Brainhack: a collaborative workshop for the open neuroscience community.

Brainhack events offer a novel workshop format with participant-generated content that caters to the rapidly growing open neuroscience community. Including components from hackathons and unconferences, as well as parallel educational sessions, Brainhack fosters novel collaborations around the interests of its attendees. Here we provide an overview of its structure, past events, and example projects. Additionally, we outline current innovations such as regional events and post-conference publications. Through introducing Brainhack to the wider neuroscience community, we hope to provide a unique conference format that promotes the features of collaborative, open science.

LinkRbrain: multi-scale data integrator of the brain.

BACKGROUND: LinkRbrain is an open-access web platform for multi-scale data integration and visualization of human brain data. This platform integrates anatomical, functional, and genetic knowledge produced by the scientific community. NEW METHOD: The linkRbrain platform has two major components: (1) a data aggregation component that integrates multiple open databases into a single platform with a unified representation; and (2) a website that provides fast multi-scale integration and visualization of these data and makes the results immediately available. RESULTS: LinkRbrain allows users to visualize functional networks or/and genetic expression over a standard brain template (MNI152). Interrelationships between these components based on topographical overlap are displayed using relational graphs. Moreover, linkRbrain enables comparison of new experimental results with previous published works. COMPARISON WITH EXISTING METHODS: Previous tools and studies illustrate the opportunities of data mining across multiple tiers of neuroscience and genetic information. However, a global systematic approach is still missing to gather cognitive, topographical, and genetic knowledge in a common framework in order to facilitate their visualization, comparison, and integration. CONCLUSIONS: LinkRbrain is an efficient open-access tool that affords an integrative understanding of human brain function.

Differences in human cortical gene expression match the temporal properties of large-scale functional networks.

We explore the relationships between the cortex functional organization and genetic expression (as provided by the Allen Human Brain Atlas). Previous work suggests that functional cortical networks (resting state and task based) are organized as two large networks (differentiated by their preferred information processing mode) shaped like two rings. The first ring--Visual-Sensorimotor-Auditory (VSA)--comprises visual, auditory, somatosensory, and motor cortices that process real time world interactions. The second ring--Parieto-Temporo-Frontal (PTF)--comprises parietal, temporal, and frontal regions with networks dedicated to cognitive functions, emotions, biological needs, and internally driven rhythms. We found--with correspondence analysis--that the patterns of expression of the 938 genes most differentially expressed across the cortex organized the cortex into two sets of regions that match the two rings. We confirmed this result using discriminant correspondence analysis by showing that the genetic profiles of cortical regions can reliably predict to what ring these regions belong. We found that several of the proteins--coded by genes that most differentiate the rings--were involved in neuronal information processing such as ionic channels and neurotransmitter release. The systematic study of families of genes revealed specific proteins within families preferentially expressed in each ring. The results showed strong congruence between the preferential expression of subsets of genes, temporal properties of the proteins they code, and the preferred processing modes of the rings. Ionic channels and release-related proteins more expressed in the VSA ring favor temporal precision of fast evoked neural transmission (Sodium channels SCNA1, SCNB1 potassium channel KCNA1, calcium channel CACNA2D2, Synaptotagmin SYT2, Complexin CPLX1, Synaptobrevin VAMP1). Conversely, genes expressed in the PTF ring favor slower, sustained, or rhythmic activation (Sodium channels SCNA3, SCNB3, SCN9A potassium channels KCNF1, KCNG1) and facilitate spontaneous transmitter release (calcium channel CACNA1H, Synaptotagmins SYT5, Complexin CPLX3, and synaptobrevin VAMP2).

Striatal and hippocampal involvement in motor sequence chunking depends on the learning strategy.

Motor sequences can be learned using an incremental approach by starting with a few elements and then adding more as training evolves (e.g., learning a piano piece); conversely, one can use a global approach and practice the whole sequence in every training session (e.g., shifting gears in an automobile). Yet, the neural correlates associated with such learning strategies in motor sequence learning remain largely unexplored to date. Here we used functional magnetic resonance imaging to measure the cerebral activity of individuals executing the same 8-element sequence after they completed a 4-days training regimen (2 sessions each day) following either a global or incremental strategy. A network comprised of striatal and fronto-parietal regions was engaged significantly regardless of the learning strategy, whereas the global training regimen led to additional cerebellar and temporal lobe recruitment. Analysis of chunking/grouping of sequence elements revealed a common prefrontal network in both conditions during the chunk initiation phase, whereas execution of chunk cores led to higher mediotemporal activity (involving the hippocampus) after global than incremental training. The novelty of our results relate to the recruitment of mediotemporal regions conditional of the learning strategy. Thus, the present findings may have clinical implications suggesting that the ability of patients with lesions to the medial temporal lobe to learn and consolidate new motor sequences may benefit from using an incremental strategy.

Resting state networks' corticotopy: the dual intertwined rings architecture.

How does the brain integrate multiple sources of information to support normal sensorimotor and cognitive functions? To investigate this question we present an overall brain architecture (called "the dual intertwined rings architecture") that relates the functional specialization of cortical networks to their spatial distribution over the cerebral cortex (or "corticotopy"). Recent results suggest that the resting state networks (RSNs) are organized into two large families: 1) a sensorimotor family that includes visual, somatic, and auditory areas and 2) a large association family that comprises parietal, temporal, and frontal regions and also includes the default mode network. We used two large databases of resting state fMRI data, from which we extracted 32 robust RSNs. We estimated: (1) the RSN functional roles by using a projection of the results on task based networks (TBNs) as referenced in large databases of fMRI activation studies; and (2) relationship of the RSNs with the Brodmann Areas. In both classifications, the 32 RSNs are organized into a remarkable architecture of two intertwined rings per hemisphere and so four rings linked by homotopic connections. The first ring forms a continuous ensemble and includes visual, somatic, and auditory cortices, with interspersed bimodal cortices (auditory-visual, visual-somatic and auditory-somatic, abbreviated as VSA ring). The second ring integrates distant parietal, temporal and frontal regions (PTF ring) through a network of association fiber tracts which closes the ring anatomically and ensures a functional continuity within the ring. The PTF ring relates association cortices specialized in attention, language and working memory, to the networks involved in motivation and biological regulation and rhythms. This "dual intertwined architecture" suggests a dual integrative process: the VSA ring performs fast real-time multimodal integration of sensorimotor information whereas the PTF ring performs multi-temporal integration (i.e., relates past, present, and future representations at different temporal scales).

A neural network model for the acquisition of a spatial body scheme through sensorimotor interaction.

This letter presents a novel unsupervised sensory matching learning technique for the development of an internal representation of three-dimensional information. The representation is invariant with respect to the sensory modalities involved. Acquisition of the internal representation is demonstrated with a neural network model of a sensorimotor system of a simple model creature, consisting of a tactile-sensitive body and a multiple-degrees-of-freedom arm with proprioceptive sensitivity. Acquisition of the 3D representation as well as a distributed representation of the body scheme, occurs through sensorimotor interactions (i.e., the sensory-motor experience of the creature). Convergence of the learning is demonstrated through computer simulations for the model creature with a 7-DoF arm and a spherical body covered by 20 tactile fields.

Parkinson's disease differentially affects adaptation to gradual as compared to sudden visuomotor distortions.

Patients with Parkinson's disease (PD) have difficulties in movement adaptation to optimize performance in novel environmental contexts such as altered screen cursor-hand relationships. Prior studies have shown that the time course of the distortion differentially affects visuomotor adaptation to screen cursor rotations, suggesting separate mechanisms for gradual and sudden adaptation. Moreover, studies in human and non-human primates suggest that adaptation to sudden kinematic distortions may engage the basal ganglia, whereas adaptation to gradual kinematic distortions involves cerebellar structures. In the present studies, participants were patients with PD, who performed center-out pointing movements, using either a digitizer tablet and pen or a computer trackball, under normal or rotated screen cursor feedback conditions. The initial study tested patients with PD using a cross-over experimental design for adaptation to gradual as compared with sudden rotated hand-screen cursor relationships and revealed significant after-effects for the gradual adaptation task only. Consistent with these results, findings from a follow-up experiment using a trackball that required only small finger movements showed that patients with PD adapt better to gradual as against sudden perturbations, when compared to age-matched healthy controls. We conclude that Parkinson's disease affects adaptation to sudden visuomotor distortions but spares adaptation to gradual distortions.

Neural basis of sensitive skin: an fMRI study.

BACKGROUND/PURPOSE: About 50% of women declare themselves to have sensitive skin. However, sensitive skin still appears to be a questionable problem not corresponding to a specific physiological pattern. To objectivate the neural basis of sensitive skin, we measured cerebral response to cutaneous provocative tests in self-perceived sensitive and non-sensitive skin subjects using functional magnetic resonance imaging (fMRI). METHODS: Subjects were divided into two groups according to their self-perceived characterization by using a dedicated questionnaire about their skin reactivity. Event-related fMRI was used to measure cerebral activation associated with skin discomfort induced by a simultaneous split-face application of lactic acid and of its vehicle. RESULTS AND DISCUSSION: In both groups, skin discomfort due to lactic acid increased activity in the primary sensorimotor cortex contralateral to application site and in a bilateral fronto-parietal network including parietal cortex, prefrontal areas around the superior frontal sulcus, and the supplementary motor area. However, activity was significantly larger in the sensitive skin group. Most remarkably, in the sensitive skin group only, activity spreaded into the ipsilateral primary sensorimotor cortex and the bilateral peri-insular secondary somatosensory area. Our results demonstrate that, compared with control subjects, self-perceived sensitive skin subjects have a specific cerebral activation during skin irritative test, which allows us to hypothesize that self-perceived sensitive skin is intrinsically linked to a specific neurophysiologic pattern for these subjects. CONCLUSION: This study demonstrates that fMRI is an effective objective method for measuring cerebral processes underlying skin reactivity and contributes to a better understanding of the neural basis of the sensitive skin phenomenon.

Is there continuity between categorical and coordinate spatial relations coding? Evidence from a grid/no-grid working memory paradigm.

We ask the question whether the coding of categorical versus coordinate spatial relations depends on different neural networks showing hemispheric specialization or whether there is continuity between these two coding types. The 'continuous spatial coding' hypothesis would mean that the two coding types rely essentially on the same neural network consisting of more general-purpose processes, such as visuo-spatial attention, but with a different weighting of these general processes depending on exact task requirements. With event-related fMRI, we have studied right-handed male subjects performing a grid/no-grid visuo-spatial working memory task inducing categorical and coordinate spatial relations coding. Our data support the 'continuous spatial coding' hypothesis, indicating that, while based on the same fronto-parieto-occipital neural network than categorical spatial relations coding, the coding of coordinate spatial relations relies more heavily on attentional and executive processes, which could induce hemispheric differences similar to those described in the literature. The results also show that visuo-spatial working memory consists of a short-term posterior store with a capacity of up to three elements in the parietal and extrastriate cortices. This store depends on the presence of a visible space categorization and thus can be used for the coding of categorical spatial relations. When no visible space categorization is given or when more than three elements have to be coded, additional attentional and executive processes are recruited, mainly located in the dorso-lateral prefrontal cortex.

Supervised learning in a recurrent network of rate-model neurons exhibiting frequency adaptation.

For gradient descent learning to yield connectivity consistent with real biological networks, the simulated neurons would have to include more realistic intrinsic properties such as frequency adaptation. However, gradient descent learning cannot be used straightforwardly with adapting rate-model neurons because the derivative of the activation function depends on the activation history. The objectives of this study were to (1) develop a simple computational approach to reproduce mathematical gradient descent and (2) use this computational approach to provide supervised learning in a network formed of rate-model neurons that exhibit frequency adaptation. The results of mathematical gradient descent were used as a reference in evaluating the performance of the computational approach. For this comparison, standard (nonadapting) rate-model neurons were used for both approaches. The only difference was the gradient calculation: the mathematical approach used the derivative at a point in weight space, while the computational approach used the slope for a step change in weight space. Theoretically, the results of the computational approach should match those of the mathematical approach, as the step size is reduced but floating-point accuracy formed a lower limit to usable step sizes. A systematic search for an optimal step size yielded a computational approach that faithfully reproduced the results of mathematical gradient descent. The computational approach was then used for supervised learning of both connection weights and intrinsic properties of rate-model neurons to convert a tonic input into a phasic-tonic output pattern. Learning produced biologically realistic connectivity that essentially used a monosynaptic connection from the tonic input neuron to an output neuron with strong frequency adaptation as compared to a complex network when using nonadapting neurons. Thus, more biologically realistic connectivity was achieved by implementing rate-model neurons with more realistic intrinsic properties. Our computational approach could be applied to learning of other neuron properties.

A morphogenetic model for the development of cortical convolutions.

The convolutions of the mammalian cortex are one of its most intriguing characteristics. Their pattern is very distinctive for different species, and there seems to be a remarkable relationship between convolutions and the architectonic and functional regionalization of the cerebral cortex. Yet the mechanisms behind the development of convolutions and their association with the cortical regionalization are poorly understood. Here we propose a morphogenetic model for the development of cortical convolutions based on the structure of the cortex as a closed surface with glial and axonal fibres pulling radially, the fundamental mechanical properties of cortex and fibres (elasticity and plasticity), and the growth of the cortical surface. The computer simulations of this model suggest that convolutions are a natural consequence of cortical growth. The model reproduces several aspects of convolutional development, such as the relationship between cortical surface and brain volume among mammals, the period of compensation in the degree of convolution observed in gyrencephalic brains and the dependence of the degree of convolution on cortical thickness. We have also studied the effect of early cortical regionalization on the development of convolutions by introducing geometric, mechanic and growth asymmetries in the model. The morphogenetic model is thus able to reproduce the gradients in the degree of convolution, the development of primary, secondary and tertiary convolution, and the overproduction of sulci observed in animals with altered afferent cortical connections.

Geometric atlas: modeling the cortex as an organized surface.

Recent atlases of the cortical surface are based on a modelization of the cerebral cortex as a topological sphere. This captures effectively its organization as a regular bidimensional sheet of layers parallel to the surface and with perpendicular cortical columns. Yet, while in the vertical direction cortices are almost the same throughout phylia, in the sense of its surface the cerebral cortex is one of the most variable and distinctive parts of the nervous system. Indeed, gyri and sulci appear to have a crucial organizing role in an architectonic, connectional, and functional sense. This organization is not explicitly captured by the surface model of the cortex. We propose a geometric model of the cortical anatomy based on flat representations of principal sulci obtained from surface reconstructions of MRI data, and on neuroanatomical and theoretical considerations concerning the folding patterns of the cortex. The cortex is modeled by a sphere where primary sulci are included as axes. The arrangement of the axes is a simplification of the arrangement of principal sulci observed in flat stereographic representations of the whole cortical surface. The position of secondary and tertiary sulci is then defined by a field of orientations parallel and orthogonal to the axes. We consider the use of the geometric model as a synthetic reference cortex for addressing reconstructions of cortical surfaces. We present a method which establishes a bijection between the geometric model and a cortical surface reconstruction by using the axes of the model as boundary conditions for a set of partial differential equations solved over both surfaces. Using the geometric model as atlas provides a natural parameterization of the cortical surface that, unlike angular coordinates, allows for a localization based on the surface distance to its main organizing landmarks and folding patterns.

Brain processing of visual sexual stimuli in healthy men: a functional magnetic resonance imaging study.

The brain plays a central role in sexual motivation. To identify cerebral areas whose activation was correlated with sexual desire, eight healthy male volunteers were studied with functional magnetic resonance imaging (fMRI). Visual stimuli were sexually stimulating photographs (S condition) and emotionally neutral photographs (N condition). Subjective responses pertaining to sexual desire were recorded after each condition. To image the entire brain, separate runs focused on the upper and the lower parts of the brain. Statistical Parametric Mapping was used for data analysis. Subjective ratings confirmed that sexual pictures effectively induced sexual arousal. In the S condition compared to the N condition, a group analysis conducted on the upper part of the brain demonstrated an increased signal in the parietal lobes (superior parietal lobules, left intraparietal sulcus, left inferior parietal lobule, and right postcentral gyrus), the right parietooccipital sulcus, the left superior occipital gyrus, and the precentral gyri. In addition, a decreased signal was recorded in the right posterior cingulate gyrus and the left precuneus. In individual analyses conducted on the lower part of the brain, an increased signal was found in the right and/or left middle occipital gyrus in seven subjects, and in the right and/or left fusiform gyrus in six subjects. In conclusion, fMRI allows to identify brain responses to visual sexual stimuli. Among activated regions in the S condition, parietal areas are known to be involved in attentional processes directed toward motivationally relevant stimuli, while frontal premotor areas have been implicated in motor preparation and motor imagery. Further work is needed to identify those specific features of the neural responses that distinguish sexual desire from other emotional and motivational states.

An integrative theory of the phasic and tonic modes of dopamine modulation in the prefrontal cortex.

This paper presents a model of both tonic and phasic dopamine (DA) effects on maintenance of working memory representations in the prefrontal cortex (PFC). The central hypothesis is that DA modulates the efficacy of inputs to prefrontal pyramidal neurons to prevent interferences for active maintenance. Phasic DA release, due to DA neurons discharges, acts at a short time-scale (a few seconds), while the tonic mode of DA release, independent of DA neurons firing, acts at a long time-scale (a few minutes). The overall effect of DA modulation is modeled as a threshold restricting incoming inputs arriving on PFC neurons. Phasic DA release temporary increases this threshold while tonic DA release progressively increases the basal level of this threshold. Thus, unlike the previous gating theory of phasic DA release, proposing that it facilitates incoming inputs at the time of their arrival, the effect of phasic DA release is supposed to restrict incoming inputs during a period of time after DA neuron discharges. The model links the cellular and behavioral levels during performance of a working memory task. It allows us to understand why a critical range of DA D1 receptors stimulation is required for optimal working memory performance and how D1 receptor agonists (respectively antagonists) increase perseverations (respectively distractability). Finally, the model leads to several testable predictions, including that the PFC regulates DA neurons firing rate to adapt to the delay of the task and that increase in tonic DA release may either improve or decrease performance, depending on the level of DA receptors stimulation at the beginning of the task.

A model of prefrontal cortex dopaminergic modulation during the delayed alternation task.

Working memory performance is modulated by the level of dopamine (DA) D1 receptors stimulation in the prefrontal cortex (PFC). This modulation is exerted at different time scales. Injection of D1 agonists/antagonists exerts a long-lasting influence (several minutes or hours) on PFC pyramidal neurons. In contrast, during performance of a cognitive task, the duration of the postsynaptic effect of phasic DA release is short lasting. The functional relationships of these two time scales of DA modulation remain poorly understood. Here we propose a model that combines these two time scales of DA modulation on a prefrontal neural network. The model links the cellular and behavioral levels during performance of the delayed alternation task. The network, which represents the activity of deep-layer pyramidal neurons with intrinsic neuronal properties, exhibits two stable states of activity that can be switched on and off by excitatory inputs from long-distance cortical areas arriving in superficial layers. These stable states allow PFC neurons to maintain representations during the delay period. The role of an increase of DA receptors stimulation is to restrict inputs arriving on the prefrontal network. The model explains how the level of working memory performance follows an inverted U-shape with an increased stimulation of DA D1 receptors. The model predicts that (1) D1 receptor agonists increase perseverations, (2) D1 antagonists increase distractability, and (3) the duration of the postsynaptic effect of phasic DA release in the PFC is adjusted to the delay period of the task. These results show how the precise duration of the postsynaptic effect of phasic DA release influences behavioral performance during a simple cognitive task.

Medial prefrontal and subcortical mechanisms underlying the acquisition of motor and cognitive action sequences in humans.

The anterior medial prefrontal cortex (AMPC) in humans is involved in affect and in regulating goal-directed behaviors. The precise function of the AMPC, however, is poorly understood. Using magnetic resonance imaging, we found that bilateral regions in the AMPC were selectively recruited to compute the reliability of subjects' expectations that developed when subjects were learning sequences of cognitive tasks. In contrast, regions similarly recruited in learning sequences of motor acts were found in the ventral striatum. Our results show that beyond the execution of motor acts, the AMPC is selectively engaged in computing the relevance of cognitive goals that subjects intend to achieve. This indicates that the fronto-striatal circuit, including the ventral striatum and AMPC, subserves hierarchically distinct evaluative processes mediating the human ability to build behavioral plans, ranging from motor to cognitive action plans.