Neural modeling and functional brain imaging: an overview

First activity and interactions in thalamus and cortex using raw single-trial EEG and MEG elicited by somatosensory stimulation

Introduction

One of the primary motivations for studying the human brain is to comprehend how external sensory input is processed and ultimately perceived by the brain. A good understanding of these processes can promote the identification of biomarkers for the diagnosis of various neurological disorders; it can also provide ways of evaluating therapeutic techniques. In this work, we seek the minimal requirements for identifying key stages of activity in the brain elicited by median nerve stimulation.

Methods

We have used a priori knowledge and applied a simple, linear, spatial filter on the electroencephalography and magnetoencephalography signals to identify the early responses in the thalamus and cortex evoked by short electrical stimulation of the median nerve at the wrist. The spatial filter is defined first from the average EEG and MEG signals and then refined using consistency selection rules across ST. The refined spatial filter is then applied to extract the timecourses of each ST in each targeted generator. These ST timecourses are studied through clustering to quantify the ST variability. The nature of ST connectivity between thalamic and cortical generators is then studied within each identified cluster using linear and non-linear algorithms with time delays to extract linked and directional activities. A novel combination of linear and non-linear methods provides in addition discrimination of influences as excitatory or inhibitory.

Results

Our method identifies two key aspects of the evoked response. Firstly, the early onset of activity in the thalamus and the somatosensory cortex, known as the P14 and P20 in EEG and the second M20 for MEG. Secondly, good estimates are obtained for the early timecourse of activity from these two areas. The results confirm the existence of variability in ST brain activations and reveal distinct and novel patterns of connectivity in different clusters.

Discussion

It has been demonstrated that we can extract new insights into stimulus processing without the use of computationally costly source reconstruction techniques which require assumptions and detailed modeling of the brain. Our methodology, thanks to its simplicity and minimal computational requirements, has the potential for real-time applications such as in neurofeedback systems and brain-computer interfaces.

Whole-Brain Network Models: From Physics to Bedside

Computational neuroscience has come a long way from its humble origins in the pioneering work of Hodgkin and Huxley. Contemporary computational models of the brain span multiple spatiotemporal scales, from single neuronal compartments to models of social cognition. Each spatial scale comes with its own unique set of promises and challenges. Here, we review models of large-scale neural communication facilitated by white matter tracts, also known as whole-brain models (WBMs). Whole-brain approaches employ inputs from neuroimaging data and insights from graph theory and non-linear systems theory to model brain-wide dynamics. Over the years, WBM models have shown promise in providing predictive insights into various facets of neuropathologies such as Alzheimer's disease, Schizophrenia, Epilepsy, Traumatic brain injury, while also offering mechanistic insights into large-scale cortical communication. First, we briefly trace the history of WBMs, leading up to the state-of-the-art. We discuss various methodological considerations for implementing a whole-brain modeling pipeline, such as choice of node dynamics, model fitting and appropriate parcellations. We then demonstrate the applicability of WBMs toward understanding various neuropathologies. We conclude by discussing ways of augmenting the biological and clinical validity of whole-brain models.

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.

Inference of brain networks with approximate Bayesian computation - assessing face validity with an example application in Parkinsonism

This paper describes and validates a novel framework using the Approximate Bayesian Computation (ABC) algorithm for parameter estimation and model selection in models of mesoscale brain network activity. We provide a proof of principle, first pass validation of this framework using a set of neural mass models of the cortico-basal ganglia thalamic circuit inverted upon spectral features from experimental, in vivo recordings. This optimization scheme relaxes an assumption of fixed-form posteriors (i.e. the Laplace approximation) taken in previous approaches to inverse modelling of spectral features. This enables the exploration of model dynamics beyond that approximated from local linearity assumptions and so fit to explicit, numerical solutions of the underlying non-linear system of equations. In this first paper, we establish a face validation of the optimization procedures in terms of: (i) the ability to approximate posterior densities over parameters that are plausible given the known causes of the data; (ii) the ability of the model comparison procedures to yield posterior model probabilities that can identify the model structure known to generate the data; and (iii) the robustness of these procedures to local minima in the face of different starting conditions. Finally, as an illustrative application we show (iv) that model comparison can yield plausible conclusions given the known neurobiology of the cortico-basal ganglia-thalamic circuit in Parkinsonism. These results lay the groundwork for future studies utilizing highly nonlinear or brittle models that can explain time dependant dynamics, such as oscillatory bursts, in terms of the underlying neural circuits.

Changes in EEG Brain Connectivity Caused by Short-Term BCI Neurofeedback-Rehabilitation Training: A Case Study

Background

In combined with neurofeedback, Motor Imagery (MI) based Brain-Computer Interface (BCI) has been an effective long-term treatment therapy for motor dysfunction caused by neurological injury in the brain (e.g., post-stroke hemiplegia). However, individual neurological differences have led to variability in the single sessions of rehabilitation training. Research on the impact of short training sessions on brain functioning patterns can help evaluate and standardize the short duration of rehabilitation training. In this paper, we use the electroencephalogram (EEG) signals to explore the brain patterns’ changes after a short-term rehabilitation training.

Materials and Methods

Using an EEG-BCI system, we analyzed the changes in short-term (about 1-h) MI training data with and without visual feedback, respectively. We first examined the EEG signal’s Mu band power’s attenuation caused by Event-Related Desynchronization (ERD). Then we use the EEG’s Event-Related Potentials (ERP) features to construct brain networks and evaluate the training from multiple perspectives: small-scale based on single nodes, medium-scale based on hemispheres, and large-scale based on all-brain.

Results

Results showed no significant difference in the ERD power attenuation estimation in both groups. But the neurofeedback group’s ERP brain network parameters had substantial changes and trend properties compared to the group without feedback. The neurofeedback group’s Mu band power’s attenuation increased but not significantly (fitting line slope = 0.2, t-test value p > 0.05) after the short-term MI training, while the non-feedback group occurred an insignificant decrease (fitting line slope = −0.4, t-test value p > 0.05). In the ERP-based brain network analysis, the neurofeedback group’s network parameters were attenuated in all scales significantly (t-test value: p < 0.01); while the non-feedback group’s most network parameters didn’t change significantly (t-test value: p > 0.05).

Conclusion

The MI-BCI training’s short-term effects does not show up in the ERD analysis significantly but can be detected by ERP-based network analysis significantly. Results inspire the efficient evaluation of short-term rehabilitation training and provide a useful reference for subsequent studies.

Quantitative Assessment of Resting-State for Mild Cognitive Impairment Detection: A Functional Near-Infrared Spectroscopy and Deep Learning Approach

BACKGROUND

Mild cognitive impairment (MCI) is considered a prodromal stage of Alzheimer's disease. Early diagnosis of MCI can allow for treatment to improve cognitive function and reduce modifiable risk factors.

OBJECTIVE

This study aims to investigate the feasibility of individual MCI detection from healthy control (HC) using a minimum duration of resting-state functional near-infrared spectroscopy (fNIRS) signals.

METHODS

In this study, nine different measurement durations (i.e., 30, 60, 90, 120, 150, 180, 210, 240, and 270 s) were evaluated for MCI detection via the graph theory analysis and traditional machine learning approach, such as linear discriminant analysis, support vector machine, and K-nearest neighbor algorithms. Moreover, feature representation- and classification-based transfer learning (TL) methods were applied to identify MCI from HC through the input of connectivity maps with 30 and 90 s duration.

RESULTS

There was no significant difference among the nine various time windows in the machine learning and graph theory analysis. The feature representation-based TL showed improved accuracy in both 30 and 90 s cases (i.e., 30 s: 81.27% and 90 s: 76.73%). Notably, the classification-based TL method achieved the highest accuracy of 95.81% using the pre-trained convolutional neural network (CNN) model with the 30 s interval functional connectivity map input.

CONCLUSION

The results indicate that a 30 s measurement of the resting-state with fNIRS could be used to detect MCI. Moreover, the combination of neuroimaging (e.g., functional connectivity maps) and deep learning methods (e.g., CNN and TL) can be considered as novel biomarkers for clinical computer-assisted MCI diagnosis.

What Can Computational Models Contribute to Neuroimaging Data Analytics?

Over the past years, nonlinear dynamical models have significantly contributed to the general understanding of brain activity as well as brain disorders. Appropriately validated and optimized mathematical models can be used to mechanistically explain properties of brain structure and neuronal dynamics observed from neuroimaging data. A thorough exploration of the model parameter space and hypothesis testing with the methods of nonlinear dynamical systems and statistical physics can assist in classification and prediction of brain states. On the one hand, such a detailed investigation and systematic parameter variation are hardly feasible in experiments and data analysis. On the other hand, the model-based approach can establish a link between empirically discovered phenomena and more abstract concepts of attractors, multistability, bifurcations, synchronization, noise-induced dynamics, etc. Such a mathematical description allows to compare and differentiate brain structure and dynamics in health and disease, such that model parameters and dynamical regimes may serve as additional biomarkers of brain states and behavioral modes. In this perspective paper we first provide very brief overview of the recent progress and some open problems in neuroimaging data analytics with emphasis on the resting state brain activity. We then focus on a few recent contributions of mathematical modeling to our understanding of the brain dynamics and model-based approaches in medicine. Finally, we discuss the question stated in the title. We conclude that incorporating computational models in neuroimaging data analytics as well as in translational medicine could significantly contribute to the progress in these fields.

Markov Model-Based Method to Analyse Time-Varying Networks in EEG Task-Related Data

The dynamic nature of functional brain networks is being increasingly recognized in cognitive neuroscience, and methods to analyse such time-varying networks in EEG/MEG data are required. In this work, we propose a pipeline to characterize time-varying networks in single-subject EEG task-related data and further, evaluate its validity on both simulated and experimental datasets. Pre-processing is done to remove channel-wise and trial-wise differences in activity. Functional networks are estimated from short non-overlapping time windows within each “trial,” using a sparse-MVAR (Multi-Variate Auto-Regressive) model. Functional “states” are then identified by partitioning the entire space of functional networks into a small number of groups/symbols via k-means clustering.The multi-trial sequence of symbols is then described by a Markov Model (MM). We show validity of this pipeline on realistic electrode-level simulated EEG data, by demonstrating its ability to discriminate “trials” from two experimental conditions in a range of scenarios. We then apply it to experimental data from two individuals using a Brain-Computer Interface (BCI) via a P300 oddball task. Using just the Markov Model parameters, we obtain statistically significant discrimination between target and non-target trials. The functional networks characterizing each ‘state’ were also highly similar between the two individuals. This work marks the first application of the Markov Model framework to infer time-varying networks from EEG/MEG data. Due to the pre-processing, results from the pipeline are orthogonal to those from conventional ERP averaging or a typical EEG microstate analysis. The results provide powerful proof-of-concept for a Markov model-based approach to analyzing the data, paving the way for its use to track rapid changes in interaction patterns as a task is being performed. MATLAB code for the entire pipeline has been made available.

Systems Neuroengineering: Understanding and Interacting with the Brain

In this paper, we review the current state-of-the-art techniques used for understanding the inner workings of the brain at a systems level. The neural activity that governs our everyday lives involves an intricate coordination of many processes that can be attributed to a variety of brain regions. On the surface, many of these functions can appear to be controlled by specific anatomical structures; however, in reality, numerous dynamic networks within the brain contribute to its function through an interconnected web of neuronal and synaptic pathways. The brain, in its healthy or pathological state, can therefore be best understood by taking a systems-level approach. While numerous neuroengineering technologies exist, we focus here on three major thrusts in the field of systems neuroengineering: neuroimaging, neural interfacing, and neuromodulation. Neuroimaging enables us to delineate the structural and functional organization of the brain, which is key in understanding how the neural system functions in both normal and disease states. Based on such knowledge, devices can be used either to communicate with the neural system, as in neural interface systems, or to modulate brain activity, as in neuromodulation systems. The consideration of these three fields is key to the development and application of neuro-devices. Feedback-based neuro-devices require the ability to sense neural activity (via a neuroimaging modality) through a neural interface (invasive or noninvasive) and ultimately to select a set of stimulation parameters in order to alter neural function via a neuromodulation modality. Systems neuroengineering refers to the use of engineering tools and technologies to image, decode, and modulate the brain in order to comprehend its functions and to repair its dysfunction. Interactions between these fields will help to shape the future of systems neuroengineering-to develop neurotechniques for enhancing the understanding of whole-brain function and dysfunction, and the management of neurological and mental disorders.

Modeling Neurovascular Coupling from Clustered Parameter Sets for Multimodal EEG-NIRS

Despite significant improvements in neuroimaging technologies and analysis methods, the fundamental relationship between local changes in cerebral hemodynamics and the underlying neural activity remains largely unknown. In this study, a data driven approach is proposed for modeling this neurovascular coupling relationship from simultaneously acquired electroencephalographic (EEG) and near-infrared spectroscopic (NIRS) data. The approach uses gamma transfer functions to map EEG spectral envelopes that reflect time-varying power variations in neural rhythms to hemodynamics measured with NIRS during median nerve stimulation. The approach is evaluated first with simulated EEG-NIRS data and then by applying the method to experimental EEG-NIRS data measured from 3 human subjects. Results from the experimental data indicate that the neurovascular coupling relationship can be modeled using multiple sets of gamma transfer functions. By applying cluster analysis, statistically significant parameter sets were found to predict NIRS hemodynamics from EEG spectral envelopes. All subjects were found to have significant clustered parameters (P < 0.05) for EEG-NIRS data fitted using gamma transfer functions. These results suggest that the use of gamma transfer functions followed by cluster analysis of the resulting parameter sets may provide insights into neurovascular coupling in human neuroimaging data.

Investigation of the cortical activation by touching fabric actively using fingers

BACKGROUND/PURPOSE

Human subjects can tactually estimate the perception of touching fabric. Although many psychophysical and neurophysiological experiments have elucidated the peripheral neural mechanisms that underlie fabric hand estimation, the associated cortical mechanisms are not well understood.

METHODS

To identify the brain regions responsible for the tactile stimulation of fabric against human skin, we used the technology of functional magnetic resonance imaging (fMRI), to observe brain activation when the subjects touched silk fabric actively using fingers.

RESULTS

Consistent with previous research about brain cognition on sensory stimulation, large activation in the primary somatosensory cortex (SI), the secondary somatosensory cortex (SII) and moto cortex, and little activation in the posterior insula cortex and Broca's Area were observed when the subjects touched silk fabric.

CONCLUSION

The technology of fMRI is a promising tool to observe and characterize the brain cognition on the tactile stimulation of fabric quantitatively. The intensity and extent of activation in the brain regions, especially the primary somatosensory cortex (SI) and the secondary somatosensory cortex (SII), can represent the perception of stimulation of fabric quantitatively.

Brain imaging and human nutrition: which measures to use in intervention studies?

The present review describes brain imaging technologies that can be used to assess the effects of nutritional interventions in human subjects. Specifically, we summarise the biological relevance of their outcome measures, practical use and feasibility, and recommended use in short- and long-term nutritional studies. The brain imaging technologies described consist of MRI, including diffusion tensor imaging, magnetic resonance spectroscopy and functional MRI, as well as electroencephalography/magnetoencephalography, near-IR spectroscopy, positron emission tomography and single-photon emission computerised tomography. In nutritional interventions and across the lifespan, brain imaging can detect macro- and microstructural, functional, electrophysiological and metabolic changes linked to broader functional outcomes, such as cognition. Imaging markers can be considered as specific for one or several brain processes and as surrogate instrumental endpoints that may provide sensitive measures of short- and long-term effects. For the majority of imaging measures, little information is available regarding their correlation with functional endpoints in healthy subjects; therefore, imaging markers generally cannot replace clinical endpoints that reflect the overall capacity of the brain to behaviourally respond to specific situations and stimuli. The principal added value of brain imaging measures for human nutritional intervention studies is their ability to provide unique in vivo information on the working mechanism of an intervention in hypothesis-driven research. Selection of brain imaging techniques and target markers within a given technique should mainly depend on the hypothesis regarding the mechanism of action of the intervention, level (structural, metabolic or functional) and anticipated timescale of the intervention's effects, target population, availability and costs of the techniques.

Acute NK₁ receptor antagonist administration affects reward incentive anticipation processing in healthy volunteers

The primary brain structures of reward processing are mainly situated in the mid-brain dopamine system. The nucleus accumbens (NAc) receives dopaminergic projections from the ventral tegmental area and works as a key brain region for the positive incentive value of rewards. Because neurokinin-1 (NK₁) receptor, the cognate receptor for substance P (SP), is highly expressed in the NAc, we hypothesized that the SP/NK₁ receptor system might play a role in positive reward processing in the NAc in humans. Therefore, we conducted a functional MRI (fMRI) study to assess the effects of an NK₁ receptor antagonist on human reward processing through a monetary incentive delay task that is known to elicit robust activation in the NAc especially during gain anticipation. Eighteen healthy adults participated in two series of an fMRI study, taking either a placebo or the NK₁ receptor antagonist aprepitant. Behavioural measurements revealed that there was no significant difference in reaction time, hit rate, or self-reported effort for incentive cues between the placebo and aprepitant treatments. fMRI showed significant decrease in blood oxygenation-level-dependent signals in the NAc during gain anticipation with the aprepitant treatment compared to the placebo treatment. These results suggest that SP/NK₁ receptor system is involved in processing of positive incentive anticipation and plays a role in accentuating positive valence in association with the primary dopaminergic pathways in the reward circuit.

Predictors of remission in depression to individual and combined treatments (PReDICT): study protocol for a randomized controlled trial

BACKGROUND

Limited controlled data exist to guide treatment choices for clinicians caring for patients with major depressive disorder (MDD). Although many putative predictors of treatment response have been reported, most were identified through retrospective analyses of existing datasets and very few have been replicated in a manner that can impact clinical practice. One major confound in previous studies examining predictors of treatment response is the patient's treatment history, which may affect both the predictor of interest and treatment outcomes. Moreover, prior treatment history provides an important source of selection bias, thereby limiting generalizability. Consequently, we initiated a randomized clinical trial designed to identify factors that moderate response to three treatments for MDD among patients never treated previously for the condition.

METHODS/DESIGN

Treatment-naïve adults aged 18 to 65 years with moderate-to-severe, non-psychotic MDD are randomized equally to one of three 12-week treatment arms: (1) cognitive behavior therapy (CBT, 16 sessions); (2) duloxetine (30-60 mg/d); or (3) escitalopram (10-20 mg/d). Prior to randomization, patients undergo multiple assessments, including resting state functional magnetic resonance imaging (fMRI), immune markers, DNA and gene expression products, and dexamethasone-corticotropin-releasing hormone (Dex/CRH) testing. Prior to or shortly after randomization, patients also complete a comprehensive personality assessment. Repeat assessment of the biological measures (fMRI, immune markers, and gene expression products) occurs at an early time-point in treatment, and upon completion of 12-week treatment, when a second Dex/CRH test is also conducted. Patients remitting by the end of this acute treatment phase are then eligible to enter a 21-month follow-up phase, with quarterly visits to monitor for recurrence. Non-remitters are offered augmentation treatment for a second 12-week course of treatment, during which they receive a combination of CBT and antidepressant medication. Predictors of the primary outcome, remission, will be identified for overall and treatment-specific effects, and a statistical model incorporating multiple predictors will be developed to predict outcomes.

DISCUSSION

The PReDICT study's evaluation of biological, psychological, and clinical factors that may differentially impact treatment outcomes represents a sizeable step toward developing personalized treatments for MDD. Identified predictors should help guide the selection of initial treatments, and identify those patients most vulnerable to recurrence, who thus warrant maintenance or combination treatments to achieve and maintain wellness.

Is Social Phobia a "Mis-Communication" Disorder? Brain Functional Connectivity during Face Perception Differs between Patients with Social Phobia and Healthy Control Subjects

Recently, a differential recruitment of brain areas throughout the distributed neural system for face perception has been found in social phobic patients as compared to healthy control subjects. These functional abnormalities in social phobic patients extend beyond emotion-related brain areas, such as the amygdala, to include cortical networks that modulate attention and process other facial features, and they are also associated with an alteration of the task-related activation/deactivation trade-off. Functional connectivity is becoming a powerful tool to examine how components of large-scale distributed neural systems are coupled together while performing a specific function. This study was designed to determine whether functional connectivity networks among brain regions within the distributed system for face perception also would differ between social phobic patients and healthy controls. Data were obtained from eight social phobic patients and seven healthy controls by using functional magnetic resonance imaging. Our findings indicated that social phobic patients and healthy controls have different patterns of functional connectivity across brain regions within both the core and the extended systems for face perception and the default mode network. To our knowledge, this is the first study that shows that functional connectivity during brain response to socially relevant stimuli differs between social phobic patients and healthy controls. These results expand our previous findings and indicate that brain functional changes in social phobic patients are not restricted to a single specific brain structure, but rather involve a mis-communication among different sensory and emotional processing brain areas.

Alcohol induced region-dependent alterations of hemodynamic response: implications for the statistical interpretation of pharmacological fMRI studies

Worldwide, ethanol abuse causes thousands of fatal accidents annually as well as innumerable social dysfunctions and severe medical disorders. Yet, few studies have used the blood oxygenation level dependent functional magnetic resonance imaging method (BOLD fMRI) to map how alcohol alters brain functions, as fMRI relies on neurovascular coupling, which may change due to the vasoactive properties of alcohol. We monitored the hemodynamic response function (HRF) with a high temporal resolution. In both motor cortices and the visual cortex, alcohol prolonged the time course of the HRF, indicating an overall slow-down of neurovascular coupling rather than an isolated reduction in neuronal activity. However, in the supplementary motor area, alcohol-induced changes to the HRF suggest a reduced neuronal activation. This may explain why initiating and coordinating complex movements, including speech production, are often impaired earlier than executing basic motor patterns. Furthermore, the present study revealed a potential pitfall associated with the statistical interpretation of pharmacological fMRI studies based on the general linear model: if the functional form of the HRF is changed between the conditions data may be erroneously interpreted as increased or decreased neuronal activation. Thus, our study not only presents an additional key to how alcohol affects the network of brain functions but also implies that potential changes to neurovascular coupling have to be taken into account when interpreting BOLD fMRI. Therefore, measuring individual drug-induced HRF changes is recommended for pharmacological fMRI.

A Dynamic Causal Model of the Coupling Between Pulse Stimulation and Neural Activity

We present a dynamic causal model that can explain context-dependent changes in neural responses, in the rat barrel cortex, to an electrical whisker stimulation at different frequencies. Neural responses were measured in terms of local field potentials. These were converted into current source density (CSD) data, and the time series of the CSD sink was extracted to provide a time series response train. The model structure consists of three layers (approximating the responses from the brain stem to the thalamus and then the barrel cortex), and the latter two layers contain nonlinearly coupled modules of linear second-order dynamic systems. The interaction of these modules forms a nonlinear regulatory system that determines the temporal structure of the neural response amplitude for the thalamic and cortical layers.

The model is based on the measured population dynamics of neurons rather than the dynamics of a single neuron and was evaluated against CSD data from experiments with varying stimulation frequency (1–40 Hz), random pulse trains, and awake and anesthetized animals. The model parameters obtained by optimization for different physiological conditions (anesthetized or awake) were significantly different.

Following Friston, Mechelli, Turner, and Price ( 2000 ), this work is part of a formal mathematical system currently being developed (Zheng et al., 2005 ) that links stimulation to the blood oxygen level dependent (BOLD) functional magnetic resonance imaging (fMRI) signal through neural activity and hemodynamic variables. The importance of the model described here is that it can be used to invert the hemodynamic measurements of changes in blood flow to estimate the underlying neural activity.

Alterations in functional connectivity for language in prematurely born adolescents

Recent data suggest recovery of language systems but persistent structural abnormalities in the prematurely born. We tested the hypothesis that subjects who were born prematurely develop alternative networks for processing language. Subjects who were born prematurely (n = 22; 600–1250 g birth weight), without neonatal brain injury on neonatal cranial ultrasound, and 26 term control subjects were examined with a functional magnetic resonance imaging (fMRI) semantic association task, the Wechsler Intelligence Scale for Children-III (WISC-III) and the Clinical Evaluation of Language Fundamentals (CELF). In-magnet task accuracy and response times were calculated, and fMRI data were evaluated for the effect of group on blood oxygen level dependent (BOLD) activation, the correlation between task accuracy and activation and the functional connectivity between regions activating to task. Although there were differences in verbal IQ and CELF scores between the preterm (PT) and term control groups, there were no significant differences for either accuracy or response time for the in-magnet task. Both groups activated classic semantic processing areas including the left superior and middle temporal gyri and inferior frontal gyrus, and there was no significant difference in activation patterns between groups. Clear differences between the groups were observed in the correlation between task accuracy and activation to task at P< 0.01, corrected for multiple comparisons. Left inferior frontal gyrus correlated with accuracy only for term controls and left sensory motor areas correlated with accuracy only for PT subjects. Left middle temporal gyri correlated with task accuracy for both groups. Connectivity analyses at P< 0.001 revealed the importance of a circuit between left middle temporal gyri and inferior frontal gyrus for both groups. In addition, the PT subjects evidenced greater connectivity between traditional language areas and sensory motor areas but significantly fewer correlated areas within the frontal lobes when compared to term controls. We conclude that at 12 years of age, children born prematurely and children born at term had no difference in performance on a simple lexical semantic processing task and activated similar areas. Connectivity analyses, however, suggested that PT subjects rely upon different neural pathways for lexical semantic processing when compared to term controls. Plasticity in network connections may provide the substrate for improving language skills in the prematurely born.

Agent-based brain modeling by means of hierarchical cooperative coevolution

We address the development of brain-inspired models that will be embedded in robotic systems to support their cognitive abilities. We introduce a novel agent-based coevolutionary computational framework for modeling assemblies of brain areas. Specifically, self-organized agent structures are employed to represent brain areas. In order to support the design of agents, we introduce a hierarchical cooperative coevolutionary (HCCE) scheme that effectively specifies the structural details of autonomous, yet cooperating system components. The design process is facilitated by the capability of the HCCE-based design mechanism to investigate the performance of the model in lesion conditions. Interestingly, HCCE also provides a consistent mechanism to reconfigure (if necessary) the structure of agents, facilitating follow-up modeling efforts. Implemented models are embedded in a simulated robot to support its behavioral capabilities, also demonstrating the validity of the proposed computational framework.

Neural specializations for speech and pitch: moving beyond the dichotomies

The idea that speech processing relies on unique, encapsulated, domain-specific mechanisms has been around for some time. Another well-known idea, often espoused as being in opposition to the first proposal, is that processing of speech sounds entails general-purpose neural mechanisms sensitive to the acoustic features that are present in speech. Here, we suggest that these dichotomous views need not be mutually exclusive. Specifically, there is now extensive evidence that spectral and temporal acoustical properties predict the relative specialization of right and left auditory cortices, and that this is a parsimonious way to account not only for the processing of speech sounds, but also for non-speech sounds such as musical tones. We also point out that there is equally compelling evidence that neural responses elicited by speech sounds can differ depending on more abstract, linguistically relevant properties of a stimulus (such as whether it forms part of one's language or not). Tonal languages provide a particularly valuable window to understand the interplay between these processes. The key to reconciling these phenomena probably lies in understanding the interactions between afferent pathways that carry stimulus information, with top-down processing mechanisms that modulate these processes. Although we are still far from the point of having a complete picture, we argue that moving forward will require us to abandon the dichotomy argument in favour of a more integrated approach.

A link between neuroscience and informatics: large-scale modeling of memory processes

Utilizing advances in functional neuroimaging and computational neural modeling, neuroscientists have increasingly sought to investigate how distributed networks, composed of functionally defined subregions, combine to produce cognition. Large-scale, biologically realistic neural models, which integrate data from cellular, regional, whole brain, and behavioral sources, delineate specific hypotheses about how these interacting neural populations might carry out high-level cognitive tasks. In this review, we discuss neuroimaging, neural modeling, and the utility of large-scale biologically realistic models using modeling of short-term memory as an example. We present a sketch of the data regarding the neural basis of short-term memory from non-human electrophysiological, computational and neuroimaging perspectives, highlighting the multiple interacting brain regions believed to be involved. Through a review of several efforts, including our own, to combine neural modeling and neuroimaging data, we argue that large scale neural models provide specific advantages in understanding the distributed networks underlying cognition and behavior.

Direct measure of local region functional connectivity by multivariate correlation technique

In order to identify the local areas whose activity are most similar with region of interest (ROI), we usually compute the correlation of fMRI data for the brain functional connectivity. The fMRI data is usually noisy, extraction of functional connectivity with the voxel by voxel based method such as Pearson correlation analysis is not robust. Many people smooth the fMRI data before compute the correlation coefficient, which only makes the effect worse, because some useful original information is lost during the smoothing. Here, we analyzed this issue in details and improved the data processing flow to make the result better. Furthermore, a new criterion RV correlation coefficient was introduced in this article to measure the correlation between two local brain regions; this multivariate correlation technique applied the spatiotemporal information within the local regions to measure the similarity of the activity in different brain regions. We compared four different strategies mentioned above to detect the functional connectivity on the simulated and real fMRI data, and the results demonstrated that the RV-coefficient method obtained the best performance.

Immediate memory consequences of the effect of emotion on attention to pictures

Emotionally arousing stimuli are at once both highly attention grabbing and memorable. We examined whether emotional enhancement of memory (EEM) reflects an indirect effect of emotion on memory, mediated by enhanced attention to emotional items during encoding. We tested a critical prediction of the mediation hypothesis-that regions conjointly activated by emotion and attention would correlate with subsequent EEM. Participants were scanned with fMRI while they watched emotional or neutral pictures under instructions to attend to them a lot or a little, and were then given an immediate recognition test. A region in the left fusiform gyrus was activated by emotion, voluntary attention, and subsequent EEM. A functional network, different for each attention condition, connected this region and the amygdala, which was associated with emotion and EEM, but not with voluntary attention. These findings support an indirect cortical mediation account of immediate EEM that may complement a direct modulation model.

Right temporopolar activation associated with unique perception

Unique mode of perception, or the ability to see things differently from others, is one of the psychological resources required for creative mental activities. Behavioral studies using ambiguous visual stimuli have successfully induced diverse responses from subjects, and the unique responses defined in this paradigm were observed in higher frequency in the artistic population as compared to the nonartistic population. However, the neural substrates that underlie such unique perception have yet to be investigated. In the present study, ten ambiguous figures were used as stimuli. The subjects were instructed to say what the figures looked like during functional MRI scanning. The responses were classified as "frequent", "infrequent" or "unique" responses based on the appearance frequency of the same response in an independent age- and gender-matched control group. An event-related analysis contrasting unique vs. frequent responses revealed the greatest activation in the right temporal pole, which survived a whole brain multiple comparison. An alternative parametric modulation analysis was also performed to show that potentially confounding perceptual effects deriving from differences in visual stimuli make no significant contribution to this temporopolar activation. Previous neuroimaging and neuropsychological studies have shown the involvement of the temporal pole in perception-emotion linkage. Thus, our results suggest that unique perception is produced by the integration of perceptual and emotional processes, and this integration might underlie essential parts of creative mental activities.

The organization of thinking: what functional brain imaging reveals about the neuroarchitecture of complex cognition

Recent findings in brain imaging, particularly in fMRI, are beginning to reveal some of the fundamental properties of the organization of the cortical systems that underpin complex cognition. We propose an emerging set of operating principles that govern this organization, characterizing the system as a set of collaborating cortical centers that operate as a large-scale cortical network. Two of the network's critical features are that it is resource constrained and dynamically configured, with resource constraints and demands dynamically shaping the network topology. The operating principles are embodied in a cognitive neuroarchitecture, 4CAPS, consisting of a number of interacting computational centers that correspond to activating cortical areas. Each 4CAPS center is a hybrid production system, possessing both symbolic and connectionist attributes. We describe 4CAPS models of sentence comprehension, spatial problem solving, and complex multitasking and compare the accounts of these models with brain activation and behavioral results. Finally, we compare 4CAPS with other proposed neuroarchitectures.

[Functional exploration of the brain by fMRI]

Functional magnetic resonance imaging (fMRI) permits to obtain physiological information about MRI signal, which is modulated by electrical, biochemical, and physiological properties of the cerebral tissue. It is possible to characterize the brain interactions from an fMRI signal. Particularly, the use of a spectral analysis at a given frequency allows access to the time series chronology, which occurs within various activated areas of the brain. Thus, spectral parameters such as coherency and phase shift may be calculated from presupposed stationary stochastic signals and of an estimate of the cross-spectral power density function. Coherency describes a correlation structure in frequency domain between signals and thus allows obtaining an accurate estimate of the phase relation (time delay), which connects the signals between them. We describe in the last part of the article a calculation method integrating spectral information obtained previously and which makes it possible to evaluate the intensity of the existing interaction between two distinct cerebral areas.

Biophysical model for integrating neuronal activity, EEG, fMRI and metabolism

Our goal is to model the coupling between neuronal activity, cerebral metabolic rates of glucose and oxygen consumption, cerebral blood flow (CBF), electroencephalography (EEG) and blood oxygenation level-dependent (BOLD) responses. In order to accomplish this, two previous models are coupled: a metabolic/hemodynamic model (MHM) for a voxel, linking BOLD signals and neuronal activity, and a neural mass model describing the neuronal dynamics within a voxel and its interactions with voxels of the same area (short-range interactions) and other areas (long-range interactions). For coupling both models, we take as the input to the BOLD model, the number of active synapses within the voxel, that is, the average number of synapses that will receive an action potential within the time unit. This is obtained by considering the action potentials transmitted between neuronal populations within the voxel, as well as those arriving from other voxels. Simulations are carried out for testing the integrated model. Results show that realistic evoked potentials (EP) at electrodes on the scalp surface and the corresponding BOLD signals for each voxel are produced by the model. In another simulation, the alpha rhythm was reproduced and reasonable similarities with experimental data were obtained when calculating correlations between BOLD signals and the alpha power curve. The origin of negative BOLD responses and the characteristics of EEG, PET and BOLD signals in Alzheimer's disease were also studied.

Modelling of syntactical processing in the cortex

Probably the hardest test for a theory of brain function is the explanation of language processing in the human brain, in particular the interplay of syntax and semantics. Clearly such an explanation can only be very speculative, because there are essentially no animal models and it is hard to study detailed neural processing in humans. The approach presented in this paper uses well established basic neural mechanisms in a plausible global network architecture that is formulated essentially in terms of cortical areas and their intracortical and corticocortical interconnections. The neural implementation of this system shows that the comparatively intricate logical task of understanding semantico-syntactical structures can be mastered by a neural network architecture. The system presented also shows additional context awareness, in particular the model is able to correct ambiguous input to a certain degree, e.g. the input "bot show/lift green wall" with an artificial ambiguity between "show" and "lift" is correctly interpreted as "bot show green wall" since a wall is not liftable. Furthermore, the system is able to learn new object words during runtime.

Unified structural equation modeling approach for the analysis of multisubject, multivariate functional MRI data

The ultimate goal of brain connectivity studies is to propose, test, modify, and compare certain directional brain pathways. Path analysis or structural equation modeling (SEM) is an ideal statistical method for such studies. In this work, we propose a two-stage unified SEM plus GLM (General Linear Model) approach for the analysis of multisubject, multivariate functional magnetic resonance imaging (fMRI) time series data with subject-level covariates. In Stage 1, we analyze the fMRI multivariate time series for each subject individually via a unified SEM model by combining longitudinal pathways represented by a multivariate autoregressive (MAR) model, and contemporaneous pathways represented by a conventional SEM. In Stage 2, the resulting subject-level path coefficients are merged with subject-level covariates such as gender, age, IQ, etc., to examine the impact of these covariates on effective connectivity via a GLM. Our approach is exemplified via the analysis of an fMRI visual attention experiment. Furthermore, the significant path network from the unified SEM analysis is compared to that from a conventional SEM analysis without incorporating the longitudinal information as well as that from a Dynamic Causal Modeling (DCM) approach.

Use of a neural mass model for the analysis of effective connectivity among cortical regions based on high resolution EEG recordings

Assessment of brain connectivity among different brain areas during cognitive or motor tasks is a crucial problem in neuroscience today. Aim of this work is to use a neural mass model to assess the effect of various connectivity patterns in cortical electroencephalogram (EEG) power spectral density, and investigate the possibility to derive connectivity circuits from EEG data. To this end, a model of an individual region of interest (ROI) has been built as the parallel arrangement of three populations, each described as in Wendling et al. (Eur J Neurosci 15:1499-1508, 2002). Connectivity among ROIs includes three parameters, which specify the strength of connection in the different frequency bands. The following main steps have been followed: (1) we analyzed how the power spectral density (PSD) is significantly modified by the kind of coupling hypothesized among the ROIs; (2) with the model, and using an automatic fitting procedure, we looked for a simple connectivity circuit able to reproduce PSD of cortical EEG in three ROIs during a finger-movement task. The estimated parameters represent the strength of connections among the ROIs in the different frequency bands. Cortical EEGs were computed with an inverse propagation algorithm, starting from measurement performed with 96 electrodes on the scalp. The present study suggests that the model can be used as a simulation tool, able to mimic the effect of connectivity on EEG. Moreover, it can be used to look for simple connectivity circuits, able to explain the main features of observed cortical PSD. These results may open new prospectives in the use of neurophysiological models, instead of empirical models, to assess effective connectivity from neuroimaging information.

Individual variability in brain activations associated with episodic retrieval: A role for large-scale databases

The localization of brain functions using neuroimaging techniques is commonly dependent on statistical analyses of groups of subjects in order to identify sites of activation, particularly in studies of episodic memory. Exclusive reliance on group analysis may be to the detriment of understanding the true underlying cognitive nature of brain activations. In this overview, we found that the patterns of brain activity associated with episodic retrieval are very distinct for individual subjects from the patterns of brain activity at the group level. These differences appear to go beyond the relatively small variations due to cyctoarchitectonic differences or spatial normalization. We review evidence that individual patterns of brain activity vary widely across subjects and are reliable over time despite extensive variability. We suggest that varied but reliable individual patterns of significant brain activity may be indicative of different cognitive strategies used to produce a recognition response. We argue that individual analyses in conjunction with group analyses are likely to be critical in fully understanding the relationship between retrieval processes and underlying neural systems.

Exploring large-scale brain networks in functional MRI

Increasing emphasis has been recently put on large-scale network processing of brain functions. To explore these networks, many approaches have been proposed in functional magnetic resonance imaging (fMRI). Their objective is to answer the following two questions: (1) what brain regions are involved in the functional process under investigation? and (2) how do these regions interact? We review some of the key concepts and corresponding methods to cope with both issues.

Language models based on Hebbian cell assemblies

This paper demonstrates how associative neural networks as standard models for Hebbian cell assemblies can be extended to implement language processes in large-scale brain simulations. To this end the classical auto- and hetero-associative paradigms of attractor nets and synfire chains (SFCs) are combined and complemented by conditioned associations as a third principle which allows for the implementation of complex graph-like transition structures between assemblies. We show example simulations of a multiple area network for object-naming, which categorises objects in a visual hierarchy and generates different specific syntactic motor sequences ("words") in response. The formation of cell assemblies due to ongoing plasticity in a multiple area network for word learning is studied afterwards. Simulations show how assemblies can form by means of percolating activity across auditory and motor-related language areas, a process supported by rhythmic, synchronized propagating waves through the network. Simulations further reproduce differences in own EEG&MEG experiments between responses to word- versus non-word stimuli in human subjects.

Arithmetic processing in the brain shaped by cultures

The universal use of Arabic numbers in mathematics raises a question whether these digits are processed the same way in people speaking various languages, such as Chinese and English, which reflect differences in Eastern and Western cultures. Using functional MRI, we demonstrated a differential cortical representation of numbers between native Chinese and English speakers. Contrasting to native English speakers, who largely employ a language process that relies on the left perisylvian cortices for mental calculation such as a simple addition task, native Chinese speakers, instead, engage a visuo-premotor association network for the same task. Whereas in both groups the inferior parietal cortex was activated by a task for numerical quantity comparison, functional MRI connectivity analyses revealed a functional distinction between Chinese and English groups among the brain networks involved in the task. Our results further indicate that the different biological encoding of numbers may be shaped by visual reading experience during language acquisition and other cultural factors such as mathematics learning strategies and education systems, which cannot be explained completely by the differences in languages per se.

fMRI MODELS OF DENDRITIC AND ASTROCYTIC NETWORKS

In order to elucidate the relationships between hierarchical structures within the neocortical neuropil and the information carried by an ensemble of neurons encompassing a single voxel, it is essential to predict through volume conductor modeling LFPs representing average extracellular potentials, which are expressed in terms of interstitial potentials of individual cells in networks of gap-junctionally connected astrocytes and synaptically connected neurons. These relationships have been provided and can then be used to investigate how the underlying neuronal population activity can be inferred from the measurement of the BOLD signal through electrovascular coupling mechanisms across the blood-brain barrier. The importance of both synaptic and extrasynaptic transmission as the basis of electrophysiological indices triggering vascular responses between dendritic and astrocytic networks, and sequential configurations of firing patterns in composite neural networks is emphasized. The purpose of this review is to show how fMRI data may be used to draw conclusions about the information transmitted by individual neurons in populations generating the BOLD signal.

Brain imaging tools in neurosciences

In this chapter brain imaging tools in neurosciences are presented. These include a brief overview on single-photon emission tomography (SPET) and a detailed focus on positron emission tomography (PET) and functional magnetic resonance imaging (fMRI). In addition, a critical discussion on the advantages and disadvantages of the three diagnostic systems is added. Furthermore, this article describes the image analysis tools from visual analysis over region-of-interest technique up to statistical parametric mapping, co-registration methods, and network analysis. It also compares the newly developed combined PET/CT scanner approach with established image fusion software approaches. There is rapid change: Better scanner qualities, new software packages and scanner concepts are on the road paved for an amply bright future in neurosciences.

Operational principles of neurocognitive networks

Large-scale neural networks are thought to be an essential substrate for the implementation of cognitive function by the brain. If so, then a thorough understanding of cognition is not possible without knowledge of how the large-scale neural networks of cognition (neurocognitive networks) operate. Of necessity, such understanding requires insight into structural, functional, and dynamical aspects of network operation, the intimate interweaving of which may be responsible for the intricacies of cognition. Knowledge of anatomical structure is basic to understanding how neurocognitive networks operate. Phylogenetically and ontogenetically determined patterns of synaptic connectivity form a structural network of brain areas, allowing communication between widely distributed collections of areas. The function of neurocognitive networks depends on selective activation of anatomically linked cortical and subcortical areas in a wide variety of configurations. Large-scale functional networks provide the cooperative processing which gives expression to cognitive function. The dynamics of neurocognitive network function relates to the evolving patterns of interacting brain areas that express cognitive function in real time. This article considers the proposition that a basic similarity of the structural, functional, and dynamical features of all neurocognitive networks in the brain causes them to function according to common operational principles. The formation of neural context through the coordinated mutual constraint of multiple interacting cortical areas, is considered as a guiding principle underlying all cognitive functions. Increasing knowledge of the operational principles of neurocognitive networks is likely to promote the advancement of cognitive theories, and to seed strategies for the enhancement of cognitive abilities.

Computational neuropharmacology: dynamical approaches in drug discovery

Computational approaches that adopt dynamical models are widely accepted in basic and clinical neuroscience research as indispensable tools with which to understand normal and pathological neuronal mechanisms. Although computer-aided techniques have been used in pharmaceutical research (e.g. in structure- and ligand-based drug design), the power of dynamical models has not yet been exploited in drug discovery. We suggest that dynamical system theory and computational neuroscience--integrated with well-established, conventional molecular and electrophysiological methods--offer a broad perspective in drug discovery and in the search for novel targets and strategies for the treatment of neurological and psychiatric diseases.

Large-scale neural models and dynamic causal modelling

Dynamic causal modelling (DCM) is a method for estimating and making inferences about the coupling among small numbers of brain areas, and the influence of experimental manipulations on that coupling [Friston, K.J., Harrison, L., Penny, W., 2003 Dynamic causal modelling. Neuroimage 19, 1273-1302]. Large-scale neural modelling aims to construct neurobiologically grounded computational models with emergent behaviours that inform our understanding of neuronal systems. One such model has been used to simulate region-specific BOLD time-series [Horwitz, B., Friston, K.J., Taylor, J.G., 2000. Neural modeling and functional brain imaging: an overview. Neural Netw. 13, 829-846]. DCM was used to make inferences about effective connectivity using data generated by a model implementing a visual delayed match-to-sample task [Tagamets, M.A., Horwitz, B., 1998. Integrating electrophysiological and anatomical experimental data to create a large-scale model that simulates a delayed match-to-sample human brain imaging study. Cereb. Cortex 8, 310-320]. The aim was to explore the validity of inferences made using DCM about the connectivity structure and task-dependent modulatory effects, in a system with a known connectivity structure. We also examined the effects of misspecifying regions of interest. Models with hierarchical connectivity and reciprocal connections were examined using DCM and Bayesian Model Comparison [Penny, W.D., Stephan, K.E., Mechelli, A., Friston, K.J., 2004. Comparing dynamic causal models. Neuroimage 22, 1157-1172]. This approach revealed strong evidence for those models with correctly specified anatomical connectivity. Furthermore, Bayesian model comparison favoured those models when bilinear effects corresponded to their implementation in the neural model. These findings generalised to an extended model with two additional areas and reentrant circuits. The conditional uncertainty of coupling parameter estimates increased in proportion to the number of incorrectly specified regions. These results highlight the role of neural models in establishing the validity of estimation and inference schemes. Specifically, Bayesian model comparison confirms the validity of DCM in relation to a well-characterised and comprehensive neuronal model.