A comprehensive macaque fMRI pipeline and hierarchical atlas

AbstractFunctional neuroimaging research in the non-human primate (NHP) has been advancing at a remarkable rate. The increase in available data establishes a need for robust analysis pipelines designed for NHP neuroimaging and accompanying template spaces to standardize the localization of neuroimaging results. Our group recently developed the NIMH Macaque Template (NMT), a high-resolution population average anatomical template and associated neuroimaging resources, providing researchers with a standard space for macaque neuroimaging (Seidlitz, Sponheim et al., 2018). Here, we release NMT v2, which includes both symmetric and asymmetric templates in stereotaxic orientation, with improvements in spatial contrast, processing efficiency, and segmentation. We also introduce the Cortical Hierarchy Atlas of the Rhesus Macaque (CHARM), a hierarchical parcellation of the macaque cerebral cortex with varying degrees of detail. These tools have been integrated into the neuroimaging analysis software AFNI (Cox, 1996) to provide a comprehensive and robust pipeline for fMRI processing, visualization and analysis of NHP data. AFNI’s new @animal_warper program can be used to efficiently align anatomical scans to the NMT v2 space, and afni_proc.py integrates these results with full fMRI processing using macaque-specific parameters: from motion correction through regression modeling. Taken together, the NMT v2 and AFNI represent an all-in-one package for macaque functional neuroimaging analysis, as demonstrated with available demos for both task and resting state fMRI.HighlightsThe NMT v2, a stereotaxically aligned symmetric macaque template, is introduced.A new atlas (CHARM), defined on NMT v2, parcellates the cortex at six spatial scales.AFNI’s @animal_warper aligns and maps data between monkey anatomicals and templates.AFNI’s afni_proc.py facilitates monkey fMRI analysis with automated scripting and QC.Demos of macaque task and resting state fMRI analysis with these tools are provided.

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Frequency-Domain Resting State fMRI Analysis Demonstrates Language Lateralization in Healthy Controls

10.26226/morressier.5e5e70e235f8e2df5e55a492 ◽

2020 ◽

Author(s):

Syed Rakin Ahmed

Keyword(s):

Frequency Domain ◽

Resting State ◽

Resting State Fmri ◽

Healthy Controls ◽

Language Lateralization ◽

Fmri Analysis

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Resting-state fMRI analysis of Alzheimer's disease progress using sparse dictionary learning

2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC) ◽

10.1109/icsmc.2012.6377868 ◽

2012 ◽

Author(s):

Jeonghyeon Lee ◽

Jong Chul Ye

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Dictionary Learning ◽

Resting State ◽

Resting State Fmri ◽

Disease Progress ◽

Sparse Dictionary Learning ◽

Fmri Analysis

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T131. KNCQ Channel Opener Ezogabine_x000B_as a Treatment for Depression: A Preliminary Resting State fMRI Analysis

Biological Psychiatry ◽

10.1016/j.biopsych.2018.02.467 ◽

2018 ◽

Vol 83 (9) ◽

pp. S179

Author(s):

Aaron Tan ◽

Sara Costi ◽

Laurel Morris ◽

Nicholas Van Dam ◽

James Murrough

Keyword(s):

Resting State ◽

Resting State Fmri ◽

Channel Opener ◽

Fmri Analysis

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Impact of anaesthesia on static and dynamic functional connectivity in mice

10.1101/2021.03.18.436098 ◽

2021 ◽

Author(s):

Tomokazu Tsurugizawa ◽

Daisuke Yoshimaru

Keyword(s):

Functional Connectivity ◽

Functional Mri ◽

Resting State ◽

Low Frequency ◽

Resting State Fmri ◽

Brain Network ◽

List Type ◽

Thalamic Nuclei ◽

Dynamic Functional Connectivity ◽

Subcortical Regions

AbstractA few studies have compared the static functional connectivity between awake and anaesthetized states in rodents by resting-state fMRI. However, impact of anaesthesia on static and dynamic fluctuations in functional connectivity has not been fully understood. Here, we developed a resting-state fMRI protocol to perform awake and anaesthetized functional MRI in the same mice. Static functional connectivity showed a widespread decrease under anaesthesia, such as when under isoflurane or a mixture of isoflurane and medetomidine. Several interhemispheric connections were key connections for anaesthetized condition from awake. Dynamic functional connectivity demonstrates the shift from frequent broad connections across the cortex, the hypothalamus, and the auditory-visual cortex to frequent local connections within the cortex only. Fractional amplitude of low frequency fluctuation in the thalamic nuclei decreased under both anaesthesia. These results indicate that typical anaesthetics for functional MRI alters the spatiotemporal profile of the dynamic brain network in subcortical regions, including the thalamic nuclei and limbic system.HighlightsResting-state fMRI was compared between awake and anaesthetized in the same mice.Anaesthesia induced a widespread decrease of static functional connectivity.Anaesthesia strengthened local connections within the cortex.fALFF in the thalamus was decreased by anaesthesia.

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Hierarchical disruption in the cortex of anesthetized monkeys as a new signature of consciousness loss

10.1101/2020.06.04.133538 ◽

2020 ◽

Author(s):

Camilo Miguel Signorelli ◽

Lynn Uhrig ◽

Morten Kringelbach ◽

Bechir Jarraya ◽

Gustavo Deco

Keyword(s):

Neural Activity ◽

Resting State ◽

Resting State Fmri ◽

Hierarchical Organization ◽

Brain Dynamics ◽

Depth Of Anesthesia ◽

Functional Changes ◽

Cortical Hierarchy ◽

Brain States ◽

Structure Similarity

AbstractAnesthesia induces a reconfiguration of the repertoire of functional brain states leading to a high function-structure similarity. However, it is unclear how these functional changes lead to loss of consciousness. Here we suggest that the mechanism of conscious access is related to a general dynamical rearrangement of the intrinsic hierarchical organization of the cortex. To measure cortical hierarchy, we applied the Intrinsic Ignition analysis to resting-state fMRI data acquired in awake and anesthetized macaques. Our results reveal the existence of spatial and temporal hierarchical differences of neural activity within the macaque cortex, with a strong modulation by the depth of anesthesia and the employed anesthetic agent. Higher values of Intrinsic Ignition correspond to rich and flexible brain dynamics whereas lower values correspond to poor and rigid, structurally driven brain dynamics. Moreover, spatial and temporal hierarchical dimensions are disrupted in a different manner, involving different hierarchical brain networks. All together suggest that disruption of brain hierarchy is a new signature of consciousness loss.

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Interpreting models interpreting brain dynamics

10.21203/rs.3.rs-798060/v1 ◽

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.

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