scholarly journals Functional segregation of the human basal forebrain using resting state neuroimaging

2017 ◽  
Author(s):  
Ross D. Markello ◽  
R. Nathan Spreng ◽  
Wen-Ming Luh ◽  
Adam K. Anderson ◽  
Eve De Rosa
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Basal Forebrain ◽  
Nucleus Basalis ◽  
Functional Networks ◽  
List Type ◽  
Functional Connections ◽  
Diagonal Band ◽  
Hippocampal Complex

AbstractThe basal forebrain (BF) is poised to play an important neuromodulatory role in brain re-gions important to cognition due to its broad projections and complex neurochemistry. While significant in vivo work has been done to elaborate BF function in nonhuman rodents and primates, comparatively limited work has examined the in vivo function of the human BF. In the current study we used multi-echo resting state functional magnetic resonance imaging (rs-fMRI) from 100 young adults (18-34 years) to assess the potential segregation of human BF nuclei as well as their associated projections. Bottom-up clustering of voxel-wise functional connectivity maps yielded adjacent functional clusters within the BF that closely aligned with the distinct, hypothesized nuclei important to cognition: the nucleus basalis of Meynert (NBM) and the me-dial septum/diagonal band of Broca (MS/DB). Examining their separate functional connections, the NBM and MS/DB revealed distinct projection patterns, suggesting a conservation of nuclei-specific functional connectivity with homologous regions known to be anatomically innervated by the BF. Specifically, the NBM demonstrated coupling with a widespread cortical network as well as the amygdala, whereas the MS/DB revealed coupling with a more circumscribed net-work, including the orbitofrontal cortex and hippocampal complex. Collectively, these in vivo rs-fMRI data demonstrate that the human BF nuclei support functional networks distinct as-pects of resting-state functional networks, suggesting the human BF may be a neuromodulatory hub important for orchestrating network dynamics.HighlightsThe basal forebrain NBM and the MS/DB support two distinct functional networksFunctional networks closely overlap with known anatomical basal forebrainBasal forebrain networks are distinct from known resting-state functional networks

scholarly journals Characterization of young and old adult brains: An EEG functional connectivity analysis

2018 ◽  
Author(s):  
Bahar Moezzi ◽  
Latha Madhuri Pratti ◽  
Brenton Hordacre ◽  
Lynton Graetz ◽  
Carolyn Berryman ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Older Age ◽  
Support Vector ◽  
Functional Networks ◽  
Svm Classifier ◽  
Age Group ◽  
Beta Band ◽  
Machine Learning Classification ◽  
Functional Connections

Brain connectivity studies have reported that functional networks change with older age. We aim to (1) investigate whether electroencephalography (EEG) data can be used to distinguish between individual functional networks of young and old adults; and (2) identify the functional connections that contribute to this classification. Two eyes-open resting-state EEG recording sessions with 64 electrodes for each of 22 younger adults (19-37 years) and 22 older adults (63-85 years) were conducted. For each session, imaginary coherence matrices in theta, alpha, beta and gamma bands were computed. A range of machine learning classification methods were utilized to distinguish younger and older adult brains. A support vector machine (SVM) classifier was 94% accurate in classifying the brains by age group. We report decreased functional connectivity with older age in theta, alpha and gamma bands, and increased connectivity with older age in beta band. Most connections involving frontal, temporal, and parietal electrodes, and approximately two-thirds of connections involving occipital electrodes, showed decreased connectivity with older age. Just over half of the connections involving central electrodes showed increased connectivity with older age. Functional connections showing decreased strength with older age had significantly longer electrode-to-electrode distance than those that increased with older age. Most of the connections used by the classifier to distinguish participants by age group belonged to the alpha band. Findings suggest a decrease in connectivity in key networks and frequency bands associated with attention and awareness, and an increase in connectivity of the sensorimotor functional networks with ageing during a resting state.

scholarly journals Lateral Asymmetry in Weekly Reproducibility of Resting State Amygdala Functional Connectivity

2018 ◽  
Author(s):  
Alina Tetereva ◽  
Vladislav Balaev ◽  
Sergey Kartashov ◽  
Vadim Ushakov ◽  
Alexey Ivanitsky ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Default Mode Network ◽  
Resting State ◽  
Right Hemisphere ◽  
List Type ◽  
Post Traumatic Stress ◽  
Lateral Asymmetry ◽  
Default Mode ◽  
Functional Connections ◽  
The Right

AbstractAbnormal functional connectivity of the amygdala with several other brain regions has been observed in patients with higher anxiety or post-traumatic stress disorder, both in a resting state and threatening conditions. However, findings on the specific connections of the amygdala might be varied due to temporal and individual fluctuations in the resting state functional connectivity (rsFC) of the amygdala and its lateral asymmetry, as well as possible variability in anxiety among healthy subjects. We studied reproducibility of rsFC data for the right and left amygdala, obtained by functional magnetic resonance imaging twice in a one-week interval in 20 healthy volunteers with low to moderate anxiety. We found resting-state amygdala network, which included not only areas involved in the emotion circuit, but regions of the default mode network (DMN) associated with memory and other brain areas involved in motor inhibition and emotion suppression. The amygdala network was stable in time and within subjects, but between-session reproducibility was asymmetrical for the right and left amygdala rsFC. The right amygdala had more significant connections with DMN regions and the right ventrolateral prefrontal cortex. The rsFC values of the right amygdala were more sustained across the week than the left amygdala rsFC. Our results support a hypothesis of functional lateralization of the amygdala. The left amygdala is more responsible for the conscious processing of threats, which may produce more variable rsFC; the right amygdala rsFC is more stable due to its greater engagement in continuous automatic evaluation of stimuli.HighlightsAmygdala resting state network included areas of emotion circuit and motor controlDuring rest amygdala was functionally connected with areas of default mode networkFunctional connectivity of the right amygdala was more sustained across the weekFunctional connections of amygdala network were more stable in the right hemisphere

scholarly journals Tonic resting-state hubness supports high-frequency activity defined verbal-memory encoding network in epilepsy

2019 ◽  
Author(s):  
Chaitanya Ganne ◽  
Walter Hinds ◽  
James Kragel ◽  
Xiaosong He ◽  
Noah Sideman ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Verbal Memory ◽  
Resting State ◽  
High Frequency ◽  
Cognitive Networks ◽  
Gamma Activity ◽  
List Type ◽  
Healthy Controls ◽  
Memory Encoding ◽  
Memory Network

AbstractHigh-frequency gamma activity of verbal-memory encoding using invasive-electroencephalogram coupled has laid the foundation for numerous studies testing the integrity of memory in diseased populations. Yet, the functional connectivity characteristics of networks subserving these HFA-memory linkages remains uncertain. By integrating this electrophysiological biomarker of memory encoding from IEEG with resting-state BOLD fluctuations, we estimated the segregation and hubness of HFA-memory regions in drug-resistant epilepsy patients and matched healthy controls. HFA-memory regions express distinctly different hubness compared to neighboring regions in health and in epilepsy, and this hubness was more relevant than segregation in predicting verbal memory encoding. The HFA-memory network comprised regions from both the cognitive control and primary processing networks, validating that effective verbal-memory encoding requires multiple functions, and is not dominated by a central cognitive core. Our results demonstrate a tonic intrinsic set of functional connectivity, which provides the necessary conditions for effective, phasic, task-dependent memory encoding.HighlightsHigh frequency memory activity in IEEG corresponds to specific BOLD changes in resting-state data.HFA-memory regions had lower hubness relative to control brain nodes in both epilepsy patients and healthy controls.HFA-memory network displayed hubness and participation (interaction) values distinct from other cognitive networks.HFA-memory network shared regional membership and interacted with other cognitive networks for successful memory encoding.HFA-memory network hubness predicted both concurrent task (phasic) and baseline (tonic) verbal-memory encoding success.

scholarly journals In vivo visuotopic brain mapping with manganese-enhanced MRI and resting-state functional connectivity MRI

NeuroImage ◽  
2014 ◽  
Vol 90 ◽  
pp. 235-245 ◽  
Author(s):  
Kevin C. Chan ◽  
Shu-Juan Fan ◽  
Russell W. Chan ◽  
Joe S. Cheng ◽  
Iris Y. Zhou ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Brain Mapping ◽  
Resting State ◽  
Resting State Functional Connectivity ◽  
Enhanced Mri ◽  
Functional Connectivity Mri ◽  
Manganese Enhanced Mri

scholarly journals Functional Segregation of Human Brain Networks Across the Lifespan: An Exploratory Analysis of Static and Dynamic Resting-State Functional Connectivity

2020 ◽  
Vol 14 ◽  
Author(s):  
Benjamin M. Rosenberg ◽  
Eva Mennigen ◽  
Martin M. Monti ◽  
Roselinde H. Kaiser
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Dwell Time ◽  
Brain Networks ◽  
Older Age ◽  
Functional Network ◽  
Functional Networks ◽  
Resting State Functional Connectivity ◽  
Network State ◽  
High Connectivity

Prior research has shown that during development, there is increased segregation between, and increased integration within, prototypical resting-state functional brain networks. Functional networks are typically defined by static functional connectivity over extended periods of rest. However, little is known about how time-varying properties of functional networks change with age. Likewise, a comparison of standard approaches to functional connectivity may provide a nuanced view of how network integration and segregation are reflected across the lifespan. Therefore, this exploratory study evaluated common approaches to static and dynamic functional network connectivity in a publicly available dataset of subjects ranging from 8 to 75 years of age. Analyses evaluated relationships between age and static resting-state functional connectivity, variability (standard deviation) of connectivity, and mean dwell time of functional network states defined by recurring patterns of whole-brain connectivity. Results showed that older age was associated with decreased static connectivity between nodes of different canonical networks, particularly between the visual system and nodes in other networks. Age was not significantly related to variability of connectivity. Mean dwell time of a network state reflecting high connectivity between visual regions decreased with age, but older age was also associated with increased mean dwell time of a network state reflecting high connectivity within and between canonical sensorimotor and visual networks. Results support a model of increased network segregation over the lifespan and also highlight potential pathways of top-down regulation among networks.

scholarly journals Impact of anaesthesia on static and dynamic functional connectivity in mice

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.

scholarly journals Low-cost embedded system for optical imaging of intrinsic signals

2019 ◽  
Vol 65 (6 Nov-Dec) ◽  
pp. 651
Author(s):  
E. Guevara ◽  
M. Miranda-Morales ◽  
K. Hernández-Vidales ◽  
M. Atzori ◽  
F.J. González
Keyword(s):  
Functional Connectivity ◽  
Optical Imaging ◽  
Resting State ◽  
Low Cost ◽  
Raspberry Pi ◽  
Murine Models ◽  
Intrinsic Signals ◽  
Multiple Imaging ◽  
Concept Evaluation

This paper describes the proof-of-concept evaluation of a low-cost imaging system for obtaining functional connectivity maps of in vivo murine models. This non-contact system is based on the Raspberry Pi 3 and its V2 camera and offers a method for obtaining resting-state images of brain activity without the use of extrinsic contrast agents. The system was fully characterized in terms of dark signal, linearity, sensor noise resolution and spatial frequency response. One mouse was observed in vivo and functional connectivity maps were obtained by combining resting-state analysis and optical intrinsic signals imaging. Intra-mouse variations in functional connectivity remain consistent across multiple imaging sessions. In principle, inexpensive optical imaging of intrinsic signals allows the study of the mechanisms underlying human brain disorders in well-controlled murine models.

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