Investigating the interaction between white matter and brain state on tDCS-induced changes in brain network activity

AbstractBackgroundTranscranial direct current stimulation (tDCS) is a form of noninvasive brain stimulation whose potential as a cognitive therapy is hindered by our limited understanding of how participant and experimental factors influence its effects. Using functional MRI to study brain networks, we have previously shown in healthy controls that the physiological effects of tDCS are strongly influenced by brain state. We have additionally shown, in both healthy and traumatic brain injury (TBI) populations, that the behavioral effects of tDCS are positively correlated with white matter (WM) structure.ObjectivesIn this study we investigate how these two factors, WM structure and brain state, interact to shape the effect of tDCS on brain network activity.MethodsWe applied anodal, cathodal and sham tDCS to the right inferior frontal gyrus (rIFG) of healthy (n=22) and TBI participants (n=34). We used the Choice Reaction Task (CRT) performance to manipulate brain state during tDCS. We acquired simultaneous fMRI to assess activity of cognitive brain networks and used Fractional Anisotropy (FA) as a measure of WM structure.ResultsWe find that the effects of tDCS on brain network activity in TBI participants are highly dependent on brain state, replicating findings from our previous healthy control study in a separate, patient cohort. We then show that WM structure further modulates the brain-state dependent effects of tDCS on brain network activity. These effects are not unidirectional – in the absence of task with anodal and cathodal tDCS, FA is positively correlated with brain activity in several regions of the default mode network. Conversely, with cathodal tDCS during CRT performance, FA is negatively correlated with brain activity in a salience network region.ConclusionsOur results show that experimental and participant factors interact to have unexpected effects on brain network activity, and that these effects are not fully predictable by studying the factors in isolation.

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Brain-wide electrical dynamics encode an appetitive socioemotional state

10.1101/2020.07.01.181347 ◽

2020 ◽

Author(s):

Stephen D. Mague ◽

Austin Talbot ◽

Cameron Blount ◽

Lara J. Duffney ◽

Kathryn K. Walder-Christensen ◽

...

Keyword(s):

Social Preference ◽

Brain Regions ◽

Brain Network ◽

Network Activity ◽

Brain State ◽

Genetic Mouse Model ◽

Spatially Distributed ◽

Subcortical Regions ◽

Complex Social Behavior ◽

The Brain

AbstractMany cortical and subcortical regions contribute to complex social behavior; nevertheless, the network level architecture whereby the brain integrates this information to encode appetitive socioemotional behavior remains unknown. Here we measure electrical activity from eight brain regions as mice engage in a social preference assay. We then use machine learning to discover an explainable brain network that encodes the extent to which mice chose to engage another mouse. This socioemotional network is organized by theta oscillations leading from prelimbic cortex and amygdala that converge on ventral tegmental area, and network activity is synchronized with brain-wide cellular firing. The network generalizes, on a mouse-by-mouse basis, to encode socioemotional behaviors in healthy animals, but fails to encode an appetitive socioemotional state in a ‘high confidence’ genetic mouse model of autism. Thus, our findings reveal the architecture whereby the brain integrates spatially distributed activity across timescales to encode an appetitive socioemotional brain state in health and disease.

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A selective diffusion model of brain network activity

10.32470/ccn.2018.1195-0 ◽

2018 ◽

Cited By ~ 1

Author(s):

Daniel Graham ◽

Yan Hao

Keyword(s):

Diffusion Model ◽

Brain Network ◽

Network Activity ◽

Selective Diffusion

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Energy constraints on brain network formation

Scientific Reports ◽

10.1038/s41598-021-91250-y ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Kosuke Takagi

Keyword(s):

Network Formation ◽

Cost Minimization ◽

Brain Network ◽

Network Activity ◽

Rich Functionality ◽

Energy Constraints ◽

Dependent Form ◽

Statistical Similarity ◽

Simple Ratio ◽

The Brain

AbstractEnergy constraints are a fundamental limitation of the brain, which is physically embedded in a restricted space. The collective dynamics of neurons through connections enable the brain to achieve rich functionality, but building connections and maintaining activity come at a high cost. The effects of reducing these costs can be found in the characteristic structures of the brain network. Nevertheless, the mechanism by which energy constraints affect the organization and formation of the neuronal network in the brain is unclear. Here, it is shown that a simple model based on cost minimization can reproduce structures characteristic of the brain network. With reference to the behavior of neurons in real brains, the cost function was introduced in an activity-dependent form correlating the activity cost and the wiring cost as a simple ratio. Cost reduction of this ratio resulted in strengthening connections, especially at highly activated nodes, and induced the formation of large clusters. Regarding these network features, statistical similarity was confirmed by comparison to connectome datasets from various real brains. The findings indicate that these networks share an efficient structure maintained with low costs, both for activity and for wiring. These results imply the crucial role of energy constraints in regulating the network activity and structure of the brain.

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White matter in prolonged glucocorticoid response to psychological stress in schizophrenia

Neuropsychopharmacology ◽

10.1038/s41386-021-01077-4 ◽

2021 ◽

Author(s):

Eric L. Goldwaser ◽

Joshua Chiappelli ◽

Mark D. Kvarta ◽

Xiaoming Du ◽

Zachary B. Millman ◽

...

Keyword(s):

White Matter ◽

Psychological Stress ◽

Stress Responses ◽

Diffusion Tensor ◽

Brain Network ◽

White Matter Tracts ◽

Cortisol Reactivity ◽

Cortisol Responses ◽

Psychological Stressor ◽

Post Stress

AbstractStress is implicated in psychosis etiology and exacerbation, but pathogenesis toward brain network alterations in schizophrenia remain unclear. White matter connects limbic and prefrontal regions responsible for stress response regulation, and white matter tissues are also vulnerable to glucocorticoid aberrancies. Using a novel psychological stressor task, we studied cortisol stress responses over time and white matter microstructural deficits in schizophrenia spectrum disorder (SSD). Cortisol was measured at baseline, 0-, 20-, and 40-min after distress induction by a psychological stressor task in 121 SSD patients and 117 healthy controls (HC). White matter microstructural integrity was measured by 64-direction diffusion tensor imaging. Fractional anisotropy (FA) in white matter tracts were related to cortisol responses and then compared to general patterns of white matter tract deficits in SSD identified by mega-analysis. Differences between 40-min post-stress and baseline, but not acute reactivity post-stress, was significantly elevated in SSD vs HC, time × diagnosis interaction F2.3,499.9 = 4.1, p = 0.013. All SSD white matter tracts were negatively associated with prolonged cortisol reactivity but all tracts were positively associated with prolonged cortisol reactivity in HC. Individual tracts most strongly associated with prolonged cortisol reactivity were also most impacted in schizophrenia in general as established by the largest schizophrenia white matter study (r = −0.56, p = 0.006). Challenged with psychological stress, SSD and HC mount similar cortisol responses, and impairments arise in the resolution timeframe. Prolonged cortisol elevations are associated with the white matter deficits in SSD, in a pattern previously associated with schizophrenia in general.

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Psilocybin-induced changes in brain network integrity and segregation correlate with plasma psilocin level and psychedelic experience

European Neuropsychopharmacology ◽

10.1016/j.euroneuro.2021.06.001 ◽

2021 ◽

Vol 50 ◽

pp. 121-132

Author(s):

Martin K. Madsen ◽

Dea S. Stenbæk ◽

Albin Arvidsson ◽

Sophia Armand ◽

Maja R. Marstrand-Joergensen ◽

...

Keyword(s):

Brain Network ◽

Induced Changes

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Detecting functional connectivity disruptions in a translational pediatric traumatic brain injury porcine model using resting-state and task-based fMRI

Scientific Reports ◽

10.1038/s41598-021-91853-5 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Gregory Simchick ◽

Kelly M. Scheulin ◽

Wenwu Sun ◽

Sydney E. Sneed ◽

Madison M. Fagan ◽

...

Keyword(s):

Traumatic Brain Injury ◽

Brain Injury ◽

Functional Connectivity ◽

Resting State ◽

Correlation Coefficients ◽

Brain Network ◽

Network Activity ◽

Anatomical Structures ◽

Healthy Control ◽

And Task

AbstractFunctional magnetic resonance imaging (fMRI) has significant potential to evaluate changes in brain network activity after traumatic brain injury (TBI) and enable early prognosis of potential functional (e.g., motor, cognitive, behavior) deficits. In this study, resting-state and task-based fMRI (rs- and tb-fMRI) were utilized to examine network changes in a pediatric porcine TBI model that has increased predictive potential in the development of novel therapies. rs- and tb-fMRI were performed one day post-TBI in piglets. Activation maps were generated using group independent component analysis (ICA) and sparse dictionary learning (sDL). Activation maps were compared to pig reference functional connectivity atlases and evaluated using Pearson spatial correlation coefficients and mean ratios. Nonparametric permutation analyses were used to determine significantly different activation areas between the TBI and healthy control groups. Significantly lower Pearson values and mean ratios were observed in the visual, executive control, and sensorimotor networks for TBI piglets compared to controls. Significant differences were also observed within several specific individual anatomical structures within each network. In conclusion, both rs- and tb-fMRI demonstrate the ability to detect functional connectivity disruptions in a translational TBI piglet model, and these disruptions can be traced to specific affected anatomical structures.

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Brain Network Dysfunction in Poststroke Delirium and Spatial Neglect: An fMRI Study

Stroke ◽

10.1161/strokeaha.121.035733 ◽

2021 ◽

Author(s):

Olga Boukrina ◽

Mateusz Kowalczyk ◽

Yury Koush ◽

Yekyung Kong ◽

A.M. Barrett

Keyword(s):

Right Hemisphere ◽

Functional Neuroimaging ◽

Brain Network ◽

Spatial Neglect ◽

Stroke Recovery ◽

Network Activity ◽

Brain Disorder ◽

Fmri Study ◽

The Right ◽

Right Hemisphere Stroke

Background and Purpose: Delirium, an acute reduction in cognitive functioning, hinders stroke recovery and contributes to cognitive decline. Right-hemisphere stroke is linked with higher delirium incidence, likely, due to the prevalence of spatial neglect (SN), a right-brain disorder of spatial processing. This study tested if symptoms of delirium and SN after right-hemisphere stroke are associated with abnormal function of the right-dominant neural networks specialized for maintaining attention, orientation, and arousal. Methods: Twenty-nine participants with right-hemisphere ischemic stroke undergoing acute rehabilitation completed delirium and SN assessments and functional neuroimaging scans. Whole-brain functional connectivity of 4 right-hemisphere seed regions in the cortical-subcortical arousal and attention networks was assessed for its relationship to validated SN and delirium severity measures. Results: Of 29 patients, 6 (21%) met the diagnostic criteria for delirium and 16 (55%) for SN. Decreased connectivity of the right basal forebrain to brain stem and basal ganglia predicted more severe SN. Increased connectivity of the arousal and attention network regions with the parietal, frontal, and temporal structures in the unaffected hemisphere was also found in more severe delirium and SN. Conclusions: Delirium and SN are associated with decreased arousal network activity and an imbalance of cortico-subcortical hemispheric connectivity. Better understanding of neural correlates of poststroke delirium and SN will lead to improved neuroscience-based treatment development for these disorders.

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