scholarly journals Core and Matrix Thalamic Sub-Populations Relate to Spatio-Temporal Cortical Connectivity Gradients

2020 ◽  
Author(s):  
Eli Müller ◽  
Brandon Munn ◽  
Luke J. Hearne ◽  
Jared B. Smith ◽  
Ben Fulcher ◽  
...  
Keyword(s):  
Cerebral Cortex ◽  
Resting State ◽  
Calcium Binding ◽  
Functional Organization ◽  
Functional Neuroimaging ◽  
Resting State Fmri ◽  
Relative Distribution ◽  
Cortical Connectivity ◽  
Functional Connections ◽  
Low Dimensional

AbstractRecent neuroimaging experiments have defined low-dimensional gradients of functional connectivity in the cerebral cortex that subserve a spectrum of capacities that span from sensation to cognition. Despite well-known anatomical connections to the cortex, the subcortical areas that support cortical functional organization have been relatively overlooked. One such structure is the thalamus, which maintains extensive anatomical and functional connections with the cerebral cortex across the cortical mantle. The thalamus has a heterogeneous cytoarchitecture, with at least two distinct cell classes that send differential projections to the cortex: granular-projecting ‘Core’ cells and supragranular-projecting ‘Matrix’ cells. Here we use high-resolution 7T resting-state fMRI data and the relative amount of two calcium-binding proteins, parvalbumin and calbindin, to infer the relative distribution of these two cell-types (Core and Matrix, respectively) in the thalamus. First, we demonstrate that thalamocortical connectivity recapitulates large-scale, low-dimensional connectivity gradients within the cerebral cortex. Next, we show that diffusely-projecting Matrix regions preferentially correlate with cortical regions with longer intrinsic fMRI timescales. We then show that the Core–Matrix architecture of the thalamus is important for understanding network topology in a manner that supports dynamic integration of signals distributed across the brain. Finally, we replicate our main results in a distinct 3T resting-state fMRI dataset. Linking molecular and functional neuroimaging data, our findings highlight the importance of the thalamic organization for understanding low-dimensional gradients of cortical connectivity.

scholarly journals Interpreting models interpreting brain dynamics

2021 ◽  
Author(s):  
Md Mahfuzur Rahman ◽  
Usman Mahmood ◽  
Noah Lewis ◽  
Harshvardhan Gazula ◽  
Alex Fedorov ◽  
...  
Keyword(s):  
Deep Learning ◽  
Resting State ◽  
Functional Neuroimaging ◽  
Resting State Fmri ◽  
High Dimensional ◽  
Brain Dynamics ◽  
Small Data ◽  
Imaging Data ◽  
Low Dimensional ◽  
Spatial Locations

Abstract Brain dynamics are highly complex and yet hold the key to understanding brain function and dysfunction. The dynamics captured by resting-state functional magnetic resonance imaging data are noisy, high-dimensional, and not readily interpretable. The typical approach of reducing this data to low-dimensional features and focusing on the most predictive features comes with strong assumptions and can miss essential aspects of the underlying dynamics. In contrast, introspection of discriminatively trained deep learning models may uncover disorder-relevant elements of the signal at the level of individual time points and spatial locations. Yet, the difficulty of reliable training on high-dimensional low sample size datasets and the unclear relevance of the resulting predictive markers prevent the widespread use of deep learning in functional neuroimaging. In this work, we introduce a deep learning framework to learn from high-dimensional dynamical data while maintaining stable, ecologically valid interpretations. Results successfully demonstrate that the proposed framework enables learning the dynamics of resting-state fMRI directly from small data and capturing compact, stable interpretations of features predictive of function and dysfunction.

scholarly journals Interpreting models interpreting brain dynamics

2021 ◽  
Author(s):  
Md Mahfuzur Rahman ◽  
Usman Mahmood ◽  
Noah Lewis ◽  
Harshvardhan Gazula ◽  
Alex Fedorov ◽  
...  
Keyword(s):  
Deep Learning ◽  
Resting State ◽  
Functional Neuroimaging ◽  
Resting State Fmri ◽  
High Dimensional ◽  
Brain Dynamics ◽  
Small Data ◽  
Imaging Data ◽  
Low Dimensional ◽  
Spatial Locations

Abstract Brain dynamics are highly complex and yet hold the key to understanding brain function and dysfunction. The dynamics captured by resting-state functional magnetic resonance imaging data are noisy, high-dimensional, and not readily interpretable. The typical approach of reducing this data to low-dimensional features and focusing on the most predictive features comes with strong assumptions and can miss essential aspects of the underlying dynamics. In contrast, introspection of discriminatively trained deep learning models may uncover disorder-relevant elements of the signal at the level of individual time points and spatial locations. Yet, the difficulty of reliable training on high-dimensional low sample size datasets and the unclear relevance of the resulting predictive markers prevent the widespread use of deep learning in functional neuroimaging. In this work, we introduce a deep learning framework to learn from high-dimensional dynamical data while maintaining stable, ecologically valid interpretations. Results successfully demonstrate that the proposed framework enables learning the dynamics of resting-state fMRI directly from small data and capturing compact, stable interpretations of features predictive of function and dysfunction.

scholarly journals The unbalanced reorganization of weaker functional connections induces the altered brain network topology in schizophrenia

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Rossana Mastrandrea ◽  
Fabrizio Piras ◽  
Andrea Gabrielli ◽  
Nerisa Banaj ◽  
Guido Caldarelli ◽  
...  
Keyword(s):  
Resting State ◽  
Functional Organization ◽  
Functional Integration ◽  
Brain Network ◽  
Modular Organization ◽  
Node Degree ◽  
Imaging Data ◽  
Brain Disorder ◽  
Functional Connections ◽  
The Brain

AbstractNetwork neuroscience shed some light on the functional and structural modifications occurring to the brain associated with the phenomenology of schizophrenia. In particular, resting-state functional networks have helped our understanding of the illness by highlighting the global and local alterations within the cerebral organization. We investigated the robustness of the brain functional architecture in 44 medicated schizophrenic patients and 40 healthy comparators through an advanced network analysis of resting-state functional magnetic resonance imaging data. The networks in patients showed more resistance to disconnection than in healthy controls, with an evident discrepancy between the two groups in the node degree distribution computed along a percolation process. Despite a substantial similarity of the basal functional organization between the two groups, the expected hierarchy of healthy brains' modular organization is crumbled in schizophrenia, showing a peculiar arrangement of the functional connections, characterized by several topologically equivalent backbones. Thus, the manifold nature of the functional organization’s basal scheme, together with its altered hierarchical modularity, may be crucial in the pathogenesis of schizophrenia. This result fits the disconnection hypothesis that describes schizophrenia as a brain disorder characterized by an abnormal functional integration among brain regions.

scholarly journals Cortical selectivity driven by connectivity: Innate connectivity patterns of the visual word form area

2019 ◽  
Author(s):  
Jin Li ◽  
David E. Osher ◽  
Heather A. Hansen ◽  
Zeynep M. Saygin
Keyword(s):  
Resting State ◽  
Functional Organization ◽  
Resting State Fmri ◽  
Visual Word ◽  
Functional Specialization ◽  
Word Form ◽  
Visual Word Form Area ◽  
Connectivity Patterns ◽  
Set Up ◽  
Language Network

AbstractWhat determines the functional organization of cortex? One hypothesis is that innate connectivity patterns set up a scaffold upon which functional specialization can later take place. We tested this hypothesis by asking whether the visual word form area (VWFA), an experience-driven region, was already connected to proto language networks in neonates scanned within one week of birth. With resting-state fMRI, we found that neonates showed adult-like functional connectivity, and observed that i) language regions connected more strongly with the putative VWFA than other adjacent ventral visual regions that also show foveal bias, and ii) the VWFA connected more strongly with frontotemporal language regions than with regions adjacent to these language regions. These data suggest that the location of the VWFA is earmarked at birth due to its connectivity with the language network, providing evidence that innate connectivity instructs the later refinement of cortex.

scholarly journals Frontal Lobe Epilepsy Alters Functional Connections Within the Brain's Motor Network: A Resting-State fMRI Study

2014 ◽  
Vol 4 (2) ◽  
pp. 91-99 ◽  
Author(s):  
Kristine Elizabeth Woodward ◽  
Ismael Gaxiola-Valdez ◽  
Bradley Gordon Goodyear ◽  
Paolo Federico
Keyword(s):  
Frontal Lobe ◽  
Resting State ◽  
Resting State Fmri ◽  
Frontal Lobe Epilepsy ◽  
Functional Connections ◽  
Motor Network ◽  
Fmri Study

scholarly journals Model testing for distinctive functional connectivity gradients with resting-state fMRI data

2018 ◽  
Author(s):  
Jonathan F. O’Rawe ◽  
Jaime S. Ide ◽  
Hoi-Chung Leung
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Functional Organization ◽  
Region Of Interest ◽  
Resting State Fmri ◽  
Model Testing ◽  
Fmri Data ◽  
Extensive Literature ◽  
Topographic Organization ◽  
Lateral Organization

AbstractIn accordance with the concept of topographic organization of neuroanatomical structures, there is an increased interest in estimating and delineating continuous changes in the functional connectivity patterns across neighboring voxels within a region of interest using resting-state fMRI data. Fundamental to this functional connectivity gradient analysis is the assumption that the functional organization is stable and uniform across the region of interest. To evaluate this assumption, we developed a model testing procedure to arbitrate between overlapping, shifted, or different topographic connectivity gradients across subdivisions of a structure. We tested the procedure using the striatum, a subcortical structure consisting of the caudate nucleus and putamen, in which an extensive literature, primarily from rodents and non-human primates, suggest to have a shared topographic organization of a single diagonal gradient. We found, across multiple resting state fMRI data samples of different spatial resolutions in humans, and one macaque resting state fMRI data sample, that the models with different functional connectivity gradients across the caudate and putamen was the preferred model. The model selection procedure was validated in control conditions of checkerboard subdivisions, demonstrating the expected overlapping gradient. More specifically, while we replicated the diagonal organization of the functional connectivity gradients in both the caudate and putamen, our analysis also revealed a medial-lateral organization within the caudate. Not surprisingly, performing the same analysis assuming a unitary gradient obfuscates the medial-lateral organization of the caudate, producing only a diagonal gradient. These findings demonstrate the importance of testing basic assumptions and evaluating interpretations across species. The significance of differential topographic gradients across the putamen and caudate and the medial-lateral gradient of the caudate in humans should be tested in future studies.

scholarly journals Dissociable Cerebellar-Prefrontal Networks Underlying Executive Function: Evidence from the Human Connectome Project

2018 ◽  
Author(s):  
Joseph M. Orr ◽  
Trevor B. Jackson ◽  
Michael J. Imburgio ◽  
Jessica A. Bernard
Keyword(s):  
Executive Function ◽  
Functional Data ◽  
Resting State ◽  
Functional Organization ◽  
Preliminary Evidence ◽  
Functional Connections ◽  
Human Connectome Project ◽  
Crus I ◽  
Resting State Connectivity

AbstractTo date, investigations of executive function (EF) have focused on the prefrontal cortex (PFC), and prominent theories of EF are framed with respect to this brain region. Multiple theories describe a hierarchical functional organization for the lateral PFC. However, recent evidence has indicated that the cerebellum (CB) also plays a role in EF. Posterior CB regions (Crus I & II) show structural and functional connections with the PFC, and CB networks are associated with individual differences in EF in healthy adults. However, it is unclear whether the cerebellum shows a similar functional gradient as does the PFC. Here, we investigated high-resolution resting-state data from 225 participants in the Human Connectome Project. We compared resting-state connectivity from posterior cerebellar ROIs, and examined functional data from several tasks that activate the lateral PFC. Demonstrating preliminary evidence for parallel PFC and CB gradients, Crus I was functionally connected with rostrolateral PFC, Crus II with middle and ventral PFC, and Lobule VI with posterior PFC. Contrary to previous work, the activation of the task thought to activate rostrolateral PFC resembled the connectivity maps of Crus II, not Crus I; similarly, the activation of the task thought to activate middle PFC resembled the connectivity maps of Crus I, not Crus II. Nevertheless, there was evidence for dissociable CB-PFC networks. Further work is necessary to understand the functional role of these networks.

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