Evolution offunctionalconnectivity: late preterm and post-term periods
While it might be thought that resting-state networks emerge in parallel with the developmentof related cognitive functions mostly during the post-term period, they rather seem to settle during the third trimester of gestation. Between 19 to 39w GA, the fetal brain demonstrates in fcMRI an organization in modules that overlap functional systems observed postnatally (Thomason, et al. 2014). In preterm newborns from 26w GA, functional networks involve varied cortical regions, the thalamus and cerebellum (Doria, et al. 2010; Smyser, et al. 2010), and during the preterm period the connectome architecture strongly develops, the inter- hemispheric connectivity increases (van den Heuvel, et al. 2014), and the age-related patterns ofdevelopment differ across networks (Doria, et al. 2010; Smyser, et al. 2010) (Figure 7a). By term age (40w GA), the full networks (visual, auditory, somatosensory, motor, frontoparietal and executive control networks) are observed (Doria, et al. 2010; Fransson, et al. 2009; van den Heuvel, et al. 2014), the architecture seems similar as the adult, nevertheless cortical hubs and associated networks may remain mostly confined to primary sensory-motor regions, suggesting that architecture is first linked to support tasks related to basal perception and action behavior (Fransson, et al. 2011). During the first postnatal year, the maturation sequence progresses differently across networks, from the primary sensorimotor/auditory networks, to the visual networks, to the default-mode network (highly similar between 2-year-old toddlers and adults (Gao, et al. 2009)), and finally to executive control networks (Gao, et al. 2014). In newborns, note that only high-amplitude EEG events show strong spatial correlations (Omidvarnia, et al. 2013).
Correspondence: Mariano Moreno-de-las-Heras (email@example.com) Received: 24 October 2019 – Discussion started: 2 January 2020
Revised: 12 April 2020 – Accepted: 24 April 2020 – Published: 29 May 2020
Abstract. Connectivity has emerged as a useful concept for exploring the movement of water and sediments between landscape locations and across spatial scales. In this study, we examine the structuralandfunctional controls of surface- patch to hillslope runoff and sediment connectivity in three Mediterranean dry reclaimed mining slope systems that have different long-term development levels of vegetation and rill networks. Structuralconnectivity was assessed using flow path analysis of coupled vegetation distribution and surface topography, providing field indicators of the extent to which surface patches that facilitate runoff and sediment produc- tion are physically linked to one another in the studied hill- slopes. Functionalconnectivity was calculated using the ra- tio of patch-scale to hillslope-scale observations of runoff and sediment yield for 21 monitored hydrologically active rainfall events. The impact of the dynamic interactions be- tween rainfall conditions andstructuralconnectivity on func- tional connectivity were further analysed using general lin- ear models with a backward model structure selection ap- proach. Functional runoff connectivity during precipitation events was found to be dynamically controlled by antecedent
Qingdao, China, 3 Beijing Engineering Research Center of Intelligent Systems and Technology, Beijing, China, 4 CIRAD, Amap
Unit, Univ. Montpellier, CNRS, INRA, IRD, Montpellier, France, 5 Department of Seeds and Seedlings Production, University
Jean Lorougnon Guédé, Daloa, Ivory Coast
Functional-structural plant models (FSPMs) generally simulate plant developmentand growth at the level of individual organs (leaves, flowers, internodes, etc.). Parameters that are not directly measurable, such as the sink strength of organs, can be estimated inversely by fitting the weights of organs along an axis (organic series) with the corresponding model output. To accommodate intracanopy variability among individual plants, stochastic FSPMs have been built by introducing the randomness in plant development; this presents a challenge in comparing model output and experimental data in parameter estimation since the plant axis contains individual organs with different amounts and weights. To achieve model calibration, the interaction between plant developmentand growth is disentangled by first computing the occurrence probabilities of each potential site of phytomer, as defined in the developmental model (potential structure). On this basis, the mean organic series is computed analytically to fit the organ-level target data. This process is applied for plants with continuous and rhythmic development simulated with different development parameter sets. The results are verified by Monte-Carlo simulation. Calibration tests are performed both in silico and on real plants. The analytical organic series are obtained for both continuous and rhythmic cases, and they match well with the results from Monte-Carlo simulation, and vice versa. This fitting process works well for both the simulated and real data sets; thus, the proposed method can solve the source-sink functions of stochastic plant architectures through a simplified approach to plant sampling. This work presents a generic method for estimating the sink parameters of a stochastic FSPM using statistical organ-level data, and it provides a method for sampling stems. The current work breaks a bottleneck in the application of FSPMs to real plants, creating the opportunity for broad applications.
Concerning the involvement of the RSC in the spatial navigation network, our results show an age-related loss ofstructural white matter integrity around the RSC, but no changes in functionalconnectivityof this high-level visual brain region. Several studies have highlighted the key function of the RSC in spatial navigation (for a review, see Mitchell et al., 2018 ) such as its role in translating from egocentric into allocentric reference frames. Neuroimaging studies have reported that the RSC activity was related to recollection processing of permanent visual landmark during a navigation task using a first-person perspective ( Auger et al., 2012; Auger and Maguire, 2018 ). This point was partially assessed by our results showing that older participants with fractional anisotropy values close to young participants between the OPA and RSC exhibited better performance on the figural memory test. Concerning RSC-PPA connectivity, a recent study showed that functionaland anatomical changes in a patient suffering from developmental topographic disorientation were associated with spatial navigation impairments ( Kim et al., 2015 ). However, in the present study of healthy aging, we did not observe functionalconnectivity differences within the RSC-PPA pathway. The preservation offunctionalconnectivity here in healthy older adults may account for their relatively spared navigational abilities compared to patients with topographic disorientation. Indeed, several studies have suggested that a loss of white matter integrity leads to functionalconnectivity changes ( Ferreira and Busatto, 2013 ).
activation of the elicited area; the second is the extent to which it is related to its structuralconnectivity pattern.
These considerations brought us to explore the link between the “natural” frequency response of the stimulated cortical areas and their structural architecture. Interestingly, for the premotor cortex controlateral to the stimulation site the correlation between directed functionalconnectivity at the natural frequency andstructural connections increases after the stimulation and reveals a long-lasting effect over time (Fig.6). The fact that this effect is not reproduced for the superior parietal cortex might be due to a number of reasons. First, it has been shown that this area has lower cortical excitability than the premotor cortex, and thus it is more difficult to trigger (Ferrarelli et al., 2012; Rosanova et al., 2009). Secondly, it is possible that the different frequency responses in each cortical area might reflect different anatomical background. Indeed, recent studies have reported that there is a strong correlation between cytoarchitecture and anatomical andfunctionalconnectivity in cat, macaque and humans (Beul et al., 2015; Scholtens et al., 2014), with superior parietal showing both a different cytoarchitecture as well as a different connectivity architecture than supplementary motor regions (van den Heuvel et al., 2015). This might explain why the functional activation at specific resonant frequencies is related to the structural coupling (i.e., the amount of tracts connecting them) differently depending on the anatomical architecture of the specific brain region.
Modelling the Guayule plant growth anddevelopment with a FunctionalStructural Plant Model
Authors: Loic Brancheriau 3 , Sylvie Sabatier 1,2 , Marc Jaeger 1,2 , Nicolas Hemery 3 , Christ Mougani 1,2 ,
Philippe de Reffye 1,2 , Ali Abed Alsater 1,2 and Serge Palu 3
Avinash Sharma 1 & Dipanjan Roy 5
A challenging problem in cognitive neuroscience is to relate the structuralconnectivity (SC) to the functionalconnectivity (FC) to better understand how large-scale network dynamics underlying human cognition emerges from the relatively fixed SC architecture. Recent modeling attempts point to the possibility of a single diffusion kernel giving a good estimate of the FC. We highlight the shortcomings of the single-diffusion-kernel model (SDK) and propose a multi-scale diffusion scheme. Our multi- scale model is formulated as a reaction-diffusion system giving rise to spatio-temporal patterns on a fixed topology. We hypothesize the presence of inter-regional co-activations (latent parameters) that combine diffusion kernels at multiple scales to characterize how FC could arise from SC. We formulated a multiple kernel learning (MKL) scheme to estimate the latent parameters from training data. Our model is analytically tractable and complex enough to capture the details of the underlying biological phenomena. The parameters learned by the MKL model lead to highly accurate predictions of subject-specific FCs from test datasets at a rate of 71%, surpassing the performance of the existing linear and non-linear models. We provide an example of how these latent parameters could be used to characterize age-specific reorganization in the brain structure and function.
Figure 1 ).
Resting state fMRI data were processed using Nipype ( Gorgolewski et al., 2011 ), a flexible neuroimaging framework that interfaces across multiple software packages. FreeSurfer was used for extracting individual subjects’ ROIs and converting from structural to functional space ( Fischl, 2012 ). Images were registered to a common space using ANTS registration ( Avants et al., 2011 ). Simultaneous motion and slice timing correction was applied ( Roche, 2011 ) and were used to estimate physiological noise with CompCor ( Behzadi et al., 2007 ). Motion outliers were identified with the artifact detection from Nipype and combined with CompCor components and motion parameters for noise reduction. Brain masks were created with the FSL brain extraction tool ( Smith, 2002 ). Data were bandpass- filtered (0.01–0.083 Hz) and smoothed with a 6 mm full-width half-max. (Subcortical data were analyzed without smoothing). For each subject we computed the mean timeseries for each DKT cortical region and FreeSurfer subcortical volume. For each subject we computed the Pearson correlation of each region’s mean timeseries with every other regions’, which were Fisher’s z-transformed for comparison across subjects. This ultimately resulted in a symmetrical 84 × 84 connectivity matrix, including 16 subcortical regions. Resting state data were not collected for one PFS subject. A second PFS subject was removed from the analysis after mean activation in the left frontal pole ROI was 0 across all timepoints. One PWS subject was excluded from the resting state analysis due to incomplete whole-brain coverage during the resting scan.
Resting-state large-scale brain models vary in the amount of biological elements they incor- porate and in the way they are being tested. One might expect that the more realistic the model is, the closer it should reproduce real functional data. It has been shown, instead, that when linear correlation across long BOLD fMRI time-series is used as a measure for func- tional connectivity (FC) to compare simulated and real data, a simple model performs just as well, or even better, than more sophisticated ones. The model in question is a simple linear model, which considers the physiological noise that is pervasively present in our brain while it diffuses across the white-matter connections, that is structuralconnectivity (SC). We deeply investigate this linear model, providing an analytical solution to straightforwardly compute FC from SC without the need of computationally costly simulations of time-series. We provide a few examples how this analytical solution could be used to perform a fast and detailed parameter exploration or to investigate resting-state non-stationarities. Most importantly, by inverting the analytical solution, we propose a method to retrieve information on the anatomi- cal structure directly from functional data. This simple method can be used to complement or guide DTI/DSI and tractography results, especially for a better assessment of inter-hemi- spheric connections, or to provide an estimate of SC when only functional data are available.
A number of open issues require future investigation. It would be important to assess using physiological methods whether aberrantly connected regions of gray matter contain neurons that are indeed intrinsically hyperexcitable as a result of their structuralandfunctional changes, as noted above. Since it takes 22 years on average for seizures to develop in PNH, longitudinal imaging studies in childhood might allow us to establish the time course of the appearance anddevelopmentof the abnormal connectivity shown here. Larger numbers of subjects would also allow for a more nuanced analysis of various anatomical subtypes of PNH, which was not possible here due to small sample size and heterotopia heterogeneity. The evidence to date, however, strongly suggests that an optimal understanding of the neurological pathophysiology of “gray matter heterotopia” will come as much from an appreciation of how these misplaced clusters of neurons are connected, as from the clusters themselves.
Time is a major modulator of vegetative and reproductive developmentof the mango tree. Our results revealed the major role of the temporal factor considered, the mother GU burst date, on the vegetative and reproductive developmentof mango tree architecture. Interestingly, this factor con- ditions probabilities in the simulation processes of growth and flowering of the mother GUs, and is itself a response of the simulation process for the daughter GUs. The rhythmic growth of mango trees, with regular delays between flushes ( Fig. 5 ), seemed to be related to the effects of the mother GU burst date. Since GU and inflorescence burst dates ( Fig. 3A ) were important, they required precise modelling, justifying a posteriori their modelling as an ordinal multinomial dis- tribution, and not as a simpler Poisson distribution to model the delay between mother GU and daughter GU burst dates, that led to less precise dynamics or demography of botanical entities (data not shown). While adapted to model the regular rhythmic growth observed, for example, during growing cycle 1, the Poisson distribution failed to simulate multiple flushes of daughter GUs for populations of mother GUs generated in the same month. It also failed to correctly model the syn- chronized vegetative flush occurring after fruit harvest during growing cycle 2 for mother/ancestor GUs generated in dif- ferent months ( Fig. 5C ).
Visual Word Form Area Language
A B S T R A C T
Learning to read leads to functionalandstructural changes in cortical brain areas related to vision and language. Previous evidence suggests that the Visual Word Form Area (VWFA), a region devoted to the recognition of letter strings in literate persons, acts as an interface between both systems. While different studies have performed univariate analyses to study the effects of literacy on brain function, little is known about its impact on whole functional networks, especially when literacy is acquired during adulthood. We investigated functional connec- tivity in three groups of adults with different literacy status: illiterates, ex-illiterates (i.e., who learned to read during adulthood), and literates (i.e., who learned to read in childhood). We used a data-driven, multivariate whole brain approach (Independent Component Analysis [ICA]) combined with a region of interest (ROI) analysis in order to explore the functionalconnectivityof the VWFA with four ICA networks related to vision and language functions. ICA allowed for the identiﬁcation of four networks of interest: left fronto-parietal, auditory, medial visual and lateral visual functional networks, plus a control right fronto-parietal network. We explored the effects literacy on the connectivity between the VWFA and these networks, trying furthermore to disentangle the roles of reading proﬁciency and age of acquisition (i.e., literacy status) in these changes. Results showed that functionalconnectivity between the VWFA and the left fronto-parietal and lateral visual networks increased and decreased, respectively, with literacy. Moreover, the functional coupling of the VWFA and the auditory network decreased with literacy. This study provides novel insights in the mechanisms of reading acquisition and brain plasticity, putting to light the emergence of the VWFA as a bridge between language and vision. Further studies are required to characterize the interplay of pro ﬁciency and age of reading acquisition, and its relevance to models of brain plasticity across lifespan.
Figure 3. Structural Insights Into the Different Functional Domains of ORP/Osh Proteins. A: The OSBP-related domain (ORD) is the common domain of all ORP/Osh proteins. Today, 20 ORD structures with/without ligand, from yeast and humans, have been solved. The architecture of ORDs of all sub-families (described here with the Osh4p-ERG complex, 1ZHZ) is organized around a near-complete b-barrel forming a hydrophobic tunnel that can host one lipid. The pocket entrance is bordered on one side by the EQVSHHPP signature (in orange), and covered on the other side by a lid containing the helix a1. This helix is connected by a long loop to the N-terminal sub- domain that closes the barrel and consists of a two-stranded b-sheet and three a-helices (a2-a4). The C-terminal region is composed of the conserved helices a5-a7 followed by a variable sub-domain of 80 amino-acids (see Figure 7 for details). B: The FFAT motif (in red, consensus sequence ¼ EFFDAxE), mediates the association of ORP/Osh proteins with the N-terminal MSP domain of VAP proteins. Only two structures of complexes between an FFAT-containing peptide derived from OSBP or ORP1, and the MSP domain of partner VAP-A, give atomic details on this interaction. The motif adopts an extended b-strand like conformation, and binds across MSP VAP-A near the b-strands C, D1, E and F. The FFAT motif is docked into a hydrophobic (in grey) and basic (in blue) cleft. C: Despite low sequence similarity, PH domains of ORP/Osh proteins share a common architecture composed of seven b-strands forming two anti-parallel b-sheets that are surrounded by a C-terminal a-helix. It is likely that the presence of a sulphate ion in the crystal structure of PH Osh3p , in interaction with the conserved residues R242, Y255, and R265, corresponds to the 4-phosphate in IP 3 , thus defining the binding site (see (Tong et al., 2013)). In
adult brain (Niu et al., 2011; Brian R. White et al., 2009) and the neonatal brain (Brian R White et al., 2012).
fMRI in animals
The application of fMRI in the animal brain has remained scarce, because a high intensity magnetic field is required in order to obtain sufficient SNR and spatial resolution for small animal imaging (Benveniste & Blackband, 2002; Jonckers et al., 2011); until recent demonstrations, functionalconnectivity-MRI (fcMRI) has been limited to either the rat brain (Bifone, Gozzi, & Schwarz, 2010; B. B. Biswal & Kannurpatti, 2009; Hutchison, Mirsattari, Jones, Gati, & Leung, 2010; Kalthoff, Seehafer, Po, Wiedermann, & Hoehn, 2011; Kannurpatti, Biswal, Kim, & Rosen, 2008; Magnuson, Majeed, & Keilholz, 2010; Pawela et al., 2008, 2009, 2010; van Meer, van der Marel, Otte, Berkelbach van der Sprenkel, & Dijkhuizen, 2010; F. Zhao, Zhao, Zhou, Wu, & Hu, 2008) or the monkey brain (Moeller, Nallasamy, Tsao, & Freiwald, 2009; Shmuel & Leopold, 2008; Teichert, Grinband, Hirsch, & Ferrera, 2010; Vincent et al., 2007). Extending these studies to a well-established and widely used species in clinical research, such as the mouse, would permit the use of this tool in multiple robust disease models. In this context, a recent effort was made to establish a comparison of resting-state functional maps between the mouse and the rat (Jonckers et al., 2011).
The current study
The hypothesis tested here is that the structural pathways reviewed above are dynamically operational during cognitive language tasks. If such is the case, the dynamic functional ac- tivity elicited during a word-production task should reveal statistical dependencies between brain regions. Based on our past research, we were particularly interested in identifying re- gions that were simultaneously and similarly active during the task (Dubarry et al 2017). Given this goal, our exploration purposely avoided exploring metrics that involve delays be- tween regions. Instead of focusing on a particular frequency band, as was done there, here we explored separately the similarity offunctional activity between pairs of brain structures in the β band (12–30 Hz) and the γ high band (70–150 Hz) on a trial-by-trial basis. Electrophys-
The present work builds on these studies to extend the concept to functionalconnectivity. The importance is twofold: (1) structural con- nectomes are related to the underlying neuroimaging data through diffusion processes captured by the Stejskal-Tanner equation ( Pestilli et al., 2014; Stejskal and Tanner, 1965 ); to make the equivalent link for functionalconnectivity is more difﬁcult, for a large part due to our insuf ﬁcient understanding of how local neuro-electric and –chemical processes organize themselves across multiple scales in space and time. An ensemble approach, based on statistical validation methods, is perhaps an alternative for accurate network identiﬁcation. (2) To date, we understand that no single connectome mapping method will be optimal in all situations ( Takemura et al., 2016 ) for this reason, statistical approaches will become fundamental in identifying connectomes with high degree of sensitivity and speci ﬁcity ( Zalesky et al., 2016 ). A growing literature focuses on the generative models of the human connectome that yield synthetic networks combining geometric and topological fac- tors in order to better understand the human connectome ( Betzel et al., 2016 ), which could be further used for the functionalconnectivity. Functional, effective and ensemble connectivity
2.2 Imaging Protocol
The MR protocol was carried out with a 3T whole-body system (Siemens, Erlangen, Germany) at the Center for Magnetic Resonance Research (CENIR), Brain and Spine Institute (ICM), Paris. The functional images were acquired by T2*-weighted fast echo planar imaging (flip angle = 90°, echo time = 30 ms, repetition time = 2.26 s) from 45 interleaved axial slices (gap = 0.3 mm) with a 3 × 3 × 3 mm 3 voxel size for the resting state. The resting-state fMRI experiment consisted of one 8-minute run in which participants were asked to relax with their eyes closed, without falling asleep. Each run consisted of 200 EPI volumes. A high-resolution structural volume was also acquired using a T1-weighted 3D magnetization prepared rapid gradient echo (MP-RAGE) sequence (160 sagittal images; thickness 1 mm; FOV 256 × 256 mm 2 ; matrix size 256 × 256).
complex and deformations is called atlas construction – . They capture the common anatomical characteristics and the morphological variability of the population respectively.
Deformations are usually defined as single diffeomorphisms of the entire ambient space which are smooth invertible transformations with smooth inverse. This kind of deformation preserves the anatomical organization of the components of the template complex, namely they can not intersect, fold or shear. Moreover, deformations are defined locally and they can vary across different areas of the ambient space. This makes it possible to capture the variations in relative position between separate structures. However, using a single diffeomorphism, one implicitly assumes that the relative position between structures in contact with each other or, in practice, close to each other, does not change across subjects. This implies that a particular fiber bundle of the neural circuits should integrate the same areas of the cortical surface and basal ganglia across the whole population. This assumption precludes the study of changes in structuralconnectivity which could be caused by an abnormal brain development. In Fig.1, we present a toy example composed of a template complex and a subject shape complex characterized by a different structuralconnectivity. A single diffeomorphism could not put into correspondence all structures and capture the differences in structuralconnectivity. Structuralconnectivity analysis is usually based on the parti- tion of the cortical surface and sub-cortical nuclei in consistent parcels across subjects . Every parcel is considered as a node of a graph and the number of streamlines connecting two nodes (or other quantities such as the projected Frac- tional Anisotropy) represents the weighted edge. Variability in structuralconnectivity across subjects can be analysed in each parcel independently or with indexes and methods from the complex network theory , . In both cases, the analysis highly depends on the chosen parcellation scheme and it does not take into consideration the morphological variability of grey and white matter structures.
How can the gap between the in vivo exploration of function and fine neuroanatomical description of structure be filled? Complex network theory, a new field of theoretical studies that combines graph theory (structure) and complex systems (dynamics), provides neurobiologists with a frame- work to interpret structure–function relationships in neu- ronal networks. Therefore, in this review, we first introduce basic notions of network topology andconnectivity to pro- vide shared common definitions to the different areas of expertise. Next, we review applications of complex network theory to neurobiological questions, in particular, by ana- lyzing structure–function relationships in the field of corti- cal development. We propose that network development provides an interesting and unique environment to dissect how microcircuits are organized to produce function. Be- cause different functional microcircuits tend to develop sequentially, network development offers experimentalists successive temporal windows to observe the impact of indi- vidual microcircuits as they develop and give rise to different network dynamics. The application of graph theoretical concepts to these sequential periods allows one to link the structure and function of each microcircuit throughout de- velopment. Because, in many aspects, immature networks prefigure the end wiring map of adult circuits, such analyses should also ultimately reveal information about the final organization of mature neuronal networks.
results indicate that random ordering eventually converges to similar results obtained from AMD ordering but it requires a larger sample size.
b) Non-stationarity in rs-fMRI: In this work, the pre- cision matrix, inverse of covariance is used to describe the statistical characteristics of the fMRI signal under the as- sumption of stationarity. In other words, this captures the steady-state contribution to the connectivity. There is evidence that in addition to this contribution, neural networks fluctuate from one brain state to another. Hence, future work should study non-stationarity in more detail. However, anatomical connectivity reflects a wiring, static over the duration of an experiment, and is thus most likely linked to steady-state functionalconnectivity. A promising future direction is the combined acquisition of rs-fMRI and electrophysiological data EEG that have temporal resolution of milliseconds and can reliably detect profound changes in brain states, such as sleep, task related and so on. Nevertheless, our proposed method highlights relationships between structure and function that could potentially change due to brain plasticity and devel- opment. In this perspective of long-term changes in brain networks that affect both structure and function significantly, modeling of short-term non-stationarity becomes less critical. Future studies should investigate changes in this anatomo- functional relationship in disease and relate them to existing knowledge and neuroscientific evidence.