scholarly journals Dynamic Causal Modelling of the Reduced Habituation to Painful Stimuli in Migraine: An EEG Study

2020 ◽  
Vol 10 (10) ◽  
pp. 712
Author(s):  
Iege Bassez ◽  
Frederik Van de Steen ◽  
Katia Ricci ◽  
Eleonora Vecchio ◽  
Eleonora Gentile ◽  
...  
Keyword(s):  
Effective Connectivity ◽  
Anterior Cingulate ◽  
Group Differences ◽  
Sensory Stimuli ◽  
Consistent Finding ◽  
Causal Modelling ◽  
Migraine Group ◽  
Somatosensory Areas ◽  
Dynamic Causal Modelling

A consistent finding in migraine is reduced cortical habituation to repetitive sensory stimuli. This study investigated brain dynamics underlying the atypical habituation to painful stimuli in interictal migraine. We investigated modulations in effective connectivity between the sources of laser evoked potentials (LEPs) from a first to final block of trigeminal LEPs using dynamic causal modelling (DCM) in a group of 23 migraine patients and 20 controls. Additionally, we looked whether the strength of dynamical connections in the migrainous brain is initially different. The examined network consisted of the secondary somatosensory areas (lS2, rS2), insulae (lIns, rIns), anterior cingulate cortex (ACC), contralateral primary somatosensory cortex (lS1), and a hidden source assumed to represent the thalamus. Results suggest that migraine patients show initially heightened communication between lS1 and the thalamus, in both directions. After repetitive stimulations, connection strengths from the thalamus to all somatosensory areas habituated in controls whereas this was not apparent in migraine. Together with further abnormalities in initial connectivity strengths and modulations between the thalamus and the insulae, these results are in line with altered thalamo-cortical network dynamics in migraine. Group differences in connectivity from and to the insulae including interhemispheric connections, suggests an important role of the insulae.

scholarly journals The neurophysiological architecture of semantic dementia: spectral dynamic causal modelling of a neurodegenerative proteinopathy

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Elia Benhamou ◽  
Charles R. Marshall ◽  
Lucy L. Russell ◽  
Chris J. D. Hardy ◽  
Rebecca L. Bond ◽  
...  
Keyword(s):  
Network Architecture ◽  
Large Scale ◽  
Effective Connectivity ◽  
Brain Regions ◽  
Semantic Dementia ◽  
Older Individuals ◽  
World Knowledge ◽  
Causal Modelling ◽  
Dynamic Causal Modelling ◽  
Semantic Impairment

Abstract The selective destruction of large-scale brain networks by pathogenic protein spread is a ubiquitous theme in neurodegenerative disease. Characterising the circuit architecture of these diseases could illuminate both their pathophysiology and the computational architecture of the cognitive processes they target. However, this is challenging using standard neuroimaging techniques. Here we addressed this issue using a novel technique—spectral dynamic causal modelling—that estimates the effective connectivity between brain regions from resting-state fMRI data. We studied patients with semantic dementia—the paradigmatic disorder of the brain system mediating world knowledge—relative to healthy older individuals. We assessed how the effective connectivity of the semantic appraisal network targeted by this disease was modulated by pathogenic protein deposition and by two key phenotypic factors, semantic impairment and behavioural disinhibition. The presence of pathogenic protein in SD weakened the normal inhibitory self-coupling of network hubs in both antero-mesial temporal lobes, with development of an abnormal excitatory fronto-temporal projection in the left cerebral hemisphere. Semantic impairment and social disinhibition were linked to a similar but more extensive profile of abnormally attenuated inhibitory self-coupling within temporal lobe regions and excitatory projections between temporal and inferior frontal regions. Our findings demonstrate that population-level dynamic causal modelling can disclose a core pathophysiological feature of proteinopathic network architecture—attenuation of inhibitory connectivity—and the key elements of distributed neuronal processing that underwrite semantic memory.

scholarly journals Changes in Effective Connectivity of the Superior Parietal Lobe during Inhibition and Redirection of Eye Movements

2015 ◽  
Vol 9s1 ◽  
pp. JEN.S32736
Author(s):  
Susanne J. Asscheman ◽  
Katharine N. Thakkar ◽  
Sebastiaan F.W. Neggers
Keyword(s):  
Eye Movements ◽  
Executive Control ◽  
Parietal Lobe ◽  
Effective Connectivity ◽  
Saccadic Eye Movements ◽  
Brain Regions ◽  
Anterior Cingulate ◽  
Imaging Data ◽  
Saccade Control

Executive control is the ability to flexibly control behavior and is frequently studied with saccadic eye movements. Contrary to frontal oculomotor areas, the role of the superior parietal lobe (SPL) in the executive control of saccades remains unknown. To explore the role of SPL networks in saccade control, we performed a saccadic search-step task while acquiring functional magnetic resonance imaging data for 41 participants. Psychophysiological interaction analyses assessed task-related differences in the effective connectivity of SPL with other brain regions during the inhibition and redirection of saccades. Results indicate an increased coupling of SPL with frontal, posterior, and striatal oculomotor areas for redirected saccades versus visually guided saccades. Saccade inhibition versus unsuccessful inhibition revealed an increased coupling of SPL with dorsolateral prefrontal cortex and anterior cingulate cortex. We discuss how these findings relate to ongoing debates about the implementation of executive control and conclude that early attentional control and rapid updating of saccade goals are important signals for executive control.

MRS Shows Regionally Increased Glutamate Levels among Patients with Exhaustion Syndrome Due to Occupational Stress

2020 ◽  
Vol 30 (6) ◽  
pp. 3759-3770 ◽  
Author(s):  
Ivanka Savic
Keyword(s):  
Occupational Stress ◽  
Potential Role ◽  
Anterior Cingulate ◽  
Group Differences ◽  
Glutamine Concentration ◽  
1H Mr Spectroscopy ◽  
Female Predominance ◽  
Tentative Explanation ◽  
Neural Marker

Abstract Despite the rapid increase of reports of exhaustion syndrome (ES) due to daily occupational stress, the mechanisms underlying ES are unknown. We used voxel-based 1H-MR spectroscopy to examine the potential role of glutamate in this condition. The levels of glutamate were found to be elevated among ES patients (n = 30, 16 females) compared with controls (n = 31, 15 females). Notably, this increase was detected only in the anterior cingulate and mesial prefrontal cortex (ACC/mPFC), and the glutamate levels were linearly correlated with the degree of perceived stress. Furthermore, there was a sex by group interaction, as the glutamate elevation was present only in female patients. Female but not male ES patients also showed an increase in N-acetyl aspartate (NAA) levels in the amygdala. No group differences were detected in glutamine concentration (also measured). These data show the key role of glutamate in stress-related neuronal signaling and the specific roles of the amygdala and ACC/mPFC. The data extend previous reports about the neurochemical basis of stress and identify a potential neural marker and mediator of ES due to occupational stress. The observation of specific sex differences provides a tentative explanation to the well-known female predominance in stress-related psychopathology.

Poster #T190 EFFECTIVE CONNECTIVITY IN SCHIZOPHRENIA – DYNAMIC CAUSAL MODELLING OF THE MISMATCH NEGATIVITY

2014 ◽  
Vol 153 ◽  
pp. S356
Author(s):  
Siri M. Ranlund ◽  
Alvaro Diez ◽  
Rick A. Adams ◽  
Harriet Brown ◽  
Muriel Walshe ◽  
...  
Keyword(s):  
Mismatch Negativity ◽  
Effective Connectivity ◽  
Causal Modelling ◽  
Dynamic Causal Modelling

Dynamic causal modelling of evoked responses: The role of intrinsic connections

NeuroImage ◽  
2007 ◽  
Vol 36 (2) ◽  
pp. 332-345 ◽  
Author(s):  
Stefan J. Kiebel ◽  
Marta I. Garrido ◽  
Karl J. Friston
Keyword(s):  
Evoked Responses ◽  
Causal Modelling ◽  
Dynamic Causal Modelling
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