scholarly journals Intrinsic timescales as an organizational principle of neural processing across the whole rhesus macaque brain

2021 ◽  
Author(s):  
Ana M.G. Manea ◽  
Anna Zilverstand ◽  
Kamil Ugurbil ◽  
Sarah R. Heilbronner ◽  
Jan Zimmermann

Hierarchical temporal dynamics are a fundamental computational property of the brain; however, there are no whole-brain, noninvasive investigations into timescales of neural processing in animal models. To that end, we used the spatial resolution and sensitivity of ultra-high field fMRI to probe timescales across the whole macaque brain. We uncovered within-species consistency between timescales estimated from fMRI and electrophysiology. Crucially, we were not only able to demonstrate that we can replicate existing electrophysiological hierarchies, but we extended these to whole brain topographies. Our results validate the complementary use of hemodynamic and electrophysiological intrinsic timescales, establishing a basis for future translational work. Second, with those results in hand, we were able to show that one facet of the high-dimensional FC topography of any region in the brain is closely related to hierarchical temporal dynamics. We demonstrated that intrinsic timescales are organized along spatial gradients that closely match functional connectivity gradient topographies across the whole brain. We conclude that intrinsic timescales are an unifying organizational principle of neural processing across the whole brain.

2020 ◽  
Vol 117 (36) ◽  
pp. 22522-22531 ◽  
Author(s):  
Mehran Spitmaan ◽  
Hyojung Seo ◽  
Daeyeol Lee ◽  
Alireza Soltani

A long-lasting challenge in neuroscience has been to find a set of principles that could be used to organize the brain into distinct areas with specific functions. Recent studies have proposed the orderly progression in the time constants of neural dynamics as an organizational principle of cortical computations. However, relationships between these timescales and their dependence on response properties of individual neurons are unknown, making it impossible to determine how mechanisms underlying such a computational principle are related to other aspects of neural processing. Here, we developed a comprehensive method to simultaneously estimate multiple timescales in neuronal dynamics and integration of task-relevant signals along with selectivity to those signals. By applying our method to neural and behavioral data during a dynamic decision-making task, we found that most neurons exhibited multiple timescales in their response, which consistently increased from parietal to prefrontal and cingulate cortex. While predicting rates of behavioral adjustments, these timescales were not correlated across individual neurons in any cortical area, resulting in independent parallel hierarchies of timescales. Additionally, none of these timescales depended on selectivity to task-relevant signals. Our results not only suggest the existence of multiple canonical mechanisms for increasing timescales of neural dynamics across cortex but also point to additional mechanisms that allow decorrelation of these timescales to enable more flexibility.


NeuroImage ◽  
2016 ◽  
Vol 124 ◽  
pp. 1143-1148 ◽  
Author(s):  
Christine Lucas Tardif ◽  
Andreas Schäfer ◽  
Robert Trampel ◽  
Arno Villringer ◽  
Robert Turner ◽  
...  

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Morteza Esmaeili ◽  
Jason Stockmann ◽  
Bernhard Strasser ◽  
Nicolas Arango ◽  
Bijaya Thapa ◽  
...  

Abstract Metabolic imaging of the human brain by in-vivo magnetic resonance spectroscopic imaging (MRSI) can non-invasively probe neurochemistry in healthy and disease conditions. MRSI at ultra-high field (≥ 7 T) provides increased sensitivity for fast high-resolution metabolic imaging, but comes with technical challenges due to non-uniform B0 field. Here, we show that an integrated RF-receive/B0-shim (AC/DC) array coil can be used to mitigate 7 T B0 inhomogeneity, which improves spectral quality and metabolite quantification over a whole-brain slab. Our results from simulations, phantoms, healthy and brain tumor human subjects indicate improvements of global B0 homogeneity by 55%, narrower spectral linewidth by 29%, higher signal-to-noise ratio by 31%, more precise metabolite quantification by 22%, and an increase by 21% of the brain volume that can be reliably analyzed. AC/DC shimming provide the highest correlation (R2 = 0.98, P = 0.001) with ground-truth values for metabolite concentration. Clinical translation of AC/DC and MRSI is demonstrated in a patient with mutant-IDH1 glioma where it enables imaging of D-2-hydroxyglutarate oncometabolite with a 2.8-fold increase in contrast-to-noise ratio at higher resolution and more brain coverage compared to previous 7 T studies. Hence, AC/DC technology may help ultra-high field MRSI become more feasible to take advantage of higher signal/contrast-to-noise in clinical applications.


2002 ◽  
Vol 14 (3) ◽  
pp. 521-536 ◽  
Author(s):  
MARK H. JOHNSON ◽  
HANIFE HALIT ◽  
SARAH J. GRICE ◽  
ANNETTE KARMILOFF–SMITH

To date, research involving functional neuroimaging of typical and atypical development has depended on several assumptions about the postnatal maturation of the brain. We consider evidence from multiple levels of analysis that brings into question these underlying assumptions and advance an alternative view. This alternative view, based on an “interactive specialization” approach to postnatal brain development, indicates that there is a need to: obtain data from early in development; focus more on differences in interregional interactions rather than searching for localized, discrete lesions; examine the temporal dynamics of neural processing; and move away from deficits to image tasks in which atypical participants perform as well as typically developing participants.


2021 ◽  
Author(s):  
Robyn L. Miller ◽  
Victor M Vergara ◽  
Vince Calhoun

The most common pipelines for studying time-varying network connectivity in resting state functional magnetic resonance imaging (rs-fMRI) operate at the whole brain level, capturing a small discrete set of 'states' that best represent time-resolved joint measures of connectivity over all network pairs in the brain. This whole-brain hidden Markov model (HMM) approach 'uniformizes' the dynamics over what is typically more than 1000 pairs of networks, forcing each time-resolved high-dimensional observation into its best-matched high-dimensional state. While straightforward and convenient, this HMM simplification obscures functional and temporal nonstationarities that could reveal systematic, informative features of resting state brain dynamics at a more granular scale. We introduce a framework for studying functionally localized dynamics that intrinsically embeds them within a whole-brain HMM frame of reference. The approach is validated in a large rs-fMRI schizophrenia study where it identifies group differences in localized patterns of entropy and dynamics that help explain consistently observed differences between schizophrenia patients and controls in occupancy of whole-brain dFNC states more mechanistically.


2020 ◽  
Vol 20 (11) ◽  
pp. 589
Author(s):  
Emily J. Allen ◽  
Yihan Wu ◽  
J. Benjamin Hutchinson ◽  
Thomas Naselaris ◽  
Kendrick N. Kay

2020 ◽  
Author(s):  
Enrico Schulz ◽  
Anne Stankewitz ◽  
Anderson M Winkler ◽  
Stephanie Irving ◽  
Viktor Witkovský ◽  
...  

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