connectivity scheme. These results indicate that multi-layer computational models can aid in interpreting layer-specific fMRI, and suggest that increased use of laminar fMRI could provide unique and fundamental insights to human neuroscience.

The Functional Connectome of Speech Control.

In the past few years, several studies have been directed to understanding the complexity of functional interactions between different brain regions during various human behaviors. Among these, neuroimaging research installed the notion that speech and language require an orchestration of brain regions for comprehension, planning, and integration of a heard sound with a spoken word. However, these studies have been largely limited to mapping the neural correlates of separate speech elements and examining distinct cortical or subcortical circuits involved in different aspects of speech control. As a result, the complexity of the brain network machinery controlling speech and language remained largely unknown. Using graph theoretical analysis of functional MRI (fMRI) data in healthy subjects, we quantified the large-scale speech network topology by constructing functional brain networks of increasing hierarchy from the resting state to motor output of meaningless syllables to complex production of real-life speech as well as compared to non-speech-related sequential finger tapping and pure tone discrimination networks. We identified a segregated network of highly connected local neural communities (hubs) in the primary sensorimotor and parietal regions, which formed a commonly shared core hub network across the examined conditions, with the left area 4p playing an important role in speech network organization. These sensorimotor core hubs exhibited features of flexible hubs based on their participation in several functional domains across different networks and ability to adaptively switch long-range functional connectivity depending on task content, resulting in a distinct community structure of each examined network. Specifically, compared to other tasks, speech production was characterized by the formation of six distinct neural communities with specialized recruitment of the prefrontal cortex, insula, putamen, and thalamus, which collectively forged the formation of the functional speech connectome. In addition, the observed capacity of the primary sensorimotor cortex to exhibit operational heterogeneity challenged the established concept of unimodality of this region.

Using a Large-scale Neural Model of Cortical Object Processing to Investigate the Neural Substrate for Managing Multiple Items in Short-term Memory.

Many cognitive and computational models have been proposed to help understand working memory. In this article, we present a simulation study of cortical processing of visual objects during several working memory tasks using an extended version of a previously constructed large-scale neural model [Tagamets, M. A., & Horwitz, B. Integrating electrophysiological and anatomical experimental data to create a large-scale model that simulates a delayed match-to-sample human brain imaging study. Cerebral Cortex, 8, 310-320, 1998]. The original model consisted of arrays of Wilson-Cowan type of neuronal populations representing primary and secondary visual cortices, inferotemporal (IT) cortex, and 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 cortex, 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 (Sternberg's task). Besides electrophysiology data and behavioral data, we also generated fMRI BOLD time series from our simulation. Our results support the involvement of IT cortex in working memory maintenance and suggest the cortical architecture underlying the neural mechanisms mediating particular working memory tasks. Furthermore, we noticed that, during simulations of memorizing a list of objects, the first and last items in the sequence were recalled best, which may implicate the neural mechanism behind this important psychological effect (i.e., the primacy and recency effect).

PreSMA stimulation changes task-free functional connectivity in the fronto-basal-ganglia that correlates with response inhibition efficiency.

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. To understand this relationship, we applied single-pulse TMS to the right preSMA during functional magnetic resonance imaging when the 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 preSMA-rIFC pathway. These results suggest that the task-free functional and structural connectivity between the rIFCop and basal ganglia are critical to the efficiency of response inhibition. Hum Brain Mapp 37:3236-3249, 2016. © 2016 Wiley Periodicals, Inc.

Speech-induced striatal dopamine release is left lateralized and coupled to functional striatal circuits in healthy humans: a combined PET, fMRI and DTI study.

Considerable progress has been recently made in understanding the brain mechanisms underlying speech and language control. However, the neurochemical underpinnings of normal speech production remain largely unknown. We investigated the extent of striatal endogenous dopamine release and its influences on the organization of functional striatal speech networks during production of meaningful English sentences using a combination of positron emission tomography (PET) with the dopamine D(2)/D(3) receptor radioligand [(11)C]raclopride and functional MRI (fMRI). In addition, we used diffusion tensor tractography (DTI) to examine the extent of dopaminergic modulatory influences on striatal structural network organization. We found that, during sentence production, endogenous dopamine was released in the ventromedial portion of the dorsal striatum, in both its associative and sensorimotor functional divisions. In the associative striatum, speech-induced dopamine release established a significant relationship with neural activity and influenced the left-hemispheric lateralization of striatal functional networks. In contrast, there were no significant effects of endogenous dopamine release on the lateralization of striatal structural networks. Our data provide the first evidence for endogenous dopamine release in the dorsal striatum during normal speaking and point to the possible mechanisms behind the modulatory influences of dopamine on the organization of functional brain circuits controlling normal human speech.

Assessing crossmodal matching of abstract auditory and visual stimuli in posterior superior temporal sulcus with MEG.

Associating crossmodal auditory and visual stimuli is an important component of perception, with the posterior superior temporal sulcus (pSTS) hypothesized to support this. However, recent evidence has argued that the pSTS serves to associate two stimuli irrespective of modality. To examine the contribution of pSTS to crossmodal recognition, participants (N=13) learned 12 abstract, non-linguistic pairs of stimuli over 3weeks. These paired associates comprised four types: auditory-visual (AV), auditory-auditory (AA), visual-auditory (VA), and visual-visual (VV). At week four, participants were scanned using magnetoencephalography (MEG) while performing a correct/incorrect judgment on pairs of items. Using an implementation of synthetic aperture magnetometry that computes real statistics across trials (SAMspm), we directly contrasted crossmodal (AV and VA) with unimodal (AA and VV) pairs from stimulus-onset to 2s in theta (4-8Hz), alpha (9-15Hz), beta (16-30Hz), and gamma (31-50Hz) frequencies. We found pSTS showed greater desynchronization in the beta frequency for crossmodal compared with unimodal trials, suggesting greater activity during the crossmodal pairs, which was not influenced by congruency of the paired stimuli. Using a sliding window SAM analysis, we found the timing of this difference began in a window from 250 to 750ms after stimulus-onset. Further, when we directly contrasted all sub-types of paired associates from stimulus-onset to 2s, we found that pSTS seemed to respond to dynamic, auditory stimuli, rather than crossmodal stimuli per se. These findings support an early role for pSTS in the processing of dynamic, auditory stimuli, and do not support claims that pSTS is responsible for associating two stimuli irrespective of their modality.

Can quantum probability help analyze the behavior of functional brain networks?

Pothos & Busemeyer (P&B) argue how key concepts of quantum probability, for example, order/context, interference, superposition, and entanglement, can be used in cognitive modeling. Here, we suggest that these concepts can be extended to analyze neurophysiological measurements of cognitive tasks in humans, especially in functional neuroimaging investigations of large-scale brain networks.

Temporal microstructure of cortical networks (TMCN) underlying task-related differences.

Neuro-electromagnetic recording techniques (EEG, MEG, iEEG) provide high temporal resolution data to study the dynamics of neurocognitive networks: large scale neural assemblies involved in task-specific information processing. How does a neurocognitive network reorganize spatiotemporally on the order of a few milliseconds to process specific aspects of the task? At what times do networks segregate for task processing, and at what time scales does integration of information occur via changes in functional connectivity? Here, we propose a data analysis framework-Temporal microstructure of cortical networks (TMCN)-that answers these questions for EEG/MEG recordings in the signal space. Method validation is established on simulated MEG data from a delayed-match to-sample (DMS) task. We then provide an example application on MEG recordings during a paired associate task (modified from the simpler DMS paradigm) designed to study modality specific long term memory recall. Our analysis identified the times at which network segregation occurs for processing the memory recall of an auditory object paired to a visual stimulus (visual-auditory) in comparison to an analogous visual-visual pair. Across all subjects, onset times for first network divergence appeared within a range of 0.08-0.47 s after initial visual stimulus onset. This indicates that visual-visual and visual auditory memory recollection involves equivalent network components without any additional recruitment during an initial period of the sensory processing stage which is then followed by recruitment of additional network components for modality specific memory recollection. Therefore, we propose TMCN as a viable computational tool for extracting network timing in various cognitive tasks.

Functional connectivity and laterality of the motor and sensory components in the volitional swallowing network.

Functional neuroimaging has shown that multiple brain regions are active during volitional swallowing. Little is known, however, about which regions integrate motor execution and sensory feedback in the swallowing system. Although unilateral brain lesions in either hemisphere can produce swallowing deficits, some functional neuroimaging studies indicate that the left hemisphere has greater activation in certain sensory and motor-related swallowing regions. In this study, correlation coefficients were computed for five seed regions during volitional saliva swallowing to determine the functional relationships of these regions with the rest of the brain: the anterior and posterior insula, inferior frontal gyrus (BA44), primary sensory cortex (S1), and primary motor cortex (M1). A laterality index (LI) was derived that accounts for relative differences in total, positive connected voxels for the left/right hemisphere seeds. Clusters of significantly connected voxels were greater from the anterior and posterior insula than from the other three seed regions. Interactions of the insula with other brain regions were greater on the left than on the right during volitional swallowing. Group means showed laterality in the anterior insula (LI = 0.25) and the posterior insula (LI = 0.33). BA44 showed a lesser degree of difference in left versus right hemisphere interactions (LI = 0.12) while S1 did not show lateralization (LI = 0.02) and M1 showed some predominance of interactions in the right hemisphere (LI = -0.19). The greater connectivity from the left hemisphere insula to brain regions within and across hemispheres suggests that the insula is a primary integrative region for volitional swallowing in humans.

Evidence of left inferior frontal-premotor structural and functional connectivity deficits in adults who stutter.

The neurophysiological basis for stuttering may involve deficits that affect dynamic interactions among neural structures supporting fluid speech processing. Here, we examined functional and structural connectivity within corticocortical and thalamocortical loops in adults who stutter. For functional connectivity, we placed seeds in the left and right inferior frontal Brodmann area 44 (BA44) and in the ventral lateral nucleus (VLN) of the thalamus. Subject-specific seeds were based on peak activation voxels captured during speech and nonspeech tasks using functional magnetic resonance imaging. Psychophysiological interaction (PPI) was used to find brain regions with heightened functional connectivity with these cortical and subcortical seeds during speech and nonspeech tasks. Probabilistic tractography was used to track white matter tracts in each hemisphere using the same seeds. Both PPI and tractrography supported connectivity deficits between the left BA44 and the left premotor regions, while connectivity among homologous right hemisphere structures was significantly increased in the stuttering group. No functional connectivity differences between BA44 and auditory regions were found between groups. The functional connectivity results derived from the VLN seeds were less definitive and were not supported by the tractography results. Our data provide strongest support for deficient left hemisphere inferior frontal to premotor connectivity as a neural correlate of stuttering.

The Spatiotemporal Neural Dynamics of Intersensory Attention Capture of Salient Stimuli: A Large-Scale Auditory-Visual Modeling Study.

The spatiotemporal dynamics of the neural mechanisms underlying endogenous (top-down) and exogenous (bottom-up) attention, and how attention is controlled or allocated in intersensory perception are not fully understood. We investigated these issues using a biologically realistic large-scale neural network model of visual-auditory object processing of short-term memory. We modeled and incorporated into our visual-auditory object-processing model the temporally changing neuronal mechanisms for the control of endogenous and exogenous attention. The model successfully performed various bimodal working memory tasks, and produced simulated behavioral and neural results that are consistent with experimental findings. Simulated fMRI data were generated that constitute predictions that human experiments could test. Furthermore, in our visual-auditory bimodality simulations, we found that increased working memory load in one modality would reduce the distraction from the other modality, and a possible network mediating this effect is proposed based on our model.

Hierarchical auditory processing directed rostrally along the monkey's supratemporal plane.

Connectional anatomical evidence suggests that the auditory core, containing the tonotopic areas A1, R, and RT, constitutes the first stage of auditory cortical processing, with feedforward projections from core outward, first to the surrounding auditory belt and then to the parabelt. Connectional evidence also raises the possibility that the core itself is serially organized, with feedforward projections from A1 to R and with additional projections, although of unknown feed direction, from R to RT. We hypothesized that area RT together with more rostral parts of the supratemporal plane (rSTP) form the anterior extension of a rostrally directed stimulus quality processing stream originating in the auditory core area A1. Here, we analyzed auditory responses of single neurons in three different sectors distributed caudorostrally along the supratemporal plane (STP): sector I, mainly area A1; sector II, mainly area RT; and sector III, principally RTp (the rostrotemporal polar area), including cortex located 3 mm from the temporal tip. Mean onset latency of excitation responses and stimulus selectivity to monkey calls and other sounds, both simple and complex, increased progressively from sector I to III. Also, whereas cells in sector I responded with significantly higher firing rates to the "other" sounds than to monkey calls, those in sectors II and III responded at the same rate to both stimulus types. The pattern of results supports the proposal that the STP contains a rostrally directed, hierarchically organized auditory processing stream, with gradually increasing stimulus selectivity, and that this stream extends from the primary auditory area to the temporal pole.

Functional but not structural networks of the human laryngeal motor cortex show left hemispheric lateralization during syllable but not breathing production.

The laryngeal motor cortex (LMC) is indispensible for the vocal motor control of speech and song production. Patients with bilateral lesions in this region are unable to speak and sing, although their nonverbal vocalizations, such as laughter and cry, are preserved. Despite the importance of the LMC in the control of voluntary voice production in humans, the literature describing its connections remains sparse. We used diffusion tensor probabilistic tractography and functional magnetic resonance imaging-based functional connectivity analysis to identify LMC networks controlling two tasks necessary for speech production: voluntary voice as repetition of two different syllables and voluntary breathing as controlled inspiration and expiration. Peaks of activation during all tasks were found in the bilateral ventral primary motor cortex in close proximity to each other. Functional networks of the LMC during voice production but not during controlled breathing showed significant left-hemispheric lateralization (p < 0.0005). However, structural networks of the LMC associated with both voluntary voice production and controlled breathing had bilateral hemispheric organization. Our findings indicate the presence of a common bilateral structural network of the LMC, upon which different functional networks are built to control various voluntary laryngeal tasks. Bilateral organization of functional LMC networks during controlled breathing supports its indispensible role in all types of laryngeal behaviors. Significant left-hemispheric lateralization of functional networks during simple but highly learned voice production suggests the readiness of the LMC network for production of a complex voluntary behavior, such as human speech.

Identification and validation of effective connectivity networks in functional magnetic resonance imaging using switching linear dynamic systems.

Dynamic connectivity networks identify directed interregional interactions between modeled brain regions in neuroimaging. However, problems arise when the regions involved in a task and their interconnections are not fully known a priori. Objective measures of model adequacy are necessary to validate such models. We present a connectivity formalism, the Switching Linear Dynamic System (SLDS), that is capable of identifying both Granger-Geweke and instantaneous connectivity that vary according to experimental conditions. SLDS explicitly models the task condition as a Markov random variable. The series of task conditions can be estimated from new data given an identified model providing a means to validate connectivity patterns. We use SLDS to model functional magnetic resonance imaging data from five regions during a finger alternation task. Using interregional connectivity alone, the identified model predicted the task condition vector from a different subject with a different task ordering with high accuracy. In addition, important regions excluded from a model can be identified by augmenting the model state space. A motor task model excluding primary motor cortices was augmented with a new neural state constrained by its connectivity with the included regions. The augmented variable time series, convolved with a hemodynamic kernel, was compared to all brain voxels. The right primary motor cortex was identified as the best region to add to the model. Our results suggest that the SLDS model framework is an effective means to address several problems with modeling connectivity including measuring overall model adequacy and identifying important regions missing from models.

Imaging systems level consolidation of novel associate memories: a longitudinal neuroimaging study.

Previously, a standard theory of systems level memory consolidation was developed to describe how memory recall becomes independent of the medial temporal memory system. More recently, an extended consolidation theory was proposed that predicts seven changes in regional neural activity and inter-regional functional connectivity. Using longitudinal event-related functional magnetic resonance imaging of an associate memory task, we simultaneously tested all predictions and additionally tested for consolidation-related changes in recall of associate memories at a sub-trial temporal resolution, analyzing cue, delay and target periods of each trial separately. Results consistent with the theoretical predictions were observed though two inconsistent results were also obtained. In particular, while medial temporal recall related delay period activity decreased with consolidation as predicted, visual cue activity increased for consolidated memories. Though the extended theory of memory consolidation is largely supported by our study, these results suggest that the extended theory needs further refinement and the medial temporal memory system has multiple, temporally distinct roles in associate memory recall. Neuroimaging analysis at a sub-trial temporal resolution, as used here, may further clarify the role of the hippocampal complex in memory consolidation.

From phonemes to articulatory codes: an fMRI study of the role of Broca's area in speech production.

We used event-related functional magnetic resonance imaging to investigate 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. To answer this question, 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 high-sublexical 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 Broca's area, the pars opercularis.

How well does structural equation modeling reveal abnormal brain anatomical connections? An fMRI simulation study.

Many brain disorders result from alterations in the strength of anatomical connectivity between different brain regions. This study investigates whether such alterations can be revealed by examining differences in interregional effective connectivity between patient and normal subjects. We applied one prominent effective connectivity method - Structural Equation Modeling (SEM) - to simulated functional MRI (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-to-sample (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. Finally, we found that of the SEM model measures we examined, the total error variance was the best at distinguishing a patient network from a normal network. 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.

The Brain Connectivity Workshops: moving the frontiers of computational systems neuroscience.

Understanding the link between neurobiology and cognition requires that neuroscience moves beyond mere structure-function correlations. An explicit systems perspective is needed in which putative mechanisms of how brain function is constrained by brain structure are mathematically formalized and made accessible for experimental investigation. Such a systems approach critically rests on a better understanding of brain connectivity in its various forms. Since 2002, frontier topics of connectivity and neural system analysis have been discussed in a multidisciplinary annual meeting, the Brain Connectivity Workshop (BCW), bringing together experimentalists and theorists from various fields. This article summarizes some of the main discussions at the two most recent workshops, 2006 at Sendai, Japan, and 2007 at Barcelona, Spain: (i) investigation of cortical micro- and macrocircuits, (ii) models of neural dynamics at multiple scales, (iii) analysis of "resting state" networks, and (iv) linking anatomical to functional connectivity. Finally, we outline some central challenges and research trajectories in computational systems neuroscience for the next years.

Investigating the neural basis for fMRI-based functional connectivity in a blocked design: application to interregional correlations and psycho-physiological interactions.

This paper investigates how well different kinds of fMRI functional connectivity analysis reflect the underlying interregional neural interactions. This is hard to evaluate using real experimental data where such relationships are unknown. Rather, we use a biologically realistic neural model to simulate both neuronal activities and multiregional fMRI data from a blocked design. Because we know how every element in the model is related to every other element, we can compare functional connectivity measurements across different spatial and temporal scales. We focus on (1) psycho-physiological interaction (PPI) analysis, which is a simple brain connectivity method that characterizes the activity in one brain region by the interaction between another region's activity and a psychological factor, and (2) interregional correlation analysis. We investigated the neurobiological underpinnings of PPI using simulated neural activities and fMRI signals generated by a large-scale neural model that performs a visual delayed match-to-sample task. Simulated fMRI data are generated by convolving integrated synaptic activities (ISAs) with a hemodynamic response function. The simulation was done under three task conditions: high-attention, low-attention and a control task ('passive viewing'). We investigated how biological and scanning parameters affect PPI and compared these with functional connectivity measures obtained using correlation analysis. We performed correlational and PPI analyses with three types of time-series data: ISA, fMRI and deconvolved fMRI (which yields estimated neural signals) obtained using a deconvolution algorithm. The simulated ISA can be considered as the 'gold standard' because it represents the underlying neural activity. Our main findings show (1) that evaluating the change in an interregional functional connection using the difference in regression coefficients (as is essentially done in the PPI method) produces results that better reflect the underlying changes in neural interrelationships than does evaluating the functional connectivity difference as a change in correlation coefficient; (2) that using fMRI and deconvolved fMRI data led to similar conclusions in the PPI-based functional connectivity results, and these generally agreed with the nature of the underlying neural interactions; and (3) the functional connectivity correlation measures often led to different conclusions regarding significance for different scanning and hemodynamic parameters, but the significances of the PPI regression parameters were relatively robust. These results highlight the way in which neural modeling can be used to help validate the inferences one can make about functional connectivity based on fMRI data.