Exploring the Rôle of White Matter Connectivity in Cortex Maturation

De nombreuses études ont mis en évidence un processus de maturation séquentiel pour différentes régions cérébrales, notamment la matière grise corticale et ses connections de matière blanche (WM). Cependant, les mécanismes à l’origine du processus de maturation cérébrale, ainsi que la relation entre la maturation des ces deux structures demeurent mal compris. Dans cette étude, nous utilisons l’imagerie par résonance magnétique (IRM) pour obtenir simultanément des informations reflétant la maturation microstructurelle des différentes structures cérébrales, ainsi que la mise en place des connections de matière blanche durant le développement précoce. Pour ce faire, nous employons deux mesures d’IRM bien établies reflétant les changements microstructurels liés à la maturation, soit le coefficient de diffusion apparent (ADC) et le temps de relaxation T (T1), ainsi que des algorithmes mathématiques permettant de reconstruire les groupes d’axones reliant différentes régions de matière grise (connectome). Ce travail démontre une interdépendance entre la maturation des structures de matière grise corticale et le développement de l’arbre connectionel de matière blanche sous-jacent. En effet, nous mettons en évidence un degré de maturation analogue entre les régions corticales, ainsi que les groupes de fibres axonales associées durant les stades précoces de la maturation. Un tel lien est également observé entre deux régions corticales reliées par un groupe de fibres. Sur la base de ces observations, cette étude propose un modèle mathématique et informatique simple parlant pour un rôle clé des structures de matière blanche dans le relai des signaux maturationnels depuis un stimuli externe via les structures primaires sensorielles et atteignant finalement les structures cérébrales corticales d’ordre plus complexes. -- The maturation of the cortical gray matter (GM) and white matter (WM) are described as sequential processes following multiple, but distinct rules. However, neither the mecha- nisms driving brain maturation processes, nor the relationship between GM and WM matu- ration are well understood. Here we use connectomics and two MRI measures reflecting maturation related changes in cerebral microstructure, namely the Apparent Diffusion Coef- ficient (ADC) and the T1 relaxation time (T1), to study brain development. We report that the advancement of GM and WM maturation are inter-related and depend on the underlying brain connectivity architecture. Particularly, GM regions and their incident WM connections show corresponding maturation levels, which is also observed for GM regions connected through a WM tract. Based on these observations, we propose a simple computational model supporting a key role for the connectome in propagating maturation signals sequen- tially from external stimuli, through primary sensory structures to higher order functional cortices

The Berlin Brain–Computer Interface: Non-Medical Uses of BCI Technology

Brain–computer interfacing (BCI) is a steadily growing area of research. While initially BCI research was focused on applications for paralyzed patients, increasingly more alternative applications in healthy human subjects are proposed and investigated. In particular, monitoring of mental states and decoding of covert user states have seen a strong rise of interest. Here, we present some examples of such novel applications which provide evidence for the promising potential of BCI technology for non-medical uses. Furthermore, we discuss distinct methodological improvements required to bring non-medical applications of BCI technology to a diversity of layperson target groups, e.g., ease of use, minimal training, general usability, short control latencies

Langerhans cell histiocytosis with initial central nervous system presentation as a mimic of neurosarcoidosis

We report the case of a 58-year-old Caucasian woman who presented with a subacute cerebellar syndrome accompanied by disturbance of the hypothalamic–pituitary axis and was diagnosed with isolated neurosarcoidosis based on radiological findings including typically located cerebral lesions (infratentorial and pituitary stalk). Due to persistent clinical and radiological disease activity during several years despite escalation of immunosuppressive treatment, the diagnosis was reevaluated, and a transsphenoidal biopsy of a lesion at the pituitary stalk was performed revealing Langerhans cell histiocytosis. In this case, we discuss the different steps leading to the diagnostic error, as well as the presence of red flags, which should have led to an earlier diagnostic reevaluation

Dynamic spatiotemporal patterns of brain connectivity reorganize across development

Late human development is characterized by the maturation of high-level functional processes, which rely on reshaping of white matter connections, as well as synaptic density. However, the relationship between the whole-brain dynamics and the underlying white matter networks in neurodevelopment is largely unknown. In this study, we focused on how the structural connectome shapes the emerging dynamics of cerebral development between the ages of 6 and 33 years, using functional and diffusion magnetic resonance imaging combined into a spatiotemporal connectivity framework. We defined two new measures of brain dynamics, namely the system diversity and the spatiotemporal diversity, which quantify the level of integration/segregation between functional systems and the level of temporal self-similarity of the functional patterns of brain dynamics, respectively. We observed a global increase in system diversity and a global decrease and local refinement in spatiotemporal diversity values with age. In support of these findings, we further found an increase in the usage of long-range and inter-system white matter connectivity and a decrease in the usage of short-range connectivity with age. These findings suggest that dynamic functional patterns in the brain progressively become more integrative and temporally self-similar with age. These functional changes are supported by a greater involvement of long-range and inter-system axonal pathways.AUTHOR SUMMARYMaturation in human development is represented by changes in both functional dynamics and structural connectivity in the human brain. By constructing a spatiotemporal connectome for a cohort of 81 subjects ranging from 6 to 33 years of age, we demonstrate how these changes can be studied in a unified framework. We do so by defining two new measures of brain dynamics, namely the spatiotemporal diversity, mapping the level of temporal self-similarity of the functional patterns of brain dynamics, and system diversity, quantifying the level of integration/segregation between functional systems. These measures, we argue, represent a novel way of looking at brain dynamics constraints by structural connectivity. Using these measures, we show that dynamic functional patterns in the brain progressively become more integrative and temporally self-similar with age

Connection properties.

<p>A: Weighted groupwise connectivity matrix including 450 connections present for > = 50% of the subjects. Color coding indicates the number of streamlines. ROIs are ordered per hemisphere (right: top, left: bottom) according to the atlas by Shi et al. [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0177466#pone.0177466.ref043" target="_blank">43</a>]. Fro: frontal; Li: limbic; Oc: occipital; Pa: parietal; Te: temporal cortex and G: basal ganglia. B: Length distribution of the 450 connections. C: Connection repartition between the different groups. <i>SUB</i> in black, <i>PRIM</i> in dark gray, <i>SEC</i> in gray, <i>TER</i> in light gray.</p

Histograms and brain surface plots for experimental ADC [10⁻⁶ mm/s²] values (left) and modelled maturation scores (right, four key RWSs) in ROI groups.

<p>Top: ROIs; bottom: incident connections. Highest Pearson’s correlations are indicated with a star for the corresponding RWS (and colour).</p

Modelled maturation scores projected on a standard brain surface for four representative simulated time points (RWSs): ROI in blue (top), connections in red (bottom).

<p>Box: Experimental data (ADC [10⁻⁶ mm/s²]) on brain surface and Pearson’s correlations with modelled maturation scores for RWS 1–10. The RWS with the best correlation is represented by a star.</p

ROIs and incident connections.

<p>A: Distribution of ADC [10⁻⁶ mm/s²] (top) and T1 [ms] (bottom) values for each ROI group and the incident connections. B: Scatterplot for ADC (top) and T1 (bottom) in ROIs and their incident connections, for all ROIs. Pearson’s correlation: ADC <i>r</i> = 0.67, p < 10⁻¹⁰; T1 <i>r</i> = 0.84, p < 10⁻¹⁰. Groups: <i>SUB</i> (n = 12) in black (diamonds), <i>PRIM</i> (n = 12) in dark gray (circles), <i>SEC</i> (n = 34) in gray (stars), <i>TER</i> (n = 32) in light gray (squares). C: Correlation between mean ADC [10⁻⁶ mm/s²] (top) and T1 [ms] (bottom) of GM ROI pairs and average ADC value along the connecting WM tracts (ADC: Pearson <i>r</i> = 0.54, p < 10⁻¹⁰, T1: Pearson <i>r</i> = 0.77, p < 10⁻¹⁰; n = 450).</p