Long-Term Effects of Attentional Performance on Functional Brain Network Topology

Abstract Background With the high spatial resolution and the potential to reach deep brain structures, ultrasound-based brain stimulation techniques offer new opportunities to non-invasively treat neurological and psychiatric disorders. However, little is known about long-term effects of ultrasound-based brain stimulation. Applying a longitudinal design, we comprehensively investigated neuromodulation induced by ultrasound brain stimulation to provide first sham-controlled evidence of long-term effects on the human brain and behavior. Methods Twelve healthy participants received three sham and three verum sessions with transcranial pulse stimulation (TPS) focused on the cortical somatosensory representation of the right hand. One week before and after the sham and verum TPS applications, comprehensive structural and functional resting state MRI investigations and behavioral tests targeting tactile spatial discrimination and sensorimotor dexterity were performed. Results Compared to sham, global efficiency significantly increased within the cortical sensorimotor network after verum TPS, indicating an upregulation of the stimulated functional brain network. Axial diffusivity in left sensorimotor areas decreased after verum TPS, demonstrating an improved axonal status in the stimulated area. Conclusions TPS increased the functional and structural coupling within the stimulated left primary somatosensory cortex and adjacent sensorimotor areas up to one week after the last stimulation. These findings suggest that TPS induces neuroplastic changes that go beyond the spatial and temporal stimulation settings encouraging further clinical applications.

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The etiological classification of the epilepsies: A brain network underpinning?

10.1101/739052 ◽

2019 ◽

Author(s):

Geertruida Slinger ◽

Willem M. Otte ◽

Lotte Noorlag ◽

Floor E. Jansen ◽

Kees P.J. Braun ◽

...

Keyword(s):

Network Topology ◽

Principal Component ◽

Brain Network ◽

Functional Brain ◽

Eyes Closed ◽

Functional Brain Network ◽

Interictal Eeg ◽

Epilepsy Classification ◽

Mechanistic Basis ◽

Patients With Epilepsy

AbstractObjectivethe current epilepsy classification is primarily clinical driven and lacks a mechanistic basis. A mechanistic basis of the classification, and within the classification especially the etiology layer, may help to better understand epilepsy and the associated comorbidities. It may also be helpful in guiding epilepsy treatment. With this study we aimed to investigate if there is a modelled mechanistic underpinning for the etiological epilepsy classification by assessing the association between epilepsy etiology and brain network topology.Methodsto that aim we assessed the association between epilepsy etiology and brain network topology. We included children referred to our outpatient first seizure clinic with suspected epilepsy who had a standard interictal EEG recording. From these EEGs, functional networks were constructed based on eyes-closed resting state time-series. Networks were characterized using measures of segregation, integration, centrality, and network strength. Principal component analyses were used to assess whether patients with epilepsy of similar etiology cluster together based on their functional brain network topology.Resultsin total, 228 children with epilepsy were included. Another 402 children served as control subjects. We were not able to detect a correlation between epilepsy etiology and functional brain network topology. We also did not find a difference in brain network topology between the controls and patients with epilepsy.Conclusionsour results do not support the presence of a brain network underpinning for the etiological epilepsy classification. This may support the hypothesis that brain network abnormalities in epilepsy are a result of ongoing seizure activity rather than the epilepsy etiology itself. Further in-depth analyses of network measures and longitudinal studies are needed to confirm this hypothesis.

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Functional brain network topology across the menstrual cycle is estradiol dependent and correlates with individual well‐being

Journal of Neuroscience Research ◽

10.1002/jnr.24898 ◽

2021 ◽

Author(s):

Marianna Liparoti ◽

Emahnuel Troisi Lopez ◽

Laura Sarno ◽

Rosaria Rucco ◽

Roberta Minino ◽

...

Keyword(s):

Menstrual Cycle ◽

Network Topology ◽

Well Being ◽

Brain Network ◽

Functional Brain ◽

Functional Brain Network

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Neural evidence for long-term marriage shaping the functional brain network organization between couples

NeuroImage ◽

10.1016/j.neuroimage.2019.05.058 ◽

2019 ◽

Vol 199 ◽

pp. 87-92

Author(s):

Lin Shi ◽

Wutao Lou ◽

Adrian Wong ◽

Fan Zhang ◽

Jill Abrigo ◽

...

Keyword(s):

Brain Network ◽

Network Organization ◽

Functional Brain ◽

Functional Brain Network

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SSVEP Response Is Related to Functional Brain Network Topology Entrained by the Flickering Stimulus

PLoS ONE ◽

10.1371/journal.pone.0072654 ◽

2013 ◽

Vol 8 (9) ◽

pp. e72654 ◽

Cited By ~ 23

Author(s):

Yangsong Zhang ◽

Peng Xu ◽

Yingling Huang ◽

Kaiwen Cheng ◽

Dezhong Yao

Keyword(s):

Network Topology ◽

Brain Network ◽

Functional Brain ◽

Functional Brain Network

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Changes in functional brain network topology after successful and unsuccessful corpus callosotomy for Lennox-Gastaut Syndrome

Scientific Reports ◽

10.1038/s41598-018-21764-5 ◽

2018 ◽

Vol 8 (1) ◽

Cited By ~ 4

Author(s):

Jun-Ge Liang ◽

Nam-Young Kim ◽

Ara Ko ◽

Heung Dong Kim ◽

Dongpyo Lee

Keyword(s):

Network Topology ◽

Brain Network ◽

Corpus Callosotomy ◽

Functional Brain ◽

Functional Brain Network

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State-dependent changes of connectivity patterns and functional brain network topology in autism spectrum disorder

Neuropsychologia ◽

10.1016/j.neuropsychologia.2012.09.047 ◽

2012 ◽

Vol 50 (14) ◽

pp. 3653-3662 ◽

Cited By ~ 43

Author(s):

Pablo Barttfeld ◽

Bruno Wicker ◽

Sebastián Cukier ◽

Silvana Navarta ◽

Sergio Lew ◽

...

Keyword(s):

Autism Spectrum Disorder ◽

Network Topology ◽

Autism Spectrum ◽

Brain Network ◽

Spectrum Disorder ◽

Functional Brain ◽

State Dependent ◽

Functional Brain Network ◽

Connectivity Patterns

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Classification Between Major Depressive Disorder and Healthy Controls Using Functional Brain Network Topology

Biological Psychiatry ◽

10.1016/j.biopsych.2020.02.673 ◽

2020 ◽

Vol 87 (9) ◽

pp. S260

Author(s):

Yael Jacob ◽

Laurel Morris ◽

Kuang-Han Huang ◽

Molly Schneider ◽

Sarah Rutter ◽

...

Keyword(s):

Major Depressive Disorder ◽

Depressive Disorder ◽

Network Topology ◽

Brain Network ◽

Healthy Controls ◽

Major Depressive ◽

Functional Brain ◽

Functional Brain Network

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Research on Functional Brain Network Metrics for Depression Patients Automatic Identification

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.427-429.1440 ◽

2013 ◽

Vol 427-429 ◽

pp. 1440-1446

Author(s):

Chen Cheng ◽

Wen Zhao Liu ◽

Jun Jie Chen

Keyword(s):

Network Topology ◽

Neurological Diseases ◽

Brain Network ◽

Classification Model ◽

Support Vector ◽

Automatic Identification ◽

Functional Brain ◽

Functional Brain Network ◽

Network Metrics ◽

Abnormal Brain

Nowadays, Brain network as a means of emerging brain disease research has been fully recognized which is applied to the neurological diseases, such as major depressive disorder (MDD). It also can detect the exception of the whole brain network topological. But there is no evidence to prove that abnormal brain network topology metrics can be an effective feature in the classification model to distinguish the healthy control and MDD. So, we hypothesize the abnormal brain network topology metrics can be used as an valid classification feature. Resting state functional brain networks were constructed for 26 healthy controls and 34 MDD patients by thresholding partial correlation matrices of 90 regions. According to the theory-based approaches, the global and local metrics were calculated. Non-parametric permutation tests were then used for group comparisons of topological metrics, which were used as classified features in support vector machine algorithm. The current study demonstrate that MDD is associated with abnormal function brain network topological metrics and statistically significance network metrics can be successfully used for feature selection in classification algorithms.

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