scholarly journals Medial Temporal Lobe Contributions to Resting-State Networks

2021 ◽  
Author(s):  
Sara Seoane ◽  
Cristián Modroño ◽  
José Luis González Mora ◽  
Niels Janssen
Keyword(s):  
Temporal Lobe ◽  
Resting State ◽  
Medial Temporal Lobe ◽  
Brain Regions ◽  
Resting State Networks ◽  
Attention Networks ◽  
Default Mode ◽  
Parahippocampal Cortex ◽  
Human Connectome Project ◽  
Dorsal Attention Network

Abstract The medial temporal lobe (MTL) is a set of interconnected brain regions that have been shown to play a central role in behavior as well as in neurological disease. Recent studies using resting-state functional Magnetic Resonance Imaging (rsfMRI) have attempted to understand the MTL in terms of its functional connectivity with the rest of the brain. However, the exact characterization of the whole-brain networks that co-activate with the MTL as well as how the various sub-regions of the MTL are associated with these networks remains poorly understood. Here we attempted to advance these issues by exploiting the high spatial-resolution 7T rsfMRI dataset from the Human Connectome Project with a data-driven analysis approach that relied on Independent Component Analysis (ICA) restricted to the MTL. We found that four different well-known resting-state networks co-activated with a unique configuration of MTL subcomponents. Specifically, we found that different sections of the parahippocampal cortex were involved in the default mode, visual and dorsal attention networks, sections of the hippocampus in the somatomotor and default mode networks, and the lateral entorhinal cortex in the dorsal attention network. We replicated this set of results in a validation sample. These results provide new insight into how the MTL and its subcomponents contribute to known resting-state networks. The participation of the MTL in an expanded range of resting-state networks requires a rethink of its presumed role in behavior and disease.

scholarly journals Relationship of Depression, Anxiety, and Rumination Scores with EEG Connectivity of Resting State Networks

2021 ◽  
Vol 47 (2) ◽  
pp. 123-127
Author(s):  
A. V. Bocharov ◽  
G. G. Knyazev ◽  
A. N. Savostyanov ◽  
A. E. Saprygin ◽  
E. A. Proshina ◽  
...  
Keyword(s):  
Depressive Symptoms ◽  
Default Mode Network ◽  
Resting State ◽  
Temporal Cortex ◽  
Brain Structures ◽  
Anterior Cingulate ◽  
Resting State Networks ◽  
Attention Networks ◽  
Default Mode ◽  
Seed Method

Abstract The aim of the research was to study the effect of depression, anxiety, and rumination scores on the balance of activity of the default mode network and attention networks revealed in the resting state EEG records. Forty-five healthy volunteers (24 men aged from 18 to 25 years) participated in the resting state EEG recording. The participants filled in the Beck Depression Inventory-II (BDI II), Ruminative Responses Scale, and Eysenck Personality Profiler. The connectivity measures of resting state networks were calculated in EEG data. The networks were detected by the “seed” method. The effects of depressive symptoms, anxiety, and rumination on the connectivity of the networks were analyzed by the regression method. The depressive symptom scores and the rumination scores were correlated with the dominance of the default mode network over attention networks in the right temporal cortex. The depression scores and the anxiety scores were correlated with the dominance of attention networks over the default mode network in the anterior cingulate cortex. It could be suggested that rumination processes are specific for depressive symptoms and are reflected in the dominance of the default mode network in brain structures associated with the processing of emotional introspection. Common to depressive and anxious symptoms is a state of alertness, which is reflected in the dominance of attention networks in brain structures associated with decision-making.

scholarly journals Extracting Reproducible Time-Resolved Resting State Networks using Dynamic Mode Decomposition

2018 ◽  
Author(s):  
James M. Kunert-Graf ◽  
Kristian M. Eschenburg ◽  
David J. Galas ◽  
J. Nathan Kutz ◽  
Swati D. Rane ◽  
...  
Keyword(s):  
Resting State ◽  
Dynamic Properties ◽  
Brain Regions ◽  
Dynamic Mode Decomposition ◽  
Single Subject ◽  
Dynamic Mode ◽  
Resting State Networks ◽  
Time Resolved ◽  
Mode Decomposition ◽  
Human Connectome Project

AbstractResting state networks (RSNs) extracted from functional magnetic resonance imaging (fMRI) scans are believed to reflect the intrinsic organization and network structure of brain regions. Most traditional methods for computing RSNs typically assume these functional networks are static throughout the duration of a scan lasting 5–15 minutes. However, they are known to vary on timescales ranging from seconds to years; in addition, the dynamic properties of RSNs are affected in a wide variety of neurological disorders. Recently, there has been a proliferation of methods for characterizing RSN dynamics, yet it remains a challenge to extract reproducible time-resolved networks. In this paper, we develop a novel method based on dynamic mode decomposition (DMD) to extract networks from short windows of noisy, high-dimensional fMRI data, allowing RSNs from single scans to be resolved robustly at a temporal resolution of seconds. We demonstrate this method on data from 120 individuals from the Human Connectome Project and show that unsupervised clustering of DMD modes discovers RSNs at both the group (gDMD) and the single subject (sDMD) levels. The gDMD modes closely resemble canonical RSNs. Compared to established methods, sDMD modes capture individualized RSN structure that both better resembles the population RSN and better captures subject-level variation. We further leverage this time-resolved sDMD analysis to infer occupancy and transitions among RSNs with high reproducibility. This automated DMD-based method is a powerful tool to characterize spatial and temporal structures of RSNs in individual subjects.

scholarly journals Discordant attributes of structural and functional connectivity in a two-layer multiplex network

2018 ◽  
Author(s):  
Sol Lim ◽  
Filippo Radicchi ◽  
Martijn P van den Heuvel ◽  
Olaf Sporns
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Structural Connectivity ◽  
Brain Regions ◽  
Resting State Networks ◽  
Structural Failure ◽  
Resonance Imaging ◽  
Multiplex Network ◽  
Human Connectome Project ◽  
Magnetic Resonance Imaging Mri

AbstractSeveral studies have suggested that functional connectivity (FC) is constrained by the underlying structural connectivity (SC) and mutually correlated. However, not many studies have focused on differences in the network organization of SC and FC, and on how these differences may inform us about their mutual interaction. To explore this issue, we adopt a multi-layer framework, with SC and FC, constructed using Magnetic Resonance Imaging (MRI) data from the Human Connectome Project, forming a two-layer multiplex network. In particular, we examine whether node strength assortativity within and between the SC and FC layer may confer increased robustness against structural failure. We find that, in general, SC is organized assortatively, indicating brain regions are on average connected to other brain regions with similar node strengths. On the other hand, FC shows disassortative mixing. This discrepancy is apparent also among individual resting-state networks within SC and FC. In addition, these patterns show lateralization, with disassortative mixing within FC subnetworks mainly driven from the left hemisphere. We discuss our findings in the context of robustness to structural failure, and we suggest that discordant and lateralized patterns of associativity in SC and FC may explain laterality of some neurological dysfunctions and recovery.

FC34.4 Temporal lobe interictal discharges affect resting state brain function in “default mode” brain regions

2006 ◽  
Vol 117 ◽  
pp. 1
Author(s):  
H. Laufs ◽  
K. Hamandi ◽  
A. Salek-Haddadi ◽  
A. Kleinschmidt ◽  
J.S. Duncan ◽  
...  
Keyword(s):  
Temporal Lobe ◽  
Resting State ◽  
Brain Function ◽  
Brain Regions ◽  
Default Mode ◽  
Interictal Discharges

2092-P: Impact of Insulin Resistance on Response to a Meal by Salience, Executive Control, and Default Mode Resting State Networks in Humans

Diabetes ◽  
2019 ◽  
Vol 68 (Supplement 1) ◽  
pp. 2092-P
Author(s):  
LETICIA ESPOSITO SEWAYBRICKER ◽  
SUSAN J. MELHORN ◽  
MARY K. ASKREN ◽  
MARY WEBB ◽  
VIDHI TYAGI ◽  
...  
Keyword(s):  
Insulin Resistance ◽  
Executive Control ◽  
Resting State ◽  
Resting State Networks ◽  
Default Mode

Children show adult-like hippocampal pattern separation for familiar but not novel events

2020 ◽  
Author(s):  
Susan L. Benear ◽  
Elizabeth A. Horwath ◽  
Emily Cowan ◽  
M. Catalina Camacho ◽  
Chi Ngo ◽  
...  
Keyword(s):  
Episodic Memory ◽  
Temporal Lobe ◽  
Medial Temporal Lobe ◽  
Developmental Change ◽  
Pattern Separation ◽  
Developmental Changes ◽  
Similarity Analysis ◽  
Separation Processes ◽  
Parahippocampal Cortex ◽  
Pattern Similarity

The medial temporal lobe (MTL) undergoes critical developmental change throughout childhood, which aligns with developmental changes in episodic memory. We used representational similarity analysis to compare neural pattern similarity for children and adults in hippocampus and parahippocampal cortex during naturalistic viewing of clips from the same movie or different movies. Some movies were more familiar to participants than others. Neural pattern similarity was generally lower for clips from the same movie, indicating that related content taxes pattern separation-like processes. However, children showed this effect only for movies with which they were familiar, whereas adults showed the effect consistently. These data suggest that children need more exposures to stimuli in order to show mature pattern separation processes.

scholarly journals Neurophysiology (fMRI)

2015 ◽  
Vol 42 (S1) ◽  
pp. S38-S38
Author(s):  
ST Lang ◽  
B Goodyear ◽  
J Kelly ◽  
P Federico
Keyword(s):  
Cognitive Functioning ◽  
Default Mode Network ◽  
Resting State ◽  
Attentional Control ◽  
Brain Tumours ◽  
Resting State Fmri ◽  
Fmri Data ◽  
Resting State Networks ◽  
Default Mode ◽  
Eloquent Cortex

Background: Resting state functional MRI (rs-fMRI) provides many advantages to task-based fMRI in neurosurgical populations, foremost of which is the lack of the need to perform a task. Many networks can be identified by rs-fMRI in a single period of scanning. Despite the advantages, there is a paucity of literature on rs-fMRI in neurosurgical populations. Methods: Eight patients with tumours near areas traditionally considered as eloquent cortex participated in a five minute rs-fMRI scan. Resting-state fMRI data underwent Independent Component Analysis (ICA) using the Multivariate Exploratory Linear Optimized Decomposition into Independent Components (MELODIC) toolbox in FSL. Resting state networks (RSNs) were identified on a visual basis. Results: Several RSNs, including language (N=7), sensorimotor (N=7), visual (N=7), default mode network (N=8) and frontoparietal attentional control (n=7) networks were readily identifiable using ICA of rs-fMRI data. Conclusion: These pilot data suggest that ICA applied to rs-fMRI data can be used to identify motor and language networks in patients with brain tumours. We have also shown that RSNs associated with cognitive functioning, including the default mode network and the frontoparietal attentional control network can be identified in individual subjects with brain tumours. While preliminary, this suggests that rs-fMRI may be used pre-operatively to localize areas of cortex important for higher order cognitive functioning.

Functional MRI in Anesthesia and Resting-State Networks

2018 ◽  
pp. 174-207
Author(s):  
Nasim Mortazavi ◽  
Cecile Staquet ◽  
Audrey Vanhaudenhuyse ◽  
Andrea Soddu ◽  
Marie-Elisabeth Faymonville ◽  
...  
Keyword(s):  
Resting State ◽  
Current Knowledge ◽  
Brain Regions ◽  
Current Evidence ◽  
Brain State ◽  
Resting State Networks ◽  
Anesthetic Agents ◽  
Complex Interactions ◽  
Specific Effects ◽  
Technological Advances

This chapter reviews current knowledge of the effects of hypnotic anesthetic agents on brain resting-state networks (RSNs) that sustain consciousness. Although full exploration of the networks under anesthesia is not yet available, current evidence indicates that anesthetic agents with hypnotic properties dose-dependently modulate RSN functioning. Each anesthetic agent has specific effects that are not uniform within a given network and probably correlate with the specific clinical features observed when one agent or another is used. Observations made on RSNs during anesthesia are supplementary arguments to link the networks with specific aspects of consciousness and connectedness to the environment and to confirm their physiological functions. The precise link between observations made on RSNs during anesthesia and known biochemical targets of anesthetic agents, or their effects on systems that regulate the sleep–wake cycle, is not established yet. PET studies using radiolabeled probes that specifically target a neurotransmission system offer insights into the links. New technological advances and modes of functional data analysis, such as Granger causality and dynamic causal modeling, will help in obtaining a more in-depth exploration of the complex interactions between brain regions, their modulation by anesthesia, and their role in information processing by the brain. Effects of hypnosis on RSNs also have been studied. The hypnotic state is useful for performing surgical procedures and explorations without general anesthesia. The hypnotic state is associated with specific changes in the activity of RSNs that confirm hypnosis as a specific brain state, different from normal wakeful consciousness and anesthetic states.

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