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Brainstem atrophy in focal epilepsy destabilizes brainstem-brain interactions: Preliminary findings.
BACKGROUND: MR Imaging has shown atrophy in brainstem regions that were linked to autonomic dysfunction in epilepsy patients. The brainstem projects to and modulates the activation state of several wide-spread cortical/subcortical regions. The goal was to investigate 1. Impact of brainstem atrophy on gray matter connectivity of cortical/subcortical structures and autonomic control. 2. Impact on the modulation of cortical/subcortical functional connectivity.
METHODS: 11 controls and 18 patients with non-lesional focal epilepsy (FE) underwent heart rate variability (HRV) measurements and a 3 T MRI (T1 in all subjects, task-free fMRI in 7 controls/ 12 FE). The brainstem was extracted, and atrophy assessed using deformation-based-morphometry. The age-corrected z-scores of the mean Jacobian determinants were extracted from 71 5x5x5 mm grids placed in brainstem regions associated with autonomic function. Cortical and non-brainstem subcortical gray matter atrophy was assessed with voxel-based-morphometry and mean age corrected z-scores of the modulated gray matter volumes extracted from 380 cortical/subcortical rois. The profile similarity index was used to characterize the impact of brainstem atrophy on gray matter connectivity. The fMRI was preprocessed in SPM12/Conn17 and the BOLD signal extracted from 398 ROIs (16 brainstem). A dynamic task-free analysis approach was used to identify activation states. Connectivity HRV relationship were assessed with Spearman rank correlations.
RESULTS: HRV was negatively correlated with reduced brainstem right hippocampus/parahippocampus gray matter connectivity in controls (p \u3c .05, FDR) and reduced brainstem to right parietal cortex, lingual gyrus, left hippocampus/amygdala, parahippocampus, temporal pole, and bilateral anterior thalamus connectivity in FE (p \u3c .05, FDR). Dynamic task-free fMRI analysis identified 22 states. The strength of the functional brainstem/cortical connectivity of state 15 was negatively associated with HRV (r = -0.5, p = .03) and positively with decreased brainstem-cortical (0.49, p = .03) gray matter connectivity.
CONCLUSION: The findings of this small pilot study suggest that impaired brainstem-cortex gray matter connectivity in FE negatively affects the brainstem\u27s ability to control cortical activation
The gray matter structural connectome and its relationship to alcohol relapse: Reconnecting for recovery.
Gray matter (GM) atrophy associated with alcohol use disorders (AUD) affects predominantly the frontal lobes. Less is known how frontal lobe GM loss affects GM loss in other regions and how it influences drinking behavior or relapse after treatment. The profile similarity index (PSI) combined with graph analysis allows to assess how GM loss in one region affects GM loss in regions connected to it, ie, GM connectivity. The PSI was used to describe the pattern of GM connectivity in 21 light drinkers (LDs) and in 54 individuals with AUD (ALC) early in abstinence. Effects of abstinence and relapse were determined in a subgroup of 36 participants after 3 months. Compared with LD, GM losses within the extended brain reward system (eBRS) at 1-month abstinence were similar between abstainers (ABST) and relapsers (REL), but REL had also GM losses outside the eBRS. Lower GM connectivities in ventro-striatal/hypothalamic and dorsolateral prefrontal regions and thalami were present in both ABST and REL. Between-networks connectivity loss of the eBRS in ABST was confined to prefrontal regions. About 3 months later, the GM volume and connectivity losses had resolved in ABST, and insula connectivity was increased compared with LD. GM losses and GM connectivity losses in REL were unchanged. Overall, prolonged abstinence was associated with a normalization of within-eBRS connectivity and a reconnection of eBRS structures with other networks. The re-formation of structural connectivities within and across networks appears critical for cognitive-behavioral functioning related to the capacity to maintain abstinence after outpatient treatment
Learning and comparing functional connectomes across subjects
Functional connectomes capture brain interactions via synchronized
fluctuations in the functional magnetic resonance imaging signal. If measured
during rest, they map the intrinsic functional architecture of the brain. With
task-driven experiments they represent integration mechanisms between
specialized brain areas. Analyzing their variability across subjects and
conditions can reveal markers of brain pathologies and mechanisms underlying
cognition. Methods of estimating functional connectomes from the imaging signal
have undergone rapid developments and the literature is full of diverse
strategies for comparing them. This review aims to clarify links across
functional-connectivity methods as well as to expose different steps to perform
a group study of functional connectomes
Self-organization without conservation: Are neuronal avalanches generically critical?
Recent experiments on cortical neural networks have revealed the existence of
well-defined avalanches of electrical activity. Such avalanches have been
claimed to be generically scale-invariant -- i.e. power-law distributed -- with
many exciting implications in Neuroscience. Recently, a self-organized model
has been proposed by Levina, Herrmann and Geisel to justify such an empirical
finding. Given that (i) neural dynamics is dissipative and (ii) there is a
loading mechanism "charging" progressively the background synaptic strength,
this model/dynamics is very similar in spirit to forest-fire and earthquake
models, archetypical examples of non-conserving self-organization, which have
been recently shown to lack true criticality. Here we show that cortical neural
networks obeying (i) and (ii) are not generically critical; unless parameters
are fine tuned, their dynamics is either sub- or super-critical, even if the
pseudo-critical region is relatively broad. This conclusion seems to be in
agreement with the most recent experimental observations. The main implication
of our work is that, if future experimental research on cortical networks were
to support that truly critical avalanches are the norm and not the exception,
then one should look for more elaborate (adaptive/evolutionary) explanations,
beyond simple self-organization, to account for this.Comment: 28 pages, 11 figures, regular pape
Dynamic fluctuations coincide with periods of high and low modularity in resting-state functional brain networks
We investigate the relationship of resting-state fMRI functional connectivity
estimated over long periods of time with time-varying functional connectivity
estimated over shorter time intervals. We show that using Pearson's correlation
to estimate functional connectivity implies that the range of fluctuations of
functional connections over short time scales is subject to statistical
constraints imposed by their connectivity strength over longer scales. We
present a method for estimating time-varying functional connectivity that is
designed to mitigate this issue and allows us to identify episodes where
functional connections are unexpectedly strong or weak. We apply this method to
data recorded from participants, and show that the number of
unexpectedly strong/weak connections fluctuates over time, and that these
variations coincide with intermittent periods of high and low modularity in
time-varying functional connectivity. We also find that during periods of
relative quiescence regions associated with default mode network tend to join
communities with attentional, control, and primary sensory systems. In
contrast, during periods where many connections are unexpectedly strong/weak,
default mode regions dissociate and form distinct modules. Finally, we go on to
show that, while all functional connections can at times manifest stronger
(more positively correlated) or weaker (more negatively correlated) than
expected, a small number of connections, mostly within the visual and
somatomotor networks, do so a disproportional number of times. Our statistical
approach allows the detection of functional connections that fluctuate more or
less than expected based on their long-time averages and may be of use in
future studies characterizing the spatio-temporal patterns of time-varying
functional connectivityComment: 47 Pages, 8 Figures, 4 Supplementary Figure
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