Anatomical connectivity defines the organization of clusters of cortical areas in the macaque monkey and the cat

The number of different cortical structures in mammalian brains and the number of extrinsic fibres linking these regions are both large. As with any complex system, systematic analysis is required to draw reliable conclusions about the organization of the complex neural networks comprising these numerous elements. One aspect of organization that has long been suspected is that cortical networks are organized into 'streams' or 'systems'. Here we report computational analyses capable of showing whether clusters of strongly interconnected areas are aspects of the global organization of cortical systems in macaque and cat. We used two different approaches to analyse compilations of corticocortical connection data from the macaque and the cat. The first approach, optimal set analysis, employed an explicit definition of a neural 'system' or 'stream', which was based on differential connectivity. We defined a two-component cost function that described the cost of the global cluster arrangement of areas in terms of the areas' connectivity within and between candidate clusters. Optimal cluster arrangements of cortical areas were then selected computationally from the very many possible arrangements, using an evolutionary optimization algorithm. The second approach, non-parametric cluster analysis (NPCA), grouped cortical areas on the basis of their proximity in multidimensional scaling representations. We used non-metric multidimensional scaling to represent the cortical connectivity structures metrically in two and five dimensions. NPCA then analysed these representations to determine the nature of the clusters for a wide range of different cluster shape parameters. The results from both approaches largely agreed. They showed that macaque and cat cortices are organized into densely intra-connected clusters of areas, and identified the constituent members of the clusters. These clusters reflected functionally specialized sets of cortical areas, suggesting that structure and function are closely linked at this gross, systems level.     

Anatomical Organization of Multiple Modulatory Inputs in a Rhythmic Motor System

In rhythmic motor systems, descending projection neuron inputs elicit distinct outputs from their target central pattern generator (CPG) circuits. Projection neuron activity is regulated by sensory inputs and inputs from other regions of the nervous system, relaying information about the current status of an organism. To gain insight into the organization of multiple inputs targeting a projection neuron, we used the identified neuron MCN1 in the stomatogastric nervous system of the crab, Cancer borealis. MCN1 originates in the commissural ganglion and projects to the stomatogastric ganglion (STG). MCN1 activity is differentially regulated by multiple inputs including neuroendocrine (POC) and proprioceptive (GPR) neurons, to elicit distinct outputs from CPG circuits in the STG. We asked whether these defined inputs are compact and spatially segregated or dispersed and overlapping relative to their target projection neuron. Immunocytochemical labeling, intracellular dye injection and three-dimensional (3D) confocal microscopy revealed overlap of MCN1 neurites and POC and GPR terminals. The POC neuron terminals form a defined neuroendocrine organ (anterior commissural organ: ACO) that utilizes peptidergic paracrine signaling to act on MCN1. The MCN1 arborization consistently coincided with the ACO structure, despite morphological variation between preparations. Contrary to a previous 2D study, our 3D analysis revealed that GPR axons did not terminate in a compact bundle, but arborized more extensively near MCN1, arguing against sparse connectivity of GPR onto MCN1. Consistent innervation patterns suggest that integration of the sensory GPR and peptidergic POC inputs occur through more distributed and more tightly constrained anatomical interactions with their common modulatory projection neuron target than anticipated.     

Prediction of operon-like gene clusters in the Arabidopsis thaliana genome based on co-expression analysis of neighboring genes

Operon-like arrangements of genes occur in eukaryotes ranging from yeasts and filamentous fungi to nematodes, plants, and mammals. In plants, several examples of operon-like gene clusters involved in metabolic pathways have recently been characterized, e.g. the cyclic hydroxamic acid pathways in maize, the avenacin biosynthesis gene clusters in oat, the thalianol pathway in Arabidopsis thaliana, and the diterpenoid momilactone cluster in rice. Such operon-like gene clusters are defined by their co-regulation or neighboring positions within immediate vicinity of chromosomal regions. A comprehensive analysis of the expression of neighboring genes therefore accounts a crucial step to reveal the complete set of operon-like gene clusters within a genome. Genome-wide prediction of operon-like gene clusters should contribute to functional annotation efforts and provide novel insight into evolutionary aspects acquiring certain biological functions as well. We predicted co-expressed gene clusters by comparing the Pearson correlation coefficient of neighboring genes and randomly selected gene pairs, based on a statistical method that takes false discovery rate (FDR) into consideration for 1469 microarray gene expression datasets of A. thaliana. We estimated that A. thaliana contains 100 operon-like gene clusters in total. We predicted 34 statistically significant gene clusters consisting of 3 to 22 genes each, based on a stringent FDR threshold of 0.1. Functional relationships among genes in individual clusters were estimated by sequence similarity and functional annotation of genes. Duplicated gene pairs (determined based on BLAST with a cutoff of E<10(-5)) are included in 27 clusters. Five clusters are associated with metabolism, containing P450 genes restricted to the Brassica family and predicted to be involved in secondary metabolism. Operon-like clusters tend to include genes encoding bio-machinery associated with ribosomes, the ubiquitin/proteasome system, secondary metabolic pathways, lipid and fatty-acid metabolism, and the lipid transfer system.     

A simple clustering algorithm can be accurate enough for use in calculations of pKs in macromolecules

Structure and function of macromolecules depend critically on the ionization states of their acidic and basic groups. Most current structure-based theoretical methods that predict pK of ionizable groups in macromolecules include, as one of the key steps, a computation of the partition sum (Boltzmann average) over all possible protonation microstates. As the number of these microstates depends exponentially on the number of ionizable groups present in the molecule, direct computation of the sum is not realistically feasible for many typical proteins that may have tens or even hundreds of ionizable groups. We have tested a simple and robust approximate algorithm for computing these partition sums for macromolecules. The method subdivides the interacting sites into independent clusters, based upon the strength of site-site electrostatic interaction. The resulting partition function is factorizable into computationally manageable components. Two variants of the approach are presented and validated on a representative test set of 602 proteins, by comparing the pK(1/2) values computed by the proposed method with those obtained by the standard Monte Carlo approach used as a reference. With 95% confidence, the relative error introduced by the more accurate of the two methods is less than 0.25 pK units. The algorithms are one to two orders of magnitude faster than the Monte Carlo method, with the typical settings. A graphical representation is introduced that visualizes the clusters of strong site-site interactions in the context of the three-dimensional (3D) structure of the macromolecule, facilitating identification of functionally important clusters of ionizable groups; the approach is exemplified on two proteins, bacteriorhodopsin and myoglobin.     

Gravity Influences Top-Down Signals in Visual Processing

Visual perception is not only based on incoming visual signals but also on information about a multimodal reference frame that incorporates vestibulo-proprioceptive input and motor signals. In addition, top-down modulation of visual processing has previously been demonstrated during cognitive operations including selective attention and working memory tasks. In the absence of a stable gravitational reference, the updating of salient stimuli becomes crucial for successful visuo-spatial behavior by humans in weightlessness. Here we found that visually-evoked potentials triggered by the image of a tunnel just prior to an impending 3D movement in a virtual navigation task were altered in weightlessness aboard the International Space Station, while those evoked by a classical 2D-checkerboard were not. Specifically, the analysis of event-related spectral perturbations and inter-trial phase coherency of these EEG signals recorded in the frontal and occipital areas showed that phase-locking of theta-alpha oscillations was suppressed in weightlessness, but only for the 3D tunnel image. Moreover, analysis of the phase of the coherency demonstrated the existence on Earth of a directional flux in the EEG signals from the frontal to the occipital areas mediating a top-down modulation during the presentation of the image of the 3D tunnel. In weightlessness, this fronto-occipital, top-down control was transformed into a diverging flux from the central areas toward the frontal and occipital areas. These results demonstrate that gravity-related sensory inputs modulate primary visual areas depending on the affordances of the visual scene.     

Human cortical mechanisms of visual attention during orienting and search.

Functional anatomical studies indicate that a set of neural signals in parietal and frontal cortex mediates the covert allocation of attention to visual locations across a wide variety of visual tasks. This frontoparietal network includes areas, such as the frontal eye field and supplementary eye field. This anatomical overlap suggests that shifts of attention to visual locations of objects recruit areas involved in oculomotor programming and execution. Finally, the fronto-parietal network may be the source of spatial attentional modulations in the ventral visual system during object recognition or discrimination.     

Probabilistic Clustering of the Human Connectome Identifies Communities and Hubs

A fundamental assumption in neuroscience is that brain function is constrained by its structural properties. This motivates the idea that the brain can be parcellated into functionally coherent regions based on anatomical connectivity patterns that capture how different areas are interconnected. Several studies have successfully implemented this idea in humans using diffusion weighted MRI, allowing parcellation to be conducted in vivo. Two distinct approaches to connectivity-based parcellation can be identified. The first uses the connection profiles of brain regions as a feature vector, and groups brain regions with similar connection profiles together. Alternatively, one may adopt a network perspective that aims to identify clusters of brain regions that show dense within-cluster and sparse between-cluster connectivity. In this paper, we introduce a probabilistic model for connectivity-based parcellation that unifies both approaches. Using the model we are able to obtain a parcellation of the human brain whose clusters may adhere to either interpretation. We find that parts of the connectome consistently cluster as densely connected components, while other parts consistently result in clusters with similar connections. Interestingly, the densely connected components consist predominantly of major cortical areas, while the clusters with similar connection profiles consist of regions that have previously been identified as the ‘rich club’; regions known for their integrative role in connectivity. Furthermore, the probabilistic model allows quantification of the uncertainty in cluster assignments. We show that, while most clusters are clearly delineated, some regions are more difficult to assign. These results indicate that care should be taken when interpreting connectivity-based parcellations obtained using alternative deterministic procedures.     

Sequential discharges of phasic activities (PGO waves) during paradoxical sleep after selective cortical lesions in the cat.

We investigated the role that cortical areas have upon the sequences of PGO waves and associated phasic activity in the lateral rectus muscles of the eyes (PALRE) during PS. We performed several combined type of lesions (bilateral ablations of frontal lobes carried out in two sessions, or occipital lesions combined with bifrontla lobotomies). We used statistical methods previously described for the normal cat (4). We showed that frontal lesions need to be bilateral in order to be effective, that thereafter there is a recovery in the complexity of the patterns in the survival period. We showed that occipital lobectomies "per se" do not affect the PGO pattern, but their combination with bifrontal lobotomies produce the most dramatic changes which lasted throughout the survival period. These findings are discussed on the light of the well known anatomical cortical projections to brain stem structures which participate in the determinism of PGO waves during paradoxical sleep.     

Changes in the spatial organization of the cortical electrical activity during formation and actualization of a cognitive set to facial expression

Coherence function of the EEG in the bands of 8-13 (alpha rhythm) and 14-25 Hz (beta rhythm) was analyzed in 35 healthy adult subjects during formation and testing of a visual cognitive set to pictures of faces with different emotional expressions. The intra- and interhemispheric coherences of the potentials in the frontal area and coherence between the right frontal and temporal derivation were shown to increase at the stage of set actualization. The results of the analysis confirm the suggestion that the frontal cortical areas are predominantly involved in formation and actualization of the set to facial emotional expression. The conclusion is based on the idea that the spatial synchronization of the brain electrical potentials is an index of the functional relations between the corresponding cortical areas and their cooperative involvement in a certain kind of activity (their simultaneous activation).     

Organization of the Human Frontal Pole Revealed by Large-Scale DTI-Based Connectivity: Implications for Control of Behavior

The goal of the current study was to examine the pattern of anatomical connectivity of the human frontal pole so as to inform theories of function of the frontal pole, perhaps one of the least understood region of the human brain. Rather than simply parcellating the frontal pole into subregions, we focused on examining the brain regions to which the frontal pole is anatomically and functionally connected. While the current findings provided support for previous work suggesting the frontal pole is connected to higher-order sensory association cortex, we found novel evidence suggesting that the frontal pole in humans is connected to posterior visual cortex. Furthermore, we propose a functional framework that incorporates these anatomical connections with existing cognitive theories of the functional organization of the frontal pole. In addition to a previously discussed medial-lateral distinction, we propose a dorsal-ventral gradient based on the information the frontal pole uses to guide behavior. We propose that dorsal regions are connected to other prefrontal regions that process goals and action plans, medial regions are connected to other brain regions that monitor action outcomes and motivate behaviors, and ventral regions connect to regions that process information about stimuli, values, and emotion. By incorporating information across these different levels of information, the frontal pole can effectively guide goal-directed behavior.     

Testing the applicability of ecosystem services mapping methods for peri-urban contexts: A case study for Paris

Through their semi-natural and agricultural areas, peri-urban regions are pivotal in providing ecosystem services (ES) to city dwellers. To quantify the ES provided by these areas, it is possible to use ES mapping methods: many ES mapping methods rely on land cover maps, but most maps are coarse compared to the peri-urban scale. Nevertheless, readily-available land use data and methods are often used to map ES at such scales, without contextualisation. As a result, such methods may not be able to capture the diversity that is present in the peri-urban vegetation, which could have consequences for their accuracy and furthermore for urban planning policies.To increase our understanding of the applicability of ES mapping methods in peri-urban regions, we assessed to what degree sites with similar plant composition in the green belt of Paris, France, were also projected to have similar ES bundles. We considered two commonly used ES model types: proxy-based models (here: look-up tables) and phenomenological models. We used 252 sites for which botanical survey data were available and applied the ES models to seven ES relevant in the peri-urban context. A cluster analysis was used to group sites, hence facilitating analyse of the spatial congruence between types of vegetation and bundles of ES.Clustering sites based on plant composition revealed six distinct clusters. Clustering sites based on ES bundles as estimated by phenomenological models and proxy-based models, resulted in four and two clusters, respectively. The proxy-based clustering only highlighted broad-leaved forests as an important ES supply source. The phenomenological model estimates of ES allowed a more nuanced clustering of sites into four different groups. The level of congruence between the different sets of clusters based on plant composition and estimated ES bundles was low. Except for forests, the commonly used ES models tested here were not able to represent the same level of heterogeneity in the peri-urban landscape as was found in the vegetation. Our results demonstrate the need to integrate finer scale approaches and primary data in ES assessments of peri-urban areas.     

Task-switching Cost and Intrinsic Functional Connectivity in the Human Brain: Toward Understanding Individual Differences in Cognitive Flexibility

The human ability to flexibly alternate between tasks (i.e., task-switching) represents a critical component of cognitive control. Many functional magnetic resonance imaging (fMRI) studies have explored the neural basis of the task-switching. However, no study to date has examined how individual differences in intrinsic functional architecture of the human brain are related to that of the task-switching. In the present study, we took 11 task-switching relevant areas from a meta-analysis study as the regions of interests (ROIs) and estimated their intrinsic functional connectivity (iFC) with the whole brain. This procedure was repeated for 32 healthy adults based upon their fMRI scans during resting-state (rfMRI) to investigate the correlations between switching cost and the iFC strength across these participants. This analysis found that switch cost was negatively correlated with a set of iFC involved ROIs including left inferior frontal junction, bilateral superior posterior parietal cortex, left precuneus, bilateral inferior parietal lobule, right middle frontal gyrus and bilateral middle occipital gyrus. These connectivity profiles represent an intrinsic functional architecture of task-switching where the left inferior frontal junction plays a hub role in this brain-behavior association. These findings are highly reproducible in another validation independent sample and provide a novel perspective for understanding the neural basis of individual differences in task-switching behaviors reflected in the intrinsic architecture of the human brain.     

Efficient computation of approximate gene clusters based on reference occurrences

Whole genome comparison based on the analysis of gene cluster conservation has become a popular approach in comparative genomics. While gene order and gene content as a whole randomize over time, it is observed that certain groups of genes which are often functionally related remain co-located across species. However, the conservation is usually not perfect which turns the identification of these structures, often referred to as approximate gene clusters, into a challenging task. In this article, we present an efficient set distance based approach that computes approximate gene clusters by means of reference occurrences. We show that it yields highly comparable results to the corresponding non-reference based approach, while its polynomial runtime allows for approximate gene cluster detection in parameter ranges that used to be feasible only with simpler, e.g., max-gap based, gene cluster models. To illustrate further the performance and predictive power of our algorithm, we compare it to a state-of-the art approach for max-gap gene cluster computation.     

Is there a brainstem substrate for action selection?

The search for the neural substrate of vertebrate action selection has focused on structures in the forebrain and midbrain, and particularly on the group of sub-cortical nuclei known as the basal ganglia. Yet, the behavioural repertoire of decerebrate and neonatal animals suggests the existence of a relatively self-contained neural substrate for action selection in the brainstem. We propose that the medial reticular formation (mRF) is the substrate's main component and review evidence showing that the mRF's inputs, outputs and intrinsic organization are consistent with the requirements of an action-selection system. The internal architecture of the mRF is composed of interconnected neuron clusters. We present an anatomical model which suggests that the mRF's intrinsic circuitry constitutes a small-world network and extend this result to show that it may have evolved to reduce axonal wiring. Potential configurations of action representation within the internal circuitry of the mRF are then assessed by computational modelling. We present new results demonstrating that each cluster's output is most likely to represent activation of a component action; thus, coactivation of a set of these clusters would lead to the coordinated behavioural response observed in the animal. Finally, we consider the potential integration of the basal ganglia and mRF substrates for selection and suggest that they may collectively form a layered/hierarchical control system.     

Sparse representation of brain aging: extracting covariance patterns from structural MRI

An enhanced understanding of how normal aging alters brain structure is urgently needed for the early diagnosis and treatment of age-related mental diseases. Structural magnetic resonance imaging (MRI) is a reliable technique used to detect age-related changes in the human brain. Currently, multivariate pattern analysis (MVPA) enables the exploration of subtle and distributed changes of data obtained from structural MRI images. In this study, a new MVPA approach based on sparse representation has been employed to investigate the anatomical covariance patterns of normal aging. Two groups of participants (group 1:290 participants; group 2:56 participants) were evaluated in this study. These two groups were scanned with two 1.5 T MRI machines. In the first group, we obtained the discriminative patterns using a t-test filter and sparse representation step. We were able to distinguish the young from old cohort with a very high accuracy using only a few voxels of the discriminative patterns (group 1:98.4%; group 2:96.4%). The experimental results showed that the selected voxels may be categorized into two components according to the two steps in the proposed method. The first component focuses on the precentral and postcentral gyri, and the caudate nucleus, which play an important role in sensorimotor tasks. The strongest volume reduction with age was observed in these clusters. The second component is mainly distributed over the cerebellum, thalamus, and right inferior frontal gyrus. These regions are not only critical nodes of the sensorimotor circuitry but also the cognitive circuitry although their volume shows a relative resilience against aging. Considering the voxels selection procedure, we suggest that the aging of the sensorimotor and cognitive brain regions identified in this study has a covarying relationship with each other.     

The chronoarchitecture of the cerebral cortex

We review here a new approach to mapping the human cerebral cortex into distinct subdivisions. Unlike cytoarchitecture or traditional functional imaging, it does not rely on specific anatomical markers or functional hypotheses. Instead, we propose that the unique activity time course (ATC) of each cortical subdivision, elicited during natural conditions, acts as a temporal fingerprint that can be used to segregate cortical subdivisions, map their spatial extent, and reveal their functional and potentially anatomical connectivity. We argue that since the modular organisation of the brain and its connectivity evolved and developed in natural conditions, these are optimal for revealing its organisation. We review the concepts, methodology and first results of this approach, relying on data obtained with functional magnetic resonance imaging (fMRI) when volunteers viewed traditional stimuli or a James Bond movie. Independent component analysis (ICA) was used to identify voxels belonging to distinct functional subdivisions, based on their differential spatio-temporal fingerprints. Many more regions could be segregated during natural viewing, demonstrating that the complexity of natural stimuli leads to more differential responses in more functional modules. We demonstrate that, in a single experiment, a multitude of distinct regions can be identified across the whole brain, even within the visual cortex, including areas V1, V4 and V5. This differentiation is based entirely on the differential ATCs of different areas during natural viewing. Distinct areas can therefore be identified without any a priori hypothesis about their function or spatial location. The areas we identified corresponded anatomically across subjects, and their ATCs showed highly area-specific inter-subject correlations. Furthermore, natural conditions led to a significant de-correlation of interregional ATCs compared to rest, indicating an increase in regional specificity during natural conditions. In contrast, the correlation between ATCs of distant regions of known substantial anatomical connections increased and reflected their known anatomical connectivity pattern. We demonstrate this using the example of the language network involving Broca's and Wernicke's area and homologous areas in the two hemispheres. In conclusion, this new approach to brain mapping may not only serve to identify novel functional subdivisions, but to reveal their connectivity as well.     

Mapping the sequences of potential guanine quadruplex motifs

The knowledge that potential guanine quadruplex sequences (PQs) are non-randomly distributed in relation to genomic features is now well established. However, this is for a general potential quadruplex motif which is characterized by short runs of guanine separated by loop regions, regardless of the nature of the loop sequence. There have been no studies to date which map the distribution of PQs in terms of primary sequence or which categorize PQs. To this end, we have generated clusters of PQ sequence groups of various sizes and various degrees of similarity for the non-template strand of introns in the human genome. We started with 86 697 sequences, and successively merged them into groups based on sequence similarity, carrying out 66 clustering cycles before convergence. We have demonstrated here that by using complete linkage hierarchical agglomerative clustering such PQ sequence categorization can be achieved. Our results give an insight into sequence diversity and categories of PQ sequences which occur in human intronic regions. We also highlight a number of clusters for which interesting relationships among their members were immediately evident and other clusters whose members seem unrelated, illustrating, we believe, a distinct role for different sequence types.