The subcortical and neurochemical organization of the Ventral and Dorsal Attention Networks

Attention is a core cognitive function that filters and selects behaviourally relevant information in the environment. The cortical mapping of attentional systems identified two segregated networks that mediate stimulus-driven and goal-driven processes, the Ventral and the Dorsal Attention Networks (VAN, DAN). Deep brain electrophysiological recordings, behavioral data from phylogenetic distant species, and observations from human brain pathologies challenge purely corticocentric models. Here, we used advanced methods of functional alignment applied to resting-state functional connectivity analyses to map the subcortical architecture of the Ventral and Dorsal Attention Networks. Our investigations revealed the involvement of the pulvinar, the superior colliculi, the head of caudate nuclei, and a cluster of brainstem nuclei relevant to both networks. These nuclei are densely connected structural network hubs, as revealed by diffusion-weighted imaging tractography. Their projections establish interrelations with the acetylcholine nicotinic receptor as well as dopamine and serotonin transporters, as demonstrated in a spatial correlation analysis with a normative atlas of neurotransmitter systems. This convergence of functional, structural, and neurochemical evidence provides a comprehensive framework to understand the neural basis of attention across different species and brain diseases

White matter microstructure of attentional networks predicts attention and consciousness functional interactions

Attention is considered as one of the pre-requisites of conscious perception. Phasic alerting and exogenous orienting improve conscious perception of near-threshold information through segregated brain networks. Using a multimodal neuroimaging approach, combining data from functional MRI (fMRI) and diffusion-weighted imaging (DWI), we investigated the influence of white matter properties of the ventral branch of superior longitudinal fasciculus (SLF III) in functional interactions between attentional systems and conscious perception. Results revealed that (1) reduced integrity of the left hemisphere SLF III was predictive of the neural interactions observed between exogenous orienting and conscious perception, and (2) increased integrity of the left hemisphere SLF III was predictive of the neural interactions observed between phasic alerting and conscious perception. Our results combining fMRI and DWI data demonstrate that structural properties of the white matter organization determine attentional modulations over conscious perception.ABC was supported by a Ramón y Cajal fellowship (RYC-2011-09320) and research project PSI2014-58681-P from the Spanish Ministry of Economy and Competitiveness (MINECO). PMP-A was supported by a Ramón y Cajal fellowship (RYC-2014-15440), and grants PSI2015-65696 and SEV-2015-049 from the MINECO. MTdS received funding from the ‘Agence Nationale de la Recherche’ (Grant number ANR-13-JSV4-0001-01) and “Investissements d’avenir” ANR-10-IAIHU-06. PB received funding from the ‘Agence Nationale de la Recherche’ (Grant number R16139DD)

The impact of early and late literacy on the functional connectivity of vision and

Introduction: Learning to read leads to functional and structural changes in the cortical regions related to vision and language. The visual word-form area (VWFA) is though to play a key role in the interaction between these two systems (Dehaene et al. 2015). For instance, the VWFA is activated not only from bottom-up during reading but also in a top-down manner during speech listening without visual stimulation (Dehaene et al. 2010). The objective of this study was twofolded: how literacy acquisition affects four intrinsic functional connectivity networks related to vision and language (a dorsal language [DLN], a bilateral auditory [AN], a low-level [LLVN] and a high-level visual [HLVN] networks); and to explore the role of the VWFA as an interface between high-level vision and language functions. Methods: Independent component analysis (ICA) was applied to functional magnetic resonance imaging data from 40 adult participants with variable levels of literacy (illiterate, late literate and early literate). The four functional connectivity networks were compared across groups using dual-regression (Filippini et al. 2009). In addition, we directly explored the functional connectivity between the VWFA and each of the studied networks. Finally, the strengh of connectivity between the VWFA and each network was compared across groups and correlated with individual reading fluency scores. Results: ICA produced 40 networks, and spatial crosscorrelation was used to identify the four networks of interest. Literacy was positively correlated with increased connectivity within the four networks. A major difference separating early literate from illiterate and late literate subjects was found. The connectivity between the VWFA and the DLN increased with literacy. Conversely, the strength of connectivity between the VWFA and the HLVN correlated negatively with literacy. Finally, , the HLVN-VWFA connectivity was negatively correlated with reading scores while the connectivity between the DLN-VWFA was positively correlated with reading scores. Discussion:Literacy has a strong influence on the visual and language functional networks. Literacy modifies the VWFA connectivity, by making it functionally closer to the language system, and more distinct from other associative visual areas that do not contribute to the reading process. The current results suggest that early acquisition of literacy plays a critical role for the tuning of the functional brain architecture. References: -Dehaene S et al. Nat Rev Neurosci.(2015)16:234 244 -Dehaene S et al. Science.(2010)330:1359–1364 -Filippini N et al. PNAS.(2009)106, 7209–7214Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Compressed representation of brain genetic transcription

The architecture of the brain is too complex to be intuitively surveyable without the use of compressed representations that project its variation into a compact, navigable space. The task is especially challenging with high-dimensional data, such as gene expression, where the joint complexity of anatomical and transcriptional patterns demands maximum compression. Established practice is to use standard principal component analysis (PCA), whose computational felicity is offset by limited expressivity, especially at great compression ratios. Employing whole-brain, voxel-wise Allen Brain Atlas transcription data, here we systematically compare compressed representations based on the most widely supported linear and non-linear methods-PCA, kernel PCA, non-negative matrix factorization (NMF), t-stochastic neighbour embedding (t-SNE), uniform manifold approximation and projection (UMAP), and deep auto-encoding-quantifying reconstruction fidelity, anatomical coherence, and predictive utility with respect to signalling, microstructural, and metabolic targets. We show that deep auto-encoders yield superior representations across all metrics of performance and target domains, supporting their use as the reference standard for representing transcription patterns in the human brain.Comment: 21 pages, 5 main figures, 1 supplementary figur

A framework for focal and connectomic mapping of transiently disrupted brain function

The distributed nature of the neural substrate, and the difficulty of establishing necessity from correlative data, combine to render the mapping of brain function a far harder task than it seems. Methods capable of combining connective anatomical information with focal disruption of function are needed to disambiguate local from global neural dependence, and critical from merely coincidental activity. Here we present a comprehensive framework for focal and connective spatial inference based on sparse disruptive data, and demonstrate its application in the context of transient direct electrical stimulation of the human medial frontal wall during the pre-surgical evaluation of patients with focal epilepsy. Our framework formalizes voxel-wise mass-univariate inference on sparsely sampled data within the statistical parametric mapping framework, encompassing the analysis of distributed maps defined by any criterion of connectivity. Applied to the medial frontal wall, this transient dysconnectome approach reveals marked discrepancies between local and distributed associations of major categories of motor and sensory behaviour, revealing differentiation by remote connectivity to which purely local analysis is blind. Our framework enables disruptive mapping of the human brain based on sparsely sampled data with minimal spatial assumptions, good statistical efficiency, flexible model formulation, and explicit comparison of local and distributed effects

Frontotemporal networks and behavioral symptoms in primary progressive aphasia

OBJECTIVE: To determine if behavioral symptoms in patients with primary progressive aphasia (PPA) were associated with degeneration of a ventral frontotemporal network. METHODS: We used diffusion tensor imaging tractography to quantify abnormalities of the uncinate fasciculus that connects the anterior temporal lobe and the ventrolateral frontal cortex. Two additional ventral tracts were studied: the inferior fronto-occipital fasciculus and the inferior longitudinal fasciculus. We also measured cortical thickness of anterior temporal and orbitofrontal regions interconnected by these tracts. Thirty-three patients with PPA and 26 healthy controls were recruited. RESULTS: In keeping with the PPA diagnosis, behavioral symptoms were distinctly less prominent than the language deficits. Although all 3 tracts had structural pathology as determined by tractography, significant correlations with scores on the Frontal Behavioral Inventory were found only for the uncinate fasciculus. Cortical atrophy of the orbitofrontal and anterior temporal lobe cortex was also correlated with these scores. CONCLUSIONS: Our findings indicate that damage to a frontotemporal network mediated by the uncinate fasciculus may underlie the emergence of behavioral symptoms in patients with PPA

Occipital Intralobar fasciculi : a description, through tractography, of three forgotten tracts

Diffusion MRI paired with tractography has facilitated a non-invasive exploration of manyassociation, projection, and commissuralfiber tracts. However, there is still a scarcity ofresearch studies related to intralobar associationfibers. The Dejerines’(two of the mostnotable neurologists of 19thcentury France) gave an in-depth description of the intralobarfibers of the occipital lobe. Unfortunately, their exquisite work has since been sparsely citedin the modern literature. This work gives a modern description of many of the occipitalintralobar lobefibers described by the Dejerines. We perform a virtual dissection andreconstruct the tracts using diffusion MRI tractography. The dissection is guided by theDejerines’treatise,Anatomie des Centres Nerveux. As an accompaniment to this article, weprovided a French-to-English translation of the treatise portion concerningfive intra-occipitaltracts, namely: the stratum calcarinum, the stratum proprium cunei, the vertical occipitalfasciculus of Wernicke, the transverse fasciculus of the cuneus and the transverse fasciculusof the lingual lobule of Vialet. It was possible to reconstruct all but one of these tracts.For completeness, the recently described sledge runner fasciculus, although not one of theDejerines’tracts, was identified and successfully reconstructed.peer-reviewe

Latent disconnectome prediction of long-term cognitive-behavioural symptoms in stroke

Stroke significantly impacts the quality of life. However, the long-term cognitive evolution in stroke is poorly predictable at the individual level. There is an urgent need to better predict long-term symptoms based on acute clinical neuroimaging data. Previous works have demonstrated a strong relationship between the location of white matter disconnections and clinical symptoms. However, rendering the entire space of possible disconnection-deficit associations optimally surveyable will allow for a systematic association between brain disconnections and cognitive-behavioural measures at the individual level. Here we present the most comprehensive framework, a composite morphospace of white matter disconnections (disconnectome) to predict neuropsychological scores 1 year after stroke. Linking the latent disconnectome morphospace to neuropsychological outcomes yields biological insights that are available as the first comprehensive atlas of disconnectome-deficit relations across 86 scores-a Neuropsychological White Matter Atlas. Our novel predictive framework, the Disconnectome Symptoms Discoverer, achieved better predictivity performances than six other models, including functional disconnection, lesion topology and volume modelling. Out-of-sample prediction derived from this atlas presented a mean absolute error below 20% and allowed personalize neuropsychological predictions. Prediction on an external cohort achieved an R2 = 0.201 for semantic fluency. In addition, training and testing were replicated on two external cohorts achieving an R2 = 0.18 for visuospatial performance. This framework is available as an interactive web application (http://disconnectomestudio.bcblab.com) to provide the foundations for a new and practical approach to modelling cognition in stroke. We hope our atlas and web application will help to reduce the burden of cognitive deficits on patients, their families and wider society while also helping to tailor future personalized treatment programmes and discover new targets for treatments. We expect our framework's range of assessments and predictive power to increase even further through future crowdsourcing

On the neural origin of pseudoneglect: EEG-correlates of shifts in line bisection performance with manipulation of line length

Healthy participants tend to show systematic biases in spatial attention, usually to the left. However, these biases can shift rightward as a result of a number of experimental manipulations. Using electroencephalography (EEG) and a computerized line bisection task, here we investigated for the first time the neural correlates of changes in spatial attention bias induced by line-length (the so-called line-length effect). In accordance with previous studies, an overall systematic left bias (pseudoneglect) was present during long line but not during short line bisection performance. This effect of line-length on behavioral bias was associated with stronger right parieto-occipital responses to long as compared to short lines in an early time window (100–200 ms) post-stimulus onset. This early differential activation to long as compared to short lines was task-independent (present even in a non-spatial control task not requiring line bisection), suggesting that it reflects a reflexive attentional response to long lines. This was corroborated by further analyses source-localizing the line-length effect to the right temporo-parietal junction (TPJ) and revealing a positive correlation between the strength of this effect and the magnitude by which long lines (relative to short lines) drive a behavioral left bias across individuals. Therefore, stimulus-driven left bisection bias was associated with increased right hemispheric engagement of areas of the ventral attention network. This further substantiates that this network plays a key role in the genesis of spatial bias, and suggests that post-stimulus TPJ-activity at early information processing stages (around the latency of the N1 component) contributes to the left bias