Relating fMRI and PET signals to neural activity by means of large-scale neural models

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

Invasive electrophysiological and neuroanatomical studies in nonhuman mammalian experimental preparations have helped elucidate the lamina (layer) dependence of neural computations and interregional connections. Noninvasive functional neuroimaging can, in principle, resolve cortical laminae (layers), and thus provide insight into human neural computations and interregional connections. However human neuroimaging data are noisy and difficult to interpret; biologically realistic simulations can aid experimental interpretation by relating the neuroimaging data to simulated neural activity. We illustrate the potential of laminar neuroimaging by upgrading an existing large-scale, multiregion neural model that simulates a visual delayed match-to-sample (DMS) task. The new laminar-based neural unit incorporates spiny stellate, pyramidal, and inhibitory neural populations which are divided among supragranular, granular, and infragranular laminae (layers). We simulated neural activity which is translated into local field potential-like data used to simulate conventional and laminar fMRI activity. We implemented the laminar connectivity schemes proposed by Felleman and Van Essen (Cerebral Cortex, 1991) for interregional connections. The hemodynamic model that we employ is a modified version of one due to Heinzle et al. (Neuroimage, 2016) that incorporates the effects of draining veins. We show that the laminar version of the model replicates the findings of the existing model. The laminar model shows the finer structure in fMRI activity and functional connectivity. Laminar differences in the magnitude of neural activities are a prominent finding; these are also visible in the simulated fMRI. We illustrate differences between task and control conditions in the fMRI signal, and demonstrate differences in interregional laminar functional connectivity that reflect the underlying connectivity scheme. These results indicate that multi-layer computational models can aid in interpreting layer-specific fMRI, and suggest that increased use of laminar fMRI could provide unique and fundamental insights to human neuroscience.

Incomplete evidence that increasing current intensity of tDCS boosts outcomes

BACKGROUND

Transcranial direct current stimulation (tDCS) is investigated to modulate neuronal function by applying a fixed low-intensity direct current to scalp.

OBJECTIVES

We critically discuss evidence for a monotonic response in effect size with increasing current intensity, with a specific focus on a question if increasing applied current enhance the efficacy of tDCS.

METHODS

We analyzed tDCS intensity does-response from different perspectives including biophysical modeling, animal modeling, human neurophysiology, neuroimaging and behavioral/clinical measures. Further, we discuss approaches to design dose-response trials.

RESULTS

Physical models predict electric field in the brain increases with applied tDCS intensity. Data from animal studies are lacking since a range of relevant low-intensities is rarely tested. Results from imaging studies are ambiguous while human neurophysiology, including using transcranial magnetic stimulation (TMS) as a probe, suggests a complex state-dependent non-monotonic dose response. The diffusivity of brain current flow produced by conventional tDCS montages complicates this analysis, with relatively few studies on focal High Definition (HD)-tDCS. In behavioral and clinical trials, only a limited range of intensities (1-2 mA), and typically just one intensity, are conventionally tested; moreover, outcomes are subject brain-state dependent. Measurements and models of current flow show that for the same applied current, substantial differences in brain current occur across individuals. Trials are thus subject to inter-individual differences that complicate consideration of population-level dose response.

CONCLUSION

The presence or absence of simple dose response does not impact how efficacious a given tDCS dose is for a given indication. Understanding dose-response in human applications of tDCS is needed for protocol optimization including individualized dose to reduce outcome variability, which requires intelligent design of dose-response studies.

Multimodal analysis of cortical chemoarchitecture and macroscale fMRI resting-state functional connectivity

The cerebral cortex is well known to display a large variation in excitatory and inhibitory chemoarchitecture, but the effect of this variation on global scale functional neural communication and synchronization patterns remains less well understood. Here, we provide evidence of the chemoarchitecture of cortical regions to be associated with large-scale region-to-region resting-state functional connectivity. We assessed the excitatory versus inhibitory chemoarchitecture of cortical areas as an ExIn ratio between receptor density mappings of excitatory (AMPA, M1 ) and inhibitory (GABAA , M2 ) receptors, computed on the basis of data collated from pioneering studies of autoradiography mappings as present in literature of the human (2 datasets) and macaque (1 dataset) cortex. Cortical variation in ExIn ratio significantly correlated with total level of functional connectivity as derived from resting-state functional connectivity recordings of cortical areas across all three datasets (human I: P = 0.0004; human II: P = 0.0008; macaque: P = 0.0007), suggesting cortical areas with an overall more excitatory character to show higher levels of intrinsic functional connectivity during resting-state. Our findings are indicative of the microscale chemoarchitecture of cortical regions to be related to resting-state fMRI connectivity patterns at the global system's level of connectome organization. Hum Brain Mapp 37:3103-3113, 2016. © 2016 Wiley Periodicals, Inc.

Vascular Dynamics Aid a Coupled Neurovascular Network Learn Sparse Independent Features: A Computational Model

Cerebral vascular dynamics are generally thought to be controlled by neural activity in a unidirectional fashion. However, both computational modeling and experimental evidence point to the feedback effects of vascular dynamics on neural activity. Vascular feedback in the form of glucose and oxygen controls neuronal ATP, either directly or via the agency of astrocytes, which in turn modulates neural firing. Recently, a detailed model of the neuron-astrocyte-vessel system has shown how vasomotion can modulate neural firing. Similarly, arguing from known cerebrovascular physiology, an approach known as “hemoneural hypothesis” postulates functional modulation of neural activity by vascular feedback. To instantiate this perspective, we present a computational model in which a network of “vascular units” supplies energy to a neural network. The complex dynamics of the vascular network, modeled by a network of oscillators, turns neurons ON and OFF randomly. The informational consequence of such dynamics is explored in the context of an auto-encoder network. In the proposed model, each vascular unit supplies energy to a subset of hidden neurons of an autoencoder network, which constitutes its “projective field.” Neurons that receive adequate energy in a given trial have reduced threshold, and thus are prone to fire. Dynamics of the vascular network are governed by changes in the reconstruction error of the auto-encoder network, interpreted as the neuronal demand. Vascular feedback causes random inactivation of a subset of hidden neurons in every trial. We observe that, under conditions of desynchronized vascular dynamics, the output reconstruction error is low and the feature vectors learnt are sparse and independent. Our earlier modeling study highlighted the link between desynchronized vascular dynamics and efficient energy delivery in skeletal muscle. We now show that desynchronized vascular dynamics leads to efficient training in an auto-encoder neural network.

Targeted electrode-based modulation of neural circuits for depression

During the last 20 years of neuroscience research, we have witnessed a fundamental shift in the conceptualization of psychiatric disorders, with the dominant psychological and neurochemical theories of the past now complemented by a growing emphasis on developmental, genetic, molecular, and brain circuit models. Facilitating this evolving paradigm shift has been the growing contribution of functional neuroimaging, which provides a versatile platform to characterize brain circuit dysfunction underlying specific syndromes as well as changes associated with their successful treatment. Discussed here are converging imaging findings that established a rationale for testing a targeted neuromodulation strategy, deep brain stimulation, for treatment-resistant major depression.

Large-scale neural model validation of partial correlation analysis for effective connectivity investigation in functional MRI

Recent studies of functional connectivity based upon blood oxygen level dependent functional magnetic resonance imaging have shown that this technique allows one to investigate large-scale functional brain networks. In a previous study, we advocated that data-driven measures of effective connectivity should be developed to bridge the gap between functional and effective connectivity. To attain this goal, we proposed a novel approach based on the partial correlation matrix. In this study, we further validate the use of partial correlation analysis by employing a large-scale, neurobiologically realistic neural network model to generate simulated data that we analyze with both structural equation modeling (SEM) and the partial correlation approach. Unlike real experimental data, where the interregional anatomical links are not necessarily known, the links between the nodes of the network model are fully specified, and thus provide a standard against which to judge the results of SEM and partial correlation analyses. Our results show that partial correlation analysis from the data alone exhibits patterns of effective connectivity that are similar to those found using SEM, and both are in agreement with respect to the underlying neuroarchitecture. Our findings thus provide a strong validation for the partial correlation method.

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.

Contribution of exploratory methods to the investigation of extended large-scale brain networks in functional MRI: methodologies, results, and challenges

A large-scale brain network can be defined as a set of segregated and integrated regions, that is, distant regions that share strong anatomical connections and functional interactions. Data-driven investigation of such networks has recently received a great deal of attention in blood-oxygen-level-dependent (BOLD) functional magnetic resonance imaging (fMRI). We here review the rationale for such an investigation, the methods used, the results obtained, and also discuss some issues that have to be faced for an efficient exploration.

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.

Aging is associated with contrasting changes in local and distant cortical connectivity in the human motor system

Pathophysiological changes in neurological and neuropsychiatric diseases are increasingly described in terms of abnormal network connectivity. However, the anatomical integrity and efficacy of connections among multiple brain regions change with aging, even in healthy adults. We combined low-frequency transcranial magnetic stimulation and positron emission tomography to study the age-related changes in regional activation and effective connectivity, associated with voluntary action by healthy adults between 22 and 68 years old. Contrasting effects of aging on the motor network were seen using analyses of regional activation, effective connectivity mediating task-related neuronal activation and effective connectivity in response to transcranial magnetic stimulation. Low-frequency rTMS reduced cerebral blood flow during both movement and resting conditions, at the site of stimulation and neighboring frontal cortex. Aging was associated with increased movement-related activation in premotor cortex, bilaterally. Increasing age also increased the susceptibility of the cortex to the inhibitory effects of rTMS, at the site of stimulation and its contralateral homologue. Moreover, older subjects showed enhanced local effective connectivity, centered on the left premotor cortex, but reduced effective connectivity between distant motor-related cortical areas. We discuss these results in relation to the HAROLD model of aging and propose that there are differential effects of aging on local and distributed neuronal subpopulations in the motor network. This differential effect of aging has important implications for the study of neurodegenerative and cerebrovascular diseases that primarily affect older people, as well as our understanding of the normal aging process.

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.

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.

The human connectome: A structural description of the human brain

The connection matrix of the human brain (the human "connectome") represents an indispensable foundation for basic and applied neurobiological research. However, the network of anatomical connections linking the neuronal elements of the human brain is still largely unknown. While some databases or collations of large-scale anatomical connection patterns exist for other mammalian species, there is currently no connection matrix of the human brain, nor is there a coordinated research effort to collect, archive, and disseminate this important information. We propose a research strategy to achieve this goal, and discuss its potential impact.

Investigating the neural basis for functional and effective connectivity. Application to fMRI

Viewing cognitive functions as mediated by networks has begun to play a central role in interpreting neuroscientific data, and studies evaluating interregional functional and effective connectivity have become staples of the neuroimaging literature. The neurobiological substrates of functional and effective connectivity are, however, uncertain. We have constructed neurobiologically realistic models for visual and auditory object processing with multiple interconnected brain regions that perform delayed match-to-sample (DMS) tasks. We used these models to investigate how neurobiological parameters affect the interregional functional connectivity between functional magnetic resonance imaging (fMRI) time-series. Variability is included in the models as subject-to-subject differences in the strengths of anatomical connections, scan-to-scan changes in the level of attention, and trial-to-trial interactions with non-specific neurons processing noise stimuli. We find that time-series correlations between integrated synaptic activities between the anterior temporal and the prefrontal cortex were larger during the DMS task than during a control task. These results were less clear when the integrated synaptic activity was haemodynamically convolved to generate simulated fMRI activity. As the strength of the model anatomical connectivity between temporal and frontal cortex was weakened, so too was the strength of the corresponding functional connectivity. These results provide a partial validation for using fMRI functional connectivity to assess brain interregional relations.