Toward discovery science of human brain function.

Although it is being successfully implemented for exploration of the genome, discovery science has eluded the functional neuroimaging community. The core challenge remains the development of common paradigms for interrogating the myriad functional systems in the brain without the constraints of a priori hypotheses. Resting-state functional MRI (R-fMRI) constitutes a candidate approach capable of addressing this challenge. Imaging the brain during rest reveals large-amplitude spontaneous low-frequency (<0.1 Hz) fluctuations in the fMRI signal that are temporally correlated across functionally related areas. Referred to as functional connectivity, these correlations yield detailed maps of complex neural systems, collectively constituting an individual's "functional connectome." Reproducibility across datasets and individuals suggests the functional connectome has a common architecture, yet each individual's functional connectome exhibits unique features, with stable, meaningful interindividual differences in connectivity patterns and strengths. Comprehensive mapping of the functional connectome, and its subsequent exploitation to discern genetic influences and brain-behavior relationships, will require multicenter collaborative datasets. Here we initiate this endeavor by gathering R-fMRI data from 1,414 volunteers collected independently at 35 international centers. We demonstrate a universal architecture of positive and negative functional connections, as well as consistent loci of inter-individual variability. Age and sex emerged as significant determinants. These results demonstrate that independent R-fMRI datasets can be aggregated and shared. High-throughput R-fMRI can provide quantitative phenotypes for molecular genetic studies and biomarkers of developmental and pathological processes in the brain. To initiate discovery science of brain function, the 1000 Functional Connectomes Project dataset is freely accessible at www.nitrc.org/projects/fcon_1000/.

Cortical thickness is associated with gait disturbances in cerebral small vessel disease.

Although gait disturbances are present in a substantial portion of patients with cerebral small vessel disease (SVD), their pathogenesis has not been clarified as they are not entirely explained by the white matter lesions (WMLs) and lacunar infarcts. The role of cortical thickness in these patients remains largely unknown. We aimed to assess the regions of cortical thickness associated with distinct gait parameters in patients with SVD, and whether these associations were dependent on WMLs and lacunar infarcts. MRI data were obtained from 415 subjects with SVD, aged between 50 and 85 years. We assessed cortical thickness using surface-based cortical thickness analysis, and gait performance using the GAITRite system. Cortical thickness of predominantly the orbitofrontal and ventrolateral prefrontal cortex, the inferior parietal lobe, cingulate areas and visual association cortices was positively related to stride length. Thickness of the primary and supplementary motor cortices and the cingulate cortex was positively related to cadence, while thickness of the orbitofrontal and ventrolateral prefrontal cortex, anterior cingulate cortex and especially the inferior parietal lobe and superior temporal gyrus was negatively related to stride width. The associations with stride length and width were partially explained by the subcortical WMLs and lacunar infarcts. Cortical thickness may therefore be important in gait disturbances in individuals with SVD, with different cortical patterns for specific gait parameters. We suggest that cortical atrophy is part of the disease processes in patients with SVD.

Key role of coupling, delay, and noise in resting brain fluctuations.

A growing body of neuroimaging research has documented that, in the absence of an explicit task, the brain shows temporally coherent activity. This so-called "resting state" activity or, more explicitly, the default-mode network, has been associated with daydreaming, free association, stream of consciousness, or inner rehearsal in humans, but similar patterns have also been found under anesthesia and in monkeys. Spatiotemporal activity patterns in the default-mode network are both complex and consistent, which raises the question whether they are the expression of an interesting cognitive architecture or the consequence of intrinsic network constraints. In numerical simulation, we studied the dynamics of a simplified cortical network using 38 noise-driven (Wilson-Cowan) oscillators, which in isolation remain just below their oscillatory threshold. Time delay coupling based on lengths and strengths of primate corticocortical pathways leads to the emergence of 2 sets of 40-Hz oscillators. The sets showed synchronization that was anticorrelated at <0.1 Hz across the sets in line with a wide range of recent experimental observations. Systematic variation of conduction velocity, coupling strength, and noise level indicate a high sensitivity of emerging synchrony as well as simulated blood flow blood oxygen level-dependent (BOLD) on the underlying parameter values. Optimal sensitivity was observed around conduction velocities of 1-2 m/s, with very weak coupling between oscillators. An additional finding was that the optimal noise level had a characteristic scale, indicating the presence of stochastic resonance, which allows the network dynamics to respond with high sensitivity to changes in diffuse feedback activity.

Patterns of cortical degeneration in an elderly cohort with cerebral small vessel disease.

Emerging noninvasive neuroimaging techniques allow for the morphometric analysis of patterns of gray and white matter degeneration in vivo, which may help explain and predict the occurrence of cognitive impairment and Alzheimer's disease. A single center prospective follow-up study (Radboud University Nijmegen Diffusion tensor and Magnetic resonance imaging Cohort study (RUN DMC)) was performed involving 503 nondemented elderly individuals (50-85 years) with a history of symptomatic cerebral small vessel disease (SVD). Age was associated with a global reduction in cortical thickness, and this relationship was strongest for ventrolateral prefrontal cortex, auditory cortex, Wernicke's area, superior temporal lobe, and primary visual cortex. Right and left hemispheres differed in the thickness of language-related areas. White matter (WM) lesions were generally negatively correlated with cortical thickness, primarily in individuals over the age of 60, with the notable exception of Brodmann areas 4 and 5, which were positively correlated in age groups 50-60 and 60-70, respectively. The observed pattern of age-related decline may explain problems in memory and executive functions, which are already well documented in individuals with SVD. The additional gray matter loss affecting visual and auditory cortex, and specifically the head region of primary motor cortex, may indicate morphological correlates of impaired sensory and motor functions. The paradoxical positive relationship between WM lesion volume and cortical thickness in some areas may reflect early compensatory hypertrophy. This study raises a further interest in the mechanisms underlying cerebral gray and white matter degeneration in association with SVD, which will require further investigation with diffusion weighted and longitudinal MR studies.

A proposal for a coordinated effort for the determination of brainwide neuroanatomical connectivity in model organisms at a mesoscopic scale.

In this era of complete genomes, our knowledge of neuroanatomical circuitry remains surprisingly sparse. Such knowledge is critical, however, for both basic and clinical research into brain function. Here we advocate for a concerted effort to fill this gap, through systematic, experimental mapping of neural circuits at a mesoscopic scale of resolution suitable for comprehensive, brainwide coverage, using injections of tracers or viral vectors. We detail the scientific and medical rationale and briefly review existing knowledge and experimental techniques. We define a set of desiderata, including brainwide coverage; validated and extensible experimental techniques suitable for standardization and automation; centralized, open-access data repository; compatibility with existing resources; and tractability with current informatics technology. We discuss a hypothetical but tractable plan for mouse, additional efforts for the macaque, and technique development for human. We estimate that the mouse connectivity project could be completed within five years with a comparatively modest budget.

Connecting mean field models of neural activity to EEG and fMRI data.

Progress in functional neuroimaging of the brain increasingly relies on the integration of data from complementary imaging modalities in order to improve spatiotemporal resolution and interpretability. However, the usefulness of merely statistical combinations is limited, since neural signal sources differ between modalities and are related non-trivially. We demonstrate here that a mean field model of brain activity can simultaneously predict EEG and fMRI BOLD with proper signal generation and expression. Simulations are shown using a realistic head model based on structural MRI, which includes both dense short-range background connectivity and long-range specific connectivity between brain regions. The distribution of modeled neural masses is comparable to the spatial resolution of fMRI BOLD, and the temporal resolution of the modeled dynamics, importantly including activity conduction, matches the fastest known EEG phenomena. The creation of a cortical mean field model with anatomically sound geometry, extensive connectivity, and proper signal expression is an important first step towards the model-based integration of multimodal neuroimages.

Optimization of cortical hierarchies with continuous scales and ranges.

Although information flow in the neocortex has an apparent hierarchical organization, there is much ambiguity with respect to the definition of such a hierarchy, particularly in higher cortical regions. This ambiguity has been addressed by utilizing observable anatomical criteria, based upon tract tracing experiments, to constrain the definition of hierarchy [Felleman D.J. and van Essen D.C., 1991. Distributed hierarchical processing in the primate. Cereb. Cortex. 1(1), 1-47.]. There are, however, a high number of equally optimal hierarchies that fit these constraints [Hilgetag C.C., O'Neill M.A., Young M.P., 1996. Indeterminate organization of the visual system. Science. 271(5250), 776-777.]. Here, we propose a refined constraint set for optimization which utilizes continuous, rather than discrete, hierarchical levels, and permits a range of acceptable values rather than attempting to fit fixed hierarchical distances. Using linear programming to obtain hierarchies across a number of range sizes, we find a clear hierarchical pattern for both the original and refined versions of the Felleman and Van Essen [Felleman D.J. and van Essen D.C., 1991. Distributed hierarchical processing in the primate. Cereb. Cortex. 1(1), 1-47.] visual network. We also obtain an optimal hierarchy from a refined set of anatomical criteria which allows for the direct specification of hierarchical distance from the laminar distribution of labelled cells (Barone P., Batardiere A., Knoblauch K., Kennedy H., 2000. Laminar distribution of neurons in extrastriate areas projecting to visual areas V1 and V4 correlates with the hierarchical rank and indicates the operation of a distance rule. J. Neurosci. 20(9), 3263-3281.), and discuss the limitations and further possible refinements of such an approach.

Rhesus Macaque Brain Atlas Regions Aligned to an MRI Template.

To aid in the analysis of rhesus macaque brain images, we aligned digitized anatomical regions from the widely used atlas of Paxinos et al. to a published magnetic resonance imaging (MRI) template based on a large number of subjects. Digitally labelled atlas images were aligned to the template in 2D and then in 3D. The resulting grey matter regions appear qualitatively to be well registered to the template. To quantitatively validate the procedure, MR brain images of 20 rhesus macaques were aligned to the template along with regions drawn by hand in striatal and cortical areas in each subject's MRI. There was good geometric overlap between the hand drawn regions and the template regions. Positron emission tomography (PET) images of the same subjects showing uptake of a dopamine D2 receptor ligand were aligned to the template space, and good agreement was found between tracer binding measures calculated using the hand drawn and template regions. In conclusion, an anatomically defined set of rhesus macaque brain regions has been aligned to an MRI template and has been validated for analysis of PET imaging in a subset of striatal and cortical areas. The entire set of over 200 regions is publicly available at https://www.nitrc.org/ . Graphical Abstract ᅟ.

Deducing logical relationships between spatially registered cortical parcellations under conditions of uncertainty.

We propose a new technique, called Spatial Objective Relational Transformation (SORT), as an automated approach for derivation of logical relationships between cortical areas in different brain maps registered in the same Euclidean space. Recently, there have been large amounts of voxel-based three-dimensional structural and functional imaging data that provide us with coordinate-based information about the location of differently defined areas in the brain, whereas coordinate-independent, parcellation-based mapping is still commonly used in the majority of animal tracing and mapping studies. Because of the impact of voxel-based imaging methods and the need to attribute their features to coordinate-independent brain entities, this mapping becomes increasingly important. Our motivation here is not to make vague statements where more precise spatial statements would be better, but to find criteria for the identity (or other logical relationships) between areas that were delineated by different methods, in different individuals, or mapped to three-dimensional space using different deformation algorithms. The relevance of this problem becomes immediately obvious as one superimposes and compares different datasets in multimodal databases (e.g. CARET, http://brainmap.wustl.edu/caret), where voxel-based data are registered to surface nodes exploited by the procedure presented here. We describe the SORT algorithm and its implementation in the Java 2 programming language (http://java.sun.com/, which we make available for download. We give an example of practical use of our approach, and validate the SORT approach against a database of the coordinate-independent statements and inferences that have been deduced using alternative techniques.

Motifs in brain networks.

Complex brains have evolved a highly efficient network architecture whose structural connectivity is capable of generating a large repertoire of functional states. We detect characteristic network building blocks (structural and functional motifs) in neuroanatomical data sets and identify a small set of structural motifs that occur in significantly increased numbers. Our analysis suggests the hypothesis that brain networks maximize both the number and the diversity of functional motifs, while the repertoire of structural motifs remains small. Using functional motif number as a cost function in an optimization algorithm, we obtain network topologies that resemble real brain networks across a broad spectrum of structural measures, including small-world attributes. These results are consistent with the hypothesis that highly evolved neural architectures are organized to maximize functional repertoires and to support highly efficient integration of information.

Databasing receptor distributions in the brain.

Receptor distributions in the brain are studied by autoradiographic mapping in brain slices, which is a labor-intensive and expensive procedure. To keep track of the results of such studies, we have designed CoReDat, a multi-user relational database system that is available for download from www.cocomac.org/coredat. Here, we describe the data model and provide an architectural overview of CoReDat for the neuroscientist who wants to use this database, adapt it for related purposes, or build a new one.

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.

Cell type-specific circuits of cortical layer IV spiny neurons.

Sensory signal processing in cortical layer IV involves two major morphological classes of excitatory neurons: spiny stellate and pyramidal cells. It is essentially unknown how these two cell types are integrated into intracortical networks and whether they play different roles in cortical signal processing. We mapped their cell-specific intracortical afferents in rat somatosensory cortex through a combination of whole-cell patch-clamp recordings and caged glutamate photolysis. Spiny stellate cells received monosynaptic excitation and inhibition originating almost exclusively from neurons located within the same barrel. Pyramidal cells, by contrast, displayed additional excitatory inputs from nongranular layers and from neighboring barrels. Their inhibitory inputs originated, as for spiny stellate cells, mainly from neurons located in the same barrel. These results indicate that spiny stellate cells act predominantly as local signal processors within a single barrel, whereas pyramidal cells globally integrate horizontal and top-down information within a functional column and between neighboring barrels.

Optical release of caged glutamate for stimulation of neurons in the in vitro slice preparation.

Optical stimulation techniques prove useful to map functional inputs in the in vitro brain slice preparation: Glutamate released by a focused beam of UV light induces action potentials, which can be detected in postsynaptic neurons. The direct activation effect is influenced by factors such as compound concentration, focus depth, light absorption in the tissue, and sensitivity of different neuronal domains. We analyze information derived from direct stimulation experiments in slices from rat barrel cortex and construct a computational model of a layer V pyramidal neuron that reproduces the experimental findings. The model predictions concerning the influence of focus depth on input maps and action potential generation are investigated further in subsequent experiments where the focus depth of a high-numerical-aperture lens is systematically varied. With our setup flashes from a xenon light source can activate neuronal compartments to a depth of 200 mum below the surface of the slice. The response amplitude is influenced both by tissue depth and focus plane. Specific somatodendritic structures can be targeted as the probability of action potential induction falls off exponentially with distance. Somata and primary apical dendrites are most sensitive to uncaged glutamate with locally increased sensitivity on proximal apical dendrites. We conclude that optical stimulation can be targeted with high precision.

The Scalable Brain Atlas: Instant Web-Based Access to Public Brain Atlases and Related Content.

The Scalable Brain Atlas (SBA) is a collection of web services that provide unified access to a large collection of brain atlas templates for different species. Its main component is an atlas viewer that displays brain atlas data as a stack of slices in which stereotaxic coordinates and brain regions can be selected. These are subsequently used to launch web queries to resources that require coordinates or region names as input. It supports plugins which run inside the viewer and respond when a new slice, coordinate or region is selected. It contains 20 atlas templates in six species, and plugins to compute coordinate transformations, display anatomical connectivity and fiducial points, and retrieve properties, descriptions, definitions and 3d reconstructions of brain regions. The ambition of SBA is to provide a unified representation of all publicly available brain atlases directly in the web browser, while remaining a responsive and light weight resource that specializes in atlas comparisons, searches, coordinate transformations and interactive displays.

Online retrieval, processing, and visualization of primate connectivity data from the CoCoMac database.

Connectivity is the key to understanding distributed and cooperative brain functions. Detailed and comprehensive data on large-scale connectivity between primate brain areas have been collated systematically from published reports of experimental tracing studies. Although the majority of the data have been made easily available for online retrieval, the multiplicity of brain maps and the precise requirements of anatomical naming limit the intuitive access to the data. The quality of data retrieval can be improved by observing a small set of conventions in data representation. Standardized interfaces open up further opportunities for automated search and retrieval, for flexible visualization of data, and for interoperability with other databases. This article provides a discussion and examples in text and image of the capabilities of the online interface to the CoCoMac database of primate connectivity. These serve to point out sources of potential confusion and failure, and to demonstrate the automated interfacing with other neuroinformatics resources that facilitate selection and processing of connectivity data, for example, for computational modelling and interpretation of functional imaging studies.

Orbitofrontal cortical dysfunction in akinetic catatonia: a functional magnetic resonance imaging study during negative emotional stimulation.

Catatonia is a psychomotor syndrome characterized by concurrent emotional, behavioral, and motor anomalies. Pathophysiological mechanisms of psychomotor disturbances may be related to abnormal emotional-motor processing in prefrontal cortical networks. We therefore investigated prefrontal cortical activation and connectivity patterns during emotional-motor stimulation using functional magnetic resonance imaging (FMRI). We investigated 10 akinetic catatonic patients in a postacute state and compared them with 10 noncatatonic postacute psychiatric controls (age-, sex-, diagnosis-, and medication-matched) and 10 healthy controls. Positive and negative pictures from the International Affective Picture System were used for emotional stimulation. FMRI measurements covered the whole frontal lobe, activation signals in various frontal cortical regions were obtained, and functional connectivity between the different prefrontal cortical regions was investigated using structural equation modeling. Catatonic patients showed alterations in the orbitofrontal cortical activation pattern and in functional connectivity to the premotor cortex in negative and positive emotions compared to psychiatric and healthy controls. Catatonic behavioral and affective symptoms correlated significantly with orbitofrontal activity, whereas catatonic motor symptoms were rather related to medial prefrontal activity. It is concluded that catatonic symptoms may be closely related to dysfunction in the orbitofrontal cortex and consequent alteration in the prefrontal cortical network during emotional processing. Because we investigated postacute patients, orbitofrontal cortical alterations may be interpreted as a trait marker predisposing for development of catatonic syndrome in schizophrenic or affective psychosis.

Network participation indices: characterizing component roles for information processing in neural networks.

We propose a set of indices that characterize-on the basis of connectivity data-how a network node participates in a larger network and what roles it may take given the specific sub-network of interest. These Network Participation Indices are derived from simple graph theoretic measures and have the interesting property of linking local features of individual network components to distributed properties that arise within the network as a whole. We use connectivity data on large-scale cortical networks to demonstrate the virtues of this approach and highlight some interesting features that had not been brought up in previously published material. Some implications of our approach for defining network characteristics relevant to functional segregation and functional integration, for example, from functional imaging studies are discussed.

Models are better than their theory.

As modelling becomes a popular approach in the study of biological systems it is necessary to clarify its concepts and dimensions. This helps to characterize and to distinguish models but cannot establish their quality. The virtue of a model depends on the insight gained in respect to a specific scientific question, and it is hard to measure this with a theory.

Auditory-prefrontal axonal connectivity in the macaque cortex: quantitative assessment of processing streams.

Primate sensory systems subserve complex neurocomputational functions. Consequently, these systems are organised anatomically in a distributed fashion, commonly linking areas to form specialised processing streams. Each stream is related to a specific function, as evidenced from studies of the visual cortex, which features rather prominent segregation into spatial and non-spatial domains. It has been hypothesised that other sensory systems, including auditory, are organised in a similar way on the cortical level. Recent studies offer rich qualitative evidence for the dual stream hypothesis. Here we provide a new paradigm to quantitatively uncover these patterns in the auditory system, based on an analysis of multiple anatomical studies using multivariate techniques. As a test case, we also apply our assessment techniques to more ubiquitously-explored visual system. Importantly, the introduced framework opens the possibility for these techniques to be applied to other neural systems featuring a dichotomised organisation, such as language or music perception.