Revisiting 'brain modes' in a new computational era: approaches for the characterization of brain-behavioural associations.

The study of brain-function relationships is undergoing a conceptual and methodological transformation due to the emergence of network neuroscience and the development of multivariate methods for lesion-deficit inferences. Anticipating this process, in 1998 Godefroy and co-workers conceptualized the potential of four elementary typologies of brain-behaviour relationships named 'brain modes' (unicity, equivalence, association, summation) as building blocks able to describe the association between intact or lesioned brain regions and cognitive processes or neurological deficits. In the light of new multivariate lesion inference and network approaches, we critically revisit and update the original theoretical notion of brain modes, and provide real-life clinical examples that support their existence. To improve the characterization of elementary units of brain-behavioural relationships further, we extend such conceptualization with a fifth brain mode (mutual inhibition/masking summation). We critically assess the ability of these five brain modes to account for any type of brain-function relationship, and discuss past versus future contributions in redefining the anatomical basis of human cognition. We also address the potential of brain modes for predicting the behavioural consequences of lesions and their future role in the design of cognitive neurorehabilitation therapies.

Game theoretical mapping of white matter contributions to visuospatial attention in stroke patients with hemineglect.

White matter bundles linking gray matter nodes are key anatomical players to fully characterize associations between brain systems and cognitive functions. Here we used a multivariate lesion inference approach grounded in coalitional game theory (multiperturbation Shapley value analysis, MSA) to infer causal contributions of white matter bundles to visuospatial orienting of attention. Our work is based on the characterization of the lesion patterns of 25 right hemisphere stroke patients and the causal analysis of their impact on three neuropsychological tasks: line bisection, letter cancellation, and bells cancellation. We report that, out of the 11 white matter bundles included in our MSA coalitions, the optic radiations, the inferior fronto-occipital fasciculus and the anterior cingulum were the only tracts to display task-invariant contributions (positive, positive, and negative, respectively) to the tasks. We also report task-dependent influences for the branches of the superior longitudinal fasciculus and the posterior cingulum. By extending prior findings to white matter tracts linking key gray matter nodes, we further characterize from a network perspective the anatomical basis of visual and attentional orienting processes. The knowledge about interactions patterns mediated by white matter tracts linking cortical nodes of attention orienting networks, consolidated by further studies, may help develop and customize brain stimulation approaches for the rehabilitation of visuospatial neglect.

Game theoretical mapping of causal interactions underlying visuo-spatial attention in the human brain based on stroke lesions.

Anatomical studies conducted in neurological conditions have developed our understanding of the causal relationships between brain lesions and their clinical consequences. The analysis of lesion patterns extended across brain networks has been particularly useful in offering new insights on brain-behavior relationships. Here we applied multiperturbation Shapley value Analysis (MSA), a multivariate method based on coalitional game theory inferring causal regional contributions to specific behavioral outcomes from the characteristic functional deficits after stroke lesions. We established the causal patterns of contributions and interactions of nodes of the attentional orienting network on the basis of lesion and behavioral data from 25 right hemisphere stroke patients tested in visuo-spatial attention tasks. We calculated the percentage of damaged voxels for five right hemisphere cortical regions contributing to attentional orienting, involving seven specific Brodmann Areas (BA): Frontal Eye Fields, (FEF-BA6), Intraparietal Sulcus (IPS-BA7), Inferior Frontal Gyrus (IFG-BA44/BA45), Temporo-Parietal Junction (TPJ-BA39/BA40), and Inferior Occipital Gyrus (IOG-BA19). We computed the MSA contributions of these seven BAs to three behavioral clinical tests (line bisection, bells cancellation, and letter cancelation). Our analyses indicated IPS as the main contributor to the attentional orienting and also revealed synergistic influences among IPS, TPJ, and IOG (for bells cancellation and line bisection) and between TPJ and IFG (for bells and letter cancellation tasks). The findings demonstrate the ability of the MSA approach to infer plausible causal contributions of relevant right hemisphere sites in poststroke visuo-spatial attention and awareness disorders. Hum Brain Mapp 38:3454-3471, 2017. © 2017 Wiley Periodicals, Inc.

Technical considerations of a game-theoretical approach for lesion symptom mapping.

BACKGROUND: Various strategies have been used for inferring brain functions from stroke lesions. We explored a new mathematical approach based on game theory, the so-called multi-perturbation Shapley value analysis (MSA), to assess causal function localizations and interactions from multiple perturbation data. We applied MSA to a dataset composed of lesion patterns of 148 acute stroke patients and their National Institutes of Health Stroke Scale (NIHSS) scores, to systematically investigate the influence of different parameter settings on the outcomes of the approach. Specifically, we investigated aspects of MSA methodology including the choice of the predictor algorithm (typology and kernel functions), training dataset (original versus binary), as well as the influence of lesion thresholds. We assessed the suitability of MSA for processing real clinical lesion data and established the central parameters for this analysis. RESULTS: We derived general recommendations for the analysis of clinical datasets by MSA and showed that, for the studied dataset, the best approach was to use a linear-kernel support vector machine predictor, trained with a binary training dataset, where the binarization was implemented through a median threshold of lesion size for each region. We demonstrated that the results obtained with different MSA variants lead to almost identical results as the basic MSA. CONCLUSIONS: MSA is a feasible approach for the multivariate lesion analysis of clinical stroke data. Informed choices need to be made to set parameters that may affect the analysis outcome.

Influence of stroke infarct location on functional outcome measured by the modified rankin scale.

BACKGROUND AND PURPOSE: In the early days after ischemic stroke, information on structural brain damage from MRI supports prognosis of functional outcome. It is rated widely by the modified Rankin Scale that correlates only moderately with lesion volume. We therefore aimed to elucidate the influence of lesion location from early MRI (days 2-3) on functional outcome after 1 month using voxel-based lesion symptom mapping. METHODS: We analyzed clinical and MRI data of patients from a prospective European multicenter stroke imaging study (I-KNOW). Lesions were delineated on fluid-attenuated inversion recovery images on days 2 to 3 after stroke onset. We generated statistic maps of lesion contribution related to clinical outcome (modified Rankin Scale) after 1 month using voxel-based lesion symptom mapping. RESULTS: Lesion maps of 101 patients with middle cerebral artery infarctions were included for analysis (right-sided stroke, 47%). Mean age was 67 years, median admission National Institutes of Health Stroke Scale was 11. Mean infarct volumes were comparable between both sides (left, 37.5 mL; right, 43.7 mL). Voxel-based lesion symptom mapping revealed areas with high influence on higher modified Rankin Scale in regions involving the corona radiata, internal capsule, and insula. In addition, asymmetrically distributed impact patterns were found involving the right inferior temporal gyrus and left superior temporal gyrus. CONCLUSIONS: In this group of patients with stroke, characteristic lesion patterns in areas of motor control and areas involved in lateralized brain functions on early MRI were found to influence functional outcome. Our data provide a novel map of the impact of lesion localization on functional stroke outcome as measured by the modified Rankin Scale.

Robust Independent Component Analysis based EMG decomposition - a comparison study.

High density surface Electromyography (HD-sEMG) provides a high fidelity measurement of the myoelectric activity that can be leveraged by EMG decomposition methods to estimate the motor neuron discharges. Independent Component Analysis (ICA) methods are used as basis for many EMG decomposition algorithms, for the estimation of motor unit action potential signals. Accurate source separation is a non-trivial task in EMG decomposition. While FastICA is widely used for this purpose, other methods with attractive characteristics, such as RobustICA, remain relatively unexplored. The purpose of the current work is to compare three different ICA-based EMG decomposition methods (FastICA, RobustICA and RobustICALCH) in terms of decomposition accuracy and computation time. The evaluation was performed on simulated data using a decomposition algorithm inspired by previous studies. Our results demonstrate that RobustICA outperforms the other methods in terms of number of correctly identified motor units, high decomposition accuracy, and low computation time, across different muscle contraction levels.

Error-related Potentials in a Virtual Pick-and-Place Experiment: Toward Real-world Shared-control.

In Human-Robot Collaboration setting a robot may be controlled by a user directly or through a Brain-Computer Interface that detects user intention, and it may act as an autonomous agent. As such interaction increases in complexity, conflicts become inevitable. Goal conflicts can arise from different sources, for instance, interface mistakes - related to misinterpretation of human's intention - or errors of the autonomous system to address task and human's expectations. Such conflicts evoke different spontaneous responses in the human's brain, which could be used to regulate intrinsic task parameters and to improve system response to errors - leading to improved transparency, performance, and safety. To study the possibility of detecting interface and agent errors, we designed a virtual pick and place task with sequential human and robot responsibility and recorded the electroencephalography (EEG) activity of six participants. In the virtual environment, the robot received a command from the participants through a computer keyboard or it moved as autonomous agent. In both cases, artificial errors were defined to occur in 20% - 25% of the trials. We found differences in the responses to interface and agent errors. From the EEG data, correct trials, interface errors, and agent errors were truly predicted for 51.62% ± 9.99% (chance level 38.21%) of the pick movements and 46.84%±6.62% (chance level 36.99%) for the place movements in a pseudo-asynchronous fashion. Our study suggests that in a human-robot collaboration setting one may improve the future performance of a system with intention detection and autonomous modes. Specific examples could be Neural Interfaces that replace and restore motor functions.

The generation of rhythms within a cortical region: analysis of a neural mass model.

Rhythms in brain electrical activity are assumed to play a significant role in many cognitive and perceptual processes. It is thus of great value to analyze these rhythms and their mutual relationships in large scale models of cortical regions. In the present work, we modified the neural mass model by Wendling et al. (Eur. J. Neurosci. 15 (2002) 1499-1508) by including a new inhibitory self-loop among GABAA,fast interneurons. A theoretical analysis was performed to demonstrate that, thanks to this loop, GABAA,fast interneurons can produce a gamma rhythm in the power spectral density (PSD) even without the participation of the other neural populations. Then, the model of a whole cortical region, built upon four interconnected neural populations (pyramidal cells, excitatory, GABAA,slow and GABAA,fast interneurons) was investigated by changing the internal connectivity parameters. Results show that different rhythm combinations (beta and gamma, alpha and gamma, or a wide spectrum) can be obtained within the same region by simply altering connectivity values, without the need to change synaptic kinetics. Finally, two or three cortical regions were connected by using different topologies of long range connections. Results show that long-range connections directed from pyramidal neurons to GABAA,fast interneurons are the most efficient to transmit rhythms from one region to another. In this way, PSD with three or four peaks can be obtained using simple connectivity patterns. The model can be of value to gain a deeper insight into the mechanisms involved in the generation of gamma rhythms and provide a better understanding of cortical EEG spectra.

Visuotactile representation of peripersonal space: a neural network study.

Neurophysiological and behavioral studies suggest that the peripersonal space is represented in a multisensory fashion by integrating stimuli of different modalities. We developed a neural network to simulate the visual-tactile representation of the peripersonal space around the right and left hands. The model is composed of two networks (one per hemisphere), each with three areas of neurons: two are unimodal (visual and tactile) and communicate by synaptic connections with a third downstream multimodal (visual-tactile) area. The hemispheres are interconnected by inhibitory synapses. We applied a combination of analytic and computer simulation techniques. The analytic approach requires some simplifying assumptions and approximations (linearization and a reduced number of neurons) and is used to investigate network stability as a function of parameter values, providing some emergent properties. These are then tested and extended by computer simulations of a more complex nonlinear network that does not rely on the previous simplifications. With basal parameter values, the extended network reproduces several in vivo phenomena: multisensory coding of peripersonal space, reinforcement of unisensory perception by multimodal stimulation, and coexistence of simultaneous right- and left-hand representations in bilateral stimulation. By reducing the strength of the synapses from the right tactile neurons, the network is able to mimic the responses characteristic of right-brain-damaged patients with left tactile extinction: perception of unilateral left tactile stimulation, cross-modal extinction and cross-modal facilitation in bilateral stimulation. Finally, a variety of sensitivity analyses on some key parameters was performed to shed light on the contribution of single-model components in network behaviour. The model may help us understand the neural circuitry underlying peripersonal space representation and identify its alterations explaining neurological deficits. In perspective, it could help in interpreting results of psychophysical and behavioral trials and clarifying the neural correlates of multisensory-based rehabilitation procedures.

Discrimination of the hierarchical structure of cortical layers in 2-photon microscopy data by combined unsupervised and supervised machine learning.

The laminar organization of the cerebral cortex is a fundamental characteristic of the brain, with essential implications for cortical function. Due to the rapidly growing amount of high-resolution brain imaging data, a great demand arises for automated and flexible methods for discriminating the laminar texture of the cortex. Here, we propose a combined approach of unsupervised and supervised machine learning to discriminate the hierarchical cortical laminar organization in high-resolution 2-photon microscopic neural image data of mouse brain without observer bias, that is, without the prerequisite of manually labeled training data. For local cortical foci, we modify an unsupervised clustering approach to identify and represent the laminar cortical structure. Subsequently, supervised machine learning is applied to transfer the resulting layer labels across different locations and image data, to ensure the existence of a consistent layer label system. By using neurobiologically meaningful features, the discrimination results are shown to be consistent with the layer classification of the classical Brodmann scheme, and provide additional insight into the structure of the cerebral cortex and its hierarchical organization. Thus, our work paves a new way for studying the anatomical organization of the cerebral cortex, and potentially its functional organization.

The effect of connectivity on EEG rhythms, power spectral density and coherence among coupled neural populations: analysis with a neural mass model.

In the present work, a neural mass model consisting of four interconnected neural groups (pyramidal neurons, excitatory interneurons, inhibitory interneurons with slow synaptic kinetics, and inhibitory interneurons with fast synaptic kinetics) is used to investigate the mechanisms which cause the appearance of multiple rhythms in EEG spectra, and to assess how these rhythms can be affected by connectivity among different populations. In particular, we analyze a circuit, composed of three interconnected populations, each with a different synaptic kinetics (hence, with a different intrinsic rhythm). Results demonstrate that a single population can exhibit many different simultaneous rhythms, provided that some of these come from external sources (for instance, from remote regions). Analysis of coherence, and of the position of peaks in power spectral density, reveals important information on the possible connections among populations, especially useful to follow temporal changes in connectivity. Subsequently, the model is validated by comparing the power spectral density simulated in one population with that computed in the controlateral cingulated cortex (a region involved in motion preparation) during a right foot movement task in four normal subjects. The model is able to simulate real spectra quite well with only moderate parameter changes within the subject. In perspective, the results may be of value for a deeper comprehension of mechanism causing EEGs rhythms, for the study of brain connectivity and for the test of neurophysiological hypotheses.

Modeling analysis of the relationship between EEG rhythms and connectivity among different neural populations.

In our research, a neural mass model consisting of four interconnected neural groups (pyramidal neurons, excitatory interneurons, inhibitory interneurons with slow synaptic kinetics, and inhibitory interneurons with fast synaptic kinetics) is used to investigate the mechanisms which cause the appearance of multiple rhythms in EEG spectra, and to assess how these rhythms can be affected by connectivity among different populations. First, we showed that a single neural population, stimulated with white noise, can oscillate with its intrinsic rhythm, and that the position of this rhythm can be finely tuned (in the alpha, beta or gamma frequency ranges), acting on the population synaptic kinetics. Subsequently, we analyzed more complex circuits, composed of two or three interconnected populations, each with a different synaptic kinetics between neural groups within a population (hence, with a different intrinsic rhythm). The results demonstrates apex that a single population can exhibit many different simultaneous rhythms, provided that some of these come from external sources (for instance, from remote regions). The analysis of coherence, and of the position of the peaks in power spectral density, reveals important information on the possible connections among populations, and is especially useful to follow temporal changes in connectivity. In perspective, the results may be of value for a deeper comprehension of the mechanisms causing EEGs rhythms, for the study of connectivity among different neural populations and for the test of neurophysiological hypotheses.

A neural mass model for the simulation of cortical activity estimated from high resolution EEG during cognitive or motor tasks.

Neural mass models have been used for many years to study the macroscopic dynamics of neural populations in a simple and computationally inexpensive way. In this paper, we modified a model proposed by Wendling et al. [Wendling F, Bartolomei F, Bellanger JJ, Chauvel P. Epileptic fast activity can be explained by a model of impaired GABAergic dendritic inhibition. Eur J Neurosci 2002;15:1499-508] to simulate EEG power spectral density (PSD) in some regions of interest (ROIs) during simple tasks (finger movement or working memory tests). The work consists of two subsequent stages: (1) in the first we evaluated the role of some model parameters (i.e., average gain of synapses and their time constants) in affecting power spectral density. This analysis confirmed the possibility to simulate various EEG rhythms (in the alpha, beta and gamma frequency ranges) by modifying just the time constants of the synapses. The position of the individual rhythms (i.e., the corresponding peaks in the PSD) can be finely tuned acting on the average gain of fast inhibitory synapses. This analysis suggested that a single neural mass model produces a unimodal spectrum, which can be finely adjusted, but cannot mimic the overall complexity of EEG in an entire cortical area. (2) Hence, in the second stage we built a model of a ROI by combining three neural mass models arranged in parallel. With this model, and using an automatic fitting procedure, we carefully reproduced the PSD of cortical EEG in several ROIs during finger movement, and their temporal changes during a working memory task, by estimating nine parameters. The estimated parameters represent the excitation of each population (mean value and variance of exogenous input noise) and the average gain of fast inhibitory synapses. Cortical EEGs were computed with an inverse propagation algorithm, starting from measurement performed with a high number of electrodes on the scalp (46-96). Results show that the proposed model is able to mimic PSD of cortical activity acting on a few parameters, which represent external activation and short-time synaptic changes. This information may be exploited to reach a quantitative summary of electrical activity in ROIs during a task, and to derive information on connectivity, starting from non-invasive EEG measurements.

Assessing cortical functional connectivity by partial directed coherence: simulations and application to real data.

The aim of this paper is to test a technique called partial directed coherence (PDC) and its modification (squared PDC; sPDC) for the estimation of human cortical connectivity by means of simulation study, in which both PDC and sPDC were studied by analysis of variance. The statistical analysis performed returned that both PDC and sPDC are able to estimate correctly the imposed connectivity patterns when data exhibit a signal-to-noise ratio of at least 3 and a length of at least 27 s of nonconsecutive recordings at 250 Hz of sampling rate, equivalent, more generally, to 6750 data samples.

Causal functional contributions and interactions in the attention network of the brain: an objective multi-perturbation analysis.

Spatial attention is a prime example for the distributed network functions of the brain. Lesion studies in animal models have been used to investigate intact attentional mechanisms as well as perspectives for rehabilitation in the injured brain. Here, we systematically analyzed behavioral data from cooling deactivation and permanent lesion experiments in the cat, where unilateral deactivation of the posterior parietal cortex (in the vicinity of the posterior middle suprasylvian cortex, pMS) or the superior colliculus (SC) cause a severe neglect in the contralateral hemifield. Counterintuitively, additional deactivation of structures in the opposite hemisphere reverses the deficit. Using such lesion data, we employed a game-theoretical approach, multi-perturbation Shapley value analysis (MSA), for inferring functional contributions and network interactions of bilateral pMS and SC from behavioral performance in visual attention studies. The approach provides an objective theoretical strategy for lesion inferences and allows a unique quantitative characterization of regional functional contributions and interactions on the basis of multi-perturbations. The quantitative analysis demonstrated that right posterior parietal cortex and superior colliculus made the strongest positive contributions to left-field orienting, while left brain regions had negative contributions, implying that their perturbation may reverse the effects of contralateral lesions or improve normal function. An analysis of functional modulations and interactions among the regions revealed redundant interactions (implying functional overlap) between regions within each hemisphere, and synergistic interactions between bilateral regions. To assess the reliability of the MSA method in the face of variable and incomplete input data, we performed a sensitivity analysis, investigating how much the contribution values of the four regions depended on the performance of specific configurations and on the prediction of unknown performances. The results suggest that the MSA approach is sensitive to categorical, but insensitive to gradual changes in the input data. Finally, we created a basic network model that was based on the known anatomical interactions among cortical-tectal regions and reproduced the experimentally observed behavior in visual orienting. We discuss the structural organization of the network model relative to the causal modulations identified by MSA, to aid a mechanistic understanding of the attention network of the brain.

Mapping causal functional contributions derived from the clinical assessment of brain damage after stroke.

Lesion analysis reveals causal contributions of brain regions to mental functions, aiding the understanding of normal brain function as well as rehabilitation of brain-damaged patients. We applied a novel lesion inference technique based on game theory, Multi-perturbation Shapley value Analysis (MSA), to a large clinical lesion dataset. We used MSA to analyze the lesion patterns of 148 acute stroke patients together with their neurological deficits, as assessed by the National Institutes of Health Stroke Scale (NIHSS). The results revealed regional functional contributions to essential behavioral and cognitive functions as reflected in the NIHSS, particularly by subcortical structures. There were also side specific differences of functional contributions between the right and left hemispheric brain regions which may reflect the dominance of the left hemispheric syndrome aphasia in the NIHSS. Comparison of MSA to established lesion inference methods demonstrated the feasibility of the approach for analyzing clinical data and indicated its capability for objectively inferring functional contributions from multiple injured, potentially interacting sites, at the cost of having to predict the outcome of unknown lesion configurations. The analysis of regional functional contributions to neurological symptoms measured by the NIHSS contributes to the interpretation of this widely used standardized stroke scale in clinical practice as well as clinical trials and provides a first approximation of a 'map of stroke'.

Binding and segmentation via a neural mass model trained with Hebbian and anti-Hebbian mechanisms.

Synchronization of neural activity in the gamma band, modulated by a slower theta rhythm, is assumed to play a significant role in binding and segmentation of multiple objects. In the present work, a recent neural mass model of a single cortical column is used to analyze the synaptic mechanisms which can warrant synchronization and desynchronization of cortical columns, during an autoassociation memory task. The model considers two distinct layers communicating via feedforward connections. The first layer receives the external input and works as an autoassociative network in the theta band, to recover a previously memorized object from incomplete information. The second realizes segmentation of different objects in the gamma band. To this end, units within both layers are connected with synapses trained on the basis of previous experience to store objects. The main model assumptions are: (i) recovery of incomplete objects is realized by excitatory synapses from pyramidal to pyramidal neurons in the same object; (ii) binding in the gamma range is realized by excitatory synapses from pyramidal neurons to fast inhibitory interneurons in the same object. These synapses (both at points i and ii) have a few ms dynamics and are trained with a Hebbian mechanism. (iii) Segmentation is realized with faster AMPA synapses, with rise times smaller than 1 ms, trained with an anti-Hebbian mechanism. Results show that the model, with the previous assumptions, can correctly reconstruct and segment three simultaneous objects, starting from incomplete knowledge. Segmentation of more objects is possible but requires an increased ratio between the theta and gamma periods.

Changes in EEG power spectral density and cortical connectivity in healthy and tetraplegic patients during a motor imagery task.

Knowledge of brain connectivity is an important aspect of modern neuroscience, to understand how the brain realizes its functions. In this work, neural mass models including four groups of excitatory and inhibitory neurons are used to estimate the connectivity among three cortical regions of interests (ROIs) during a foot-movement task. Real data were obtained via high-resolution scalp EEGs on two populations: healthy volunteers and tetraplegic patients. A 3-shell Boundary Element Model of the head was used to estimate the cortical current density and to derive cortical EEGs in the three ROIs. The model assumes that each ROI can generate an intrinsic rhythm in the beta range, and receives rhythms in the alpha and gamma ranges from other two regions. Connectivity strengths among the ROIs were estimated by means of an original genetic algorithm that tries to minimize several cost functions of the difference between real and model power spectral densities. Results show that the stronger connections are those from the cingulate cortex to the primary and supplementary motor areas, thus emphasizing the pivotal role played by the CMA_L during the task. Tetraplegic patients exhibit higher connectivity strength on average, with significant statistical differences in some connections. The results are commented and virtues and limitations of the proposed method discussed.