scholarly journals Individual-specific functional connectivity of the amygdala: A substrate for precision psychiatry

2020 ◽  
Vol 117 (7) ◽  
pp. 3808-3818 ◽  
Author(s):  
Chad M. Sylvester ◽  
Qiongru Yu ◽  
A. Benjamin Srivastava ◽  
Scott Marek ◽  
Annie Zheng ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Psychiatric Symptoms ◽  
The Other ◽  
Fmri Data ◽  
Independent Group ◽  
Network Organization ◽  
Functional Brain ◽  
Psychiatric Illnesses ◽  
Functional Brain Networks ◽  
The Brain

The amygdala is central to the pathophysiology of many psychiatric illnesses. An imprecise understanding of how the amygdala fits into the larger network organization of the human brain, however, limits our ability to create models of dysfunction in individual patients to guide personalized treatment. Therefore, we investigated the position of the amygdala and its functional subdivisions within the network organization of the brain in 10 highly sampled individuals (5 h of fMRI data per person). We characterized three functional subdivisions within the amygdala of each individual. We discovered that one subdivision is preferentially correlated with the default mode network; a second is preferentially correlated with the dorsal attention and fronto-parietal networks; and third subdivision does not have any networks to which it is preferentially correlated relative to the other two subdivisions. All three subdivisions are positively correlated with ventral attention and somatomotor networks and negatively correlated with salience and cingulo-opercular networks. These observations were replicated in an independent group dataset of 120 individuals. We also found substantial across-subject variation in the distribution and magnitude of amygdala functional connectivity with the cerebral cortex that related to individual differences in the stereotactic locations both of amygdala subdivisions and of cortical functional brain networks. Finally, using lag analyses, we found consistent temporal ordering of fMRI signals in the cortex relative to amygdala subdivisions. Altogether, this work provides a detailed framework of amygdala–cortical interactions that can be used as a foundation for models relating aberrations in amygdala connectivity to psychiatric symptoms in individual patients.

scholarly journals Brain network dynamics correlate with personality traits

2019 ◽  
Author(s):  
Aya Kabbara ◽  
Veronique Paban ◽  
Arnaud Weill ◽  
Julien Modolo ◽  
Mahmoud Hassan
Keyword(s):  
Functional Connectivity ◽  
Personality Traits ◽  
Resting State ◽  
Brain Networks ◽  
Neural Substrates ◽  
Brain Network ◽  
Functional Brain ◽  
Human Connectome Project ◽  
Functional Brain Networks ◽  
The Brain

AbstractIntroductionIdentifying the neural substrates underlying the personality traits is a topic of great interest. On the other hand, it is now established that the brain is a dynamic networked system which can be studied using functional connectivity techniques. However, much of the current understanding of personality-related differences in functional connectivity has been obtained through the stationary analysis, which does not capture the complex dynamical properties of brain networks.ObjectiveIn this study, we aimed to evaluate the feasibility of using dynamic network measures to predict personality traits.MethodUsing the EEG/MEG source connectivity method combined with a sliding window approach, dynamic functional brain networks were reconstructed from two datasets: 1) Resting state EEG data acquired from 56 subjects. 2) Resting state MEG data provided from the Human Connectome Project. Then, several dynamic functional connectivity metrics were evaluated.ResultsSimilar observations were obtained by the two modalities (EEG and MEG) according to the neuroticism, which showed a negative correlation with the dynamic variability of resting state brain networks. In particular, a significant relationship between this personality trait and the dynamic variability of the temporal lobe regions was observed. Results also revealed that extraversion and openness are positively correlated with the dynamics of the brain networks.ConclusionThese findings highlight the importance of tracking the dynamics of functional brain networks to improve our understanding about the neural substrates of personality.

scholarly journals Effects of a Motor Imagery Task on Functional Brain Network Community Structure in Older Adults: Data from the Brain Networks and Mobility Function (B-NET) Study

2021 ◽  
Vol 11 (1) ◽  
pp. 118
Author(s):  
Blake R. Neyland ◽  
Christina E. Hugenschmidt ◽  
Robert G. Lyday ◽  
Jonathan H. Burdette ◽  
Laura D. Baker ◽  
...  
Keyword(s):  
Older Adults ◽  
Graph Theory ◽  
Community Structure ◽  
Resting State ◽  
Brain Networks ◽  
Brain Network ◽  
Functional Brain ◽  
Network Community ◽  
Functional Brain Networks ◽  
The Brain

Elucidating the neural correlates of mobility is critical given the increasing population of older adults and age-associated mobility disability. In the current study, we applied graph theory to cross-sectional data to characterize functional brain networks generated from functional magnetic resonance imaging data both at rest and during a motor imagery (MI) task. Our MI task is derived from the Mobility Assessment Tool–short form (MAT-sf), which predicts performance on a 400 m walk, and the Short Physical Performance Battery (SPPB). Participants (n = 157) were from the Brain Networks and Mobility (B-NET) Study (mean age = 76.1 ± 4.3; % female = 55.4; % African American = 8.3; mean years of education = 15.7 ± 2.5). We used community structure analyses to partition functional brain networks into communities, or subnetworks, of highly interconnected regions. Global brain network community structure decreased during the MI task when compared to the resting state. We also examined the community structure of the default mode network (DMN), sensorimotor network (SMN), and the dorsal attention network (DAN) across the study population. The DMN and SMN exhibited a task-driven decline in consistency across the group when comparing the MI task to the resting state. The DAN, however, displayed an increase in consistency during the MI task. To our knowledge, this is the first study to use graph theory and network community structure to characterize the effects of a MI task, such as the MAT-sf, on overall brain network organization in older adults.

LANGUAGE AND WILLIAMS SYNDROME

2008 ◽  
Vol 28 ◽  
pp. 191-204 ◽  
Author(s):  
Ching-fen Hsu ◽  
Annette Karmiloff-Smith
Keyword(s):  
Williams Syndrome ◽  
Cognitive Processes ◽  
Language Production ◽  
Developmental Disorders ◽  
Human Life ◽  
Initial State ◽  
Functional Brain ◽  
Functional Brain Networks ◽  
Overt Language Production ◽  
The Brain

Most aspects of human life—from gene expression, to brain structure/function, to underlying linguistic and cognitive processes, through to overt language production and comprehension behaviors—are the result of dynamic developmental processes, in which timing plays a crucial role. So, the study of language acquisition in developmental disorders such as Williams syndrome (WS) needs to change from the still widely held view that developmental disorders can be accounted for in terms of spared versus impaired modules to one that takes serious account of the fact that the infant cortex passes from an initial state of high regional interconnectivity to a subsequent state of progressively increasing specialization and localization of functional brain networks. With such early interconnectivity in mind, developmental neuroscientists must explore the possibility that a small perturbation in low-level processes in one part of the brain very early in development can result in serious deficits in higher-level processes in another part of the brain later in development. Therefore, in profiling developmental disorders of language such as in WS, it is vital to start in early infancy, from which to trace the full trajectory of the interactions of language and other cognitive processes across infancy, toddlerhood, and childhood, through to adolescence and adulthood.

scholarly journals Characterizing Complex Networks Using Entropy-Degree Diagrams: Unveiling Changes in Functional Brain Connectivity Induced by Ayahuasca

Entropy ◽  
2019 ◽  
Vol 21 (2) ◽  
pp. 128 ◽  
Author(s):  
Aline Viol ◽  
Fernanda Palhano-Fontes ◽  
Heloisa Onias ◽  
Draulio de Araujo ◽  
Philipp Hövel ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Human Brain ◽  
Resting State ◽  
Structural Information ◽  
Brain Connectivity ◽  
Functional Brain ◽  
States Of Consciousness ◽  
Functional Brain Networks ◽  
Empirical Networks ◽  
Emergent Behaviors

With the aim of further advancing the understanding of the human brain’s functional connectivity, we propose a network metric which we term the geodesic entropy. This metric quantifies the Shannon entropy of the distance distribution to a specific node from all other nodes. It allows to characterize the influence exerted on a specific node considering statistics of the overall network structure. The measurement and characterization of this structural information has the potential to greatly improve our understanding of sustained activity and other emergent behaviors in networks. We apply this method to study how the psychedelic infusion Ayahuasca affects the functional connectivity of the human brain in resting state. We show that the geodesic entropy is able to differentiate functional networks of the human brain associated with two different states of consciousness in the awaking resting state: (i) the ordinary state and (ii) a state altered by ingestion of the Ayahuasca. The functional brain networks from subjects in the altered state have, on average, a larger geodesic entropy compared to the ordinary state. Finally, we discuss why the geodesic entropy may bring even further valuable insights into the study of the human brain and other empirical networks.

scholarly journals Vestibular Agnosia is linked to abnormal functional brain networks

2021 ◽  
Author(s):  
Zaeem Hadi ◽  
Yuscah Pondeca ◽  
Elena Calzolari ◽  
Mariya Chepisheva ◽  
Rebecca M Smith ◽  
...  
Keyword(s):  
Motion Perception ◽  
Brain Network ◽  
Functional Brain ◽  
Inferior Longitudinal Fasciculus ◽  
Functional Brain Networks ◽  
Vestibular Perception ◽  
Postural Imbalance ◽  
Self Motion ◽  
The Brain ◽  
Anterior Posterior

AbstractActivation of the peripheral vestibular apparatus simultaneously elicits a reflex vestibular nystagmus and the vestibular perception of self-motion (vestibular-motion perception) or vertigo. In a newly characterised condition called Vestibular Agnosia found in conditions with disrupted brain network connectivity, e.g. traumatic brain injury (TBI) or neurodegeneration (Parkinson’s Disease), the link between vestibular reflex and perception is uncoupled, such that, peripheral vestibular activation elicits a vestibular ocular reflex nystagmus but without vertigo. Using structural brain imaging in acute traumatic brain injury, we recently linked vestibular agnosia to postural imbalance via disrupted right temporal white-matter circuits (inferior longitudinal fasciculus), however no white-matter tracts were specifically linked to vestibular agnosia. Given the relative difficulty in localizing the neuroanatomical correlates of vestibular-motion perception, and compatible with current theories of human consciousness (viz. the Global Neuronal Workspace Theory), we postulate that vestibular-motion perception (vertigo) is mediated by the coordinated interplay between fronto-parietal circuits linked to whole-brain broadcasting of the vestibular signal of self-motion. We thus used resting state functional MRI (rsfMRI) to map functional brain networks and hence test our postulate of an anterior-posterior cortical network mediating vestibular agnosia. Whole-brain rsfMRI was acquired from 39 prospectively recruited acute TBI patients (and 37 matched controls) with preserved peripheral and reflex vestibular function, along with self-motion perceptual thresholds during passive yaw rotations in the dark, and posturography. Following quality control of the brain imaging, 25 TBI patients’ images were analyzed. We classified 11 TBI patients with vestibular agnosia and 14 without vestibular agnosia based on laboratory testing of self-motion perception. Using independent component analysis, we found altered functional connectivity within posterior (right superior longitudinal fasciculus) and anterior networks (left rostral prefrontal cortex) in vestibular agnosia. Regions of interest analyses showed both inter-hemispheric and intra-hemispheric (left anterior-posterior) network disruption in vestibular agnosia. Assessing the brain regions linked via right inferior longitudinal fasciculus, a tract linked to vestibular agnosia in unbalanced patients (but now controlled for postural imbalance), seed-based analyses showed altered connectivity between higher order visual cortices involved in motion perception and mid-temporal regions. In conclusion, vestibular agnosia in our patient group is mediated by multiple brain network dysfunction, involving primarily left frontal and bilateral posterior networks. Understanding the brain mechanisms of vestibular agnosia provide both an insight into the physiological mechanisms of vestibular perception as well as an opportunity to diagnose and monitor vestibular cognitive deficits in brain disease such as TBI and neurodegeneration linked to imbalance and spatial disorientation.

scholarly journals Abnormal Functional Brain Network in Parkinson's Disease and the Effect of Acute Deep Brain Stimulation

2021 ◽  
Vol 12 ◽  
Author(s):  
Zhibao Li ◽  
Chong Liu ◽  
Qiao Wang ◽  
Kun Liang ◽  
Chunlei Han ◽  
...  
Keyword(s):  
Deep Brain Stimulation ◽  
Functional Connectivity ◽  
Frequency Band ◽  
Brain Regions ◽  
Brain Network ◽  
Clustering Coefficient ◽  
Functional Brain ◽  
Frequency Bands ◽  
The Brain ◽  
Deep Brain

Objective: The objective of this study was to use functional connectivity and graphic indicators to investigate the abnormal brain network topological characteristics caused by Parkinson's disease (PD) and the effect of acute deep brain stimulation (DBS) on those characteristics in patients with PD.Methods: We recorded high-density EEG (256 channels) data from 21 healthy controls (HC) and 20 patients with PD who were in the DBS-OFF state and DBS-ON state during the resting state with eyes closed. A high-density EEG source connectivity method was used to identify functional brain networks. Power spectral density (PSD) analysis was compared between the groups. Functional connectivity was calculated for 68 brain regions in the theta (4–8 Hz), alpha (8–13 Hz), beta1 (13–20 Hz), and beta2 (20–30 Hz) frequency bands. Network estimates were measured at both the global (network topology) and local (inter-regional connection) levels.Results: Compared with HC, PSD was significantly increased in the theta (p = 0.003) frequency band and was decreased in the beta1 (p = 0.009) and beta2 (p = 0.04) frequency bands in patients with PD. However, there were no differences in any frequency bands between patients with PD with DBS-OFF and DBS-ON. The clustering coefficient and local efficiency of patients with PD showed a significant decrease in the alpha, beta1, and beta2 frequency bands (p < 0.001). In addition, edgewise statistics showed a significant difference between the HC and patients with PD in all analyzed frequency bands (p < 0.005). However, there were no significant differences between the DBS-OFF state and DBS-ON state in the brain network, except for the functional connectivity in the beta2 frequency band (p < 0.05).Conclusion: Compared with HC, patients with PD showed the following characteristics: slowed EEG background activity, decreased clustering coefficient and local efficiency of the brain network, as well as both increased and decreased functional connectivity between different brain areas. Acute DBS induces a local response of the brain network in patients with PD, mainly showing decreased functional connectivity in a few brain regions in the beta2 frequency band.

scholarly journals Disruption to functional networks in neonates with perinatal brain injury predicts motor skills at 8 months

2017 ◽  
Author(s):  
Annika C. Linke ◽  
Conor Wild ◽  
Leire Zubiaurre-Elorza ◽  
Charlotte Herzmann ◽  
Hester Duffy ◽  
...  
Keyword(s):  
Brain Injury ◽  
Functional Connectivity ◽  
Motor Skills ◽  
Brain Networks ◽  
Motor Impairment ◽  
Structural Mri ◽  
Perinatal Brain Injury ◽  
Diverse Range ◽  
Functional Brain ◽  
Functional Brain Networks

AbstractObjectiveFunctional connectivity magnetic resonance imaging (fcMRI) of neonates with perinatal brain injury could improve prediction of motor impairment before symptoms manifest, and establish how early brain organization relates to subsequent development. Methods: This cohort study is the first to describe and quantitatively assess functional brain networks and their relation to later motor skills in neonates with a diverse range of perinatal brain injuries. Infants (n=65, included in final analyses: n=53) were recruited from the neonatal intensive care unit (NICU) and were stratified based on their age at birth (premature vs. term), and on whether neuropathology was diagnosed from structural MRI. Functional brain networks and a measure of disruption to functional connectivity were obtained from 14 minutes of fcMRI acquired during natural sleep at term-equivalent age.ResultsDisruption to connectivity of the somatomotor and frontoparietal executive networks predicted motor impairment at 4 and 8 months. This disruption in functional connectivity was not found to be driven by differences between clinical groups, or by any of the specific measures we captured to describe the clinical course.ConclusionfcMRI was predictive over and above other clinical measures available at discharge from the NICU, including structural MRI. Motor learning was affected by disruption to somatomotor networks, but also frontoparietal executive networks, which supports the functional importance of these networks in early development. Disruption to these two networks might be best addressed by distinct intervention strategies.

scholarly journals METHODOLOGY OF DISCOVERING FUNCTIONAL BRAIN NETWORKS FROM FMRI DATA IN RESTING STAGE

2019 ◽  
Author(s):  
Irina Enyagina ◽  
Alexey Poyda ◽  
Vadim Ushakov ◽  
Maxim Sharaev ◽  
Vyacheslav Orlov ◽  
...  
Keyword(s):  
Brain Networks ◽  
Fmri Data ◽  
Functional Brain ◽  
Functional Brain Networks
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