Patterns of resting state connectivity in human primary visual cortical areas: A 7T fMRI study

Neocortex is striking in its laminar architecture. Tracer studies have uncovered anatomical connectivity among laminae, but the functional connectivity between laminar compartments is still largely unknown. Such functional connectivity can be discerned through spontaneous neural correlations during rest. Previous work demonstrated a robust pattern of mesoscopic resting-state connectivity in macaque primary visual cortex (V1) through interlaminar cross-frequency coupling. Here we investigated whether this pattern generalizes to other cortical areas by comparing resting-state laminar connectivity between V1 and the supplementary eye field (SEF), a frontal area lacking a granular layer 4 (L4). Local field potentials (LFPs) were recorded with linear microelectrode arrays from all laminae of granular V1 and agranular SEF while monkeys rested in darkness. We found substantial differences in the relationship between the amplitude of gamma-band (>30 Hz) LFP and the phase of alpha-band (7–14 Hz) LFP between these areas. In V1, gamma amplitudes in L2/3 and L5 were coupled with alpha-band LFP phase in L5, as previously described. In contrast, in SEF phase-amplitude coupling was prominent within L3 and much weaker across layers. These results suggest that laminar interactions in agranular SEF are unlike those in granular V1. Thus the intrinsic functional connectivity of the cortical microcircuit does not seem to generalize across cortical areas.

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Palm-to-finger cortical functional interactions in primary somatosensory cortex: a 7T fMRI study

10.1101/2020.09.07.286062 ◽

2020 ◽

Author(s):

Michel Akselrod ◽

Roberto Martuzzi ◽

Wietske van der Zwaag ◽

Olaf Blanke ◽

Andrea Serino

Keyword(s):

Somatosensory Cortex ◽

Resting State ◽

Primary Somatosensory Cortex ◽

Spatial Proximity ◽

Single Subject ◽

Functional Interactions ◽

Somatotopic Organization ◽

Pattern Similarity ◽

Resting State Connectivity ◽

7T Fmri

ABSTRACTMany studies focused on the cortical representations of fingers, while the palm is relatively neglected despite its importance for hand function. Here, we investigated palm representation (PR) and its interactions with finger representations (FRs) in primary somatosensory cortex (S1). Few studies in humans suggested that PR is located medially with respect to FRs in S1, yet to date, no study directly quantified the somatotopic organization of PR and the five FRs. Importantly, the relationship between the somatotopic organization and the cortical functional interactions between PR and FRs remains largely unexplored. Using 7T fMRI, we mapped PR and the five FRs at the single subject level. First, we analyzed the cortical distance between PR and FRs to determine their somatotopic organization. Results show that the PR was located medially with respect to D5. Second, we tested whether the observed cortical distances would predict palm-finger functional interactions. Using three complementary measures of functional interactions (co-activations, pattern similarity and resting-state connectivity), we show that palm-finger functional interactions were not determined by their somatotopic organization, that is, there was no gradient moving from D5 to D1, except for resting-state connectivity, which was predicted by the somatotopy. Instead, we show that the representational geometry of palm-finger functional interactions reflected the physical structure of the hand. Collectively, our findings suggest that the spatial proximity between topographically organized neuronal populations do not necessarily predicts their functional interactions, rather the structure of the sensory space (e.g. the hand shape) better predicts the observed functional interactions.

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Assessing uncertainty in connective field estimations from resting state fMRI activity

10.1101/2020.11.02.365452 ◽

2020 ◽

Author(s):

Azzurra Invernizzi ◽

Nicolas Gravel ◽

Koen V. Haak ◽

Remco J. Renken ◽

Frans W. Cornelissen

Keyword(s):

Visual Field ◽

Functional Connectivity ◽

Resting State ◽

Resting State Fmri ◽

Fmri Data ◽

List Type ◽

Spatial Integration ◽

Cortical Areas ◽

Local Functional ◽

Visual Cortical

AbstractConnective Field (CF) modeling estimates the local spatial integration between signals in distinct cortical visual field areas. As we have shown previously using 7T data, CF can reveal the visuotopic organization of visual cortical areas even when applied to BOLD activity recorded in the absence of external stimulation. This indicates that CF modeling can be used to evaluate cortical processing in participants in which the visual input may be compromised. Furthermore, by using Bayesian CF modelling it is possible to estimate the co-variability of the parameter estimates and therefore, apply CF modeling to single cases. However, no previous studies evaluated the (Bayesian) CF model using 3T resting-state fMRI data, although this is important since 3T scanners are much more abundant and more often used in clinical research than 7T ones. In this study, we investigate whether it is possible to obtain meaningful CF estimates from 3T resting state (RS) fMRI data. To do so, we applied the standard and Bayesian CF modeling approaches on two RS scans interleaved by the acquisition of visual stimulation in 12 healthy participants.Our results show that both approaches reveal good agreement between RS- and visual field (VF)-based maps. Moreover, the 3T observations were similar to those previously reported at 7T. In addition, to quantify the uncertainty associated with each estimate in both RS and VF data, we applied our Bayesian CF framework to provide the underlying marginal distribution of the CF parameters. Finally, we show how an additional CF parameter, beta, can be used as a data-driven threshold on the RS data to further improve CF estimates. We conclude that Bayesian CF modeling can characterize local functional connectivity between visual cortical areas from RS data at 3T. In particular, we expect the ability to assess parameter uncertainty in individual participants will be important for future clinical studies.HighlightsLocal functional connectivity between visual cortical areas can be estimated from RS-fMRI data at 3T using both standard CF and Bayesian CF modelling.Bayesian CF modelling quantifies the model uncertainty associated with each CF parameter on RS and VF data, important in particular for future studies on clinical populations.3T observations were qualitatively similar to those previously reported at 7T.

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Altered resting-state connectivity in subjects at ultra-high risk for psychosis: an fMRI study

Behavioral and Brain Functions ◽

10.1186/1744-9081-6-58 ◽

2010 ◽

Vol 6 (1) ◽

pp. 58 ◽

Cited By ~ 82

Author(s):

Geumsook Shim ◽

Jungsu S Oh ◽

Wi Jung ◽

Joon Jang ◽

Chi-Hoon Choi ◽

...

Keyword(s):

High Risk ◽

Resting State ◽

Fmri Study ◽

Ultra High Risk ◽

Resting State Connectivity

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Assessing Uncertainty and Reliability of Connective Field Estimations From Resting State fMRI Activity at 3T

Frontiers in Neuroscience ◽

10.3389/fnins.2021.625309 ◽

2021 ◽

Vol 15 ◽

Author(s):

Azzurra Invernizzi ◽

Nicolas Gravel ◽

Koen V. Haak ◽

Remco J. Renken ◽

Frans W. Cornelissen

Keyword(s):

Visual Field ◽

Resting State ◽

Resting State Fmri ◽

Fmri Data ◽

Parameter Estimates ◽

Spatial Integration ◽

External Stimulation ◽

Cortical Areas ◽

Local Functional ◽

Visual Cortical

Connective Field (CF) modeling estimates the local spatial integration between signals in distinct cortical visual field areas. As we have shown previously using 7T data, CF can reveal the visuotopic organization of visual cortical areas even when applied to BOLD activity recorded in the absence of external stimulation. This indicates that CF modeling can be used to evaluate cortical processing in participants in which the visual input may be compromised. Furthermore, by using Bayesian CF modeling it is possible to estimate the co-variability of the parameter estimates and therefore, apply CF modeling to single cases. However, no previous studies evaluated the (Bayesian) CF model using 3T resting-state fMRI data. This is important since 3T scanners are much more abundant and more often used in clinical research compared to 7T scanners. Therefore in this study, we investigate whether it is possible to obtain meaningful CF estimates from 3T resting state (RS) fMRI data. To do so, we applied the standard and Bayesian CF modeling approaches on two RS scans, which were separated by the acquisition of visual field mapping data in 12 healthy participants. Our results show good agreement between RS- and visual field (VF)- based maps using either the standard or Bayesian CF approach. In addition to quantify the uncertainty associated with each estimate in both RS and VF data, we applied our Bayesian CF framework to provide the underlying marginal distribution of the CF parameters. Finally, we show how an additional CF parameter, beta, can be used as a data-driven threshold on the RS data to further improve CF estimates. We conclude that Bayesian CF modeling can characterize local functional connectivity between visual cortical areas from RS data at 3T. Moreover, observations obtained using 3T scanners were qualitatively similar to those reported for 7T. In particular, we expect the ability to assess parameter uncertainty in individual participants will be important for future clinical studies.

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