scholarly journals Topological Aberrance of Structural Brain Network Provides Quantitative Markers of post-TBI Attention Deficits in Children

2020 ◽  
Author(s):  
Meng Cao ◽  
Yuyang Luo ◽  
Ziyan Wu ◽  
Catherine A. Mazzola ◽  
Arlene Goodman ◽  
...  
Keyword(s):  
Temporal Lobe ◽  
Critical Role ◽  
Brain Network ◽  
Attention Problems ◽  
Clustering Coefficient ◽  
Attention Deficits ◽  
Topological Properties ◽  
Local Efficiency ◽  
Structural Brain Network ◽  
Structural Brain

ABSTRACTTraumatic brain injury (TBI)-induced attention deficits are among the most common long-term cognitive consequences in children. Most of the existing studies attenpting to understand the neuropathological underpinnings of cognitive and behavioral impairments in TBI have utilized heterogeneous samples and resulted in inconsistent findings. The current research proposed to investigate topological properties of the structural brain network in children with TBI and their associations with TBI-induced attention problems in a more homogeneous subgroup of children who had severe post-TBI attention deficits (TBI-A).A total of 31 children with TBI-A and 35 group-matched controls were involved in the study. Diffusion tensor imaging-based probabilistic tractography and graph theoretical techniques were used to construct the structural brain network in each subject. Network topological properties were calculated in both global level and regional (nodal) level. Between-group comparisons among the topological network measures and analyses for searching brain-behavioral associations were all corrected for multiple comparisons using Bonferroni method.Compare to controls, the TBI-A group showed significantly higher nodal local efficiency and nodal clustering coefficient in left inferior frontal gyrus and right transverse temporal gyrus, while significantly lower nodal clustering coefficient in left supramarginal gyrus as well as lower nodal local efficiency in left parahippocampal gyrus. The temporal lobe topological alterations were significantly associated with the post-TBI inattentive and hyperactive symptoms in the TBI-A group.The results suggest that TBI-related structural re-modularity in the WM subnetworks associated with temporal lobe may play a critical role in the onset of severe post-TBI attention deficits in children. These findings provide valuable input for understanding the neurobiological substrates of TBI-A, and have the potential to serve as a biomarker guiding the development of more timely and tailored strategies for diagnoses and treatments to the affected individuals.

scholarly journals A study of brain functional network and alertness changes in temporal lobe epilepsy with and without focal to bilateral tonic–clonic seizures

BMC Neurology ◽  
2022 ◽  
Vol 22 (1) ◽  
Author(s):  
Liluo Nie ◽  
Yanchun Jiang ◽  
Zongxia Lv ◽  
Xiaomin Pang ◽  
Xiulin Liang ◽  
...  
Keyword(s):  
Temporal Lobe Epilepsy ◽  
Temporal Lobe ◽  
Information Integration ◽  
Refractory Epilepsy ◽  
Brain Network ◽  
Clustering Coefficient ◽  
Global Network ◽  
Seizure Type ◽  
Topological Properties ◽  
Clonic Seizures

Abstract Background Temporal lobe epilepsy (TLE) is commonly refractory. Epilepsy surgery is an effective treatment strategy for refractory epilepsy, but patients with a history of focal to bilateral tonic-clonic seizures (FBTCS) have poor outcomes. Previous network studies on epilepsy have found that TLE and idiopathic generalized epilepsy with generalized tonic-clonic seizures (IGE-GTCS) showed altered global and nodal topological properties. Alertness deficits also were found in TLE. However, FBTCS is a common type of seizure in TLE, and the implications for alertness as well as the topological rearrangements associated with this seizure type are not well understood. Methods We obtained rs-fMRI data and collected the neuropsychological assessment data from 21 TLE patients with FBTCS (TLE- FBTCS), 18 TLE patients without FBTCS (TLE-non- FBTCS) and 22 controls, and constructed their respective functional brain networks. The topological properties were analyzed using the graph theoretical approach and correlations between altered topological properties and alertness were analyzed. Results We found that TLE-FBTCS patients showed more serious impairment in alertness effect, intrinsic alertness and phasic alertness than the patients with TLE-non-FBTCS. They also showed significantly higher small-worldness, normalized clustering coefficient (γ) and a trend of higher global network efficiency (gE) compared to TLE-non-FBTCS patients. The gE showed a significant negative correlation with intrinsic alertness for TLE-non-FBTCS patients. Conclusion Our findings show different impairments in brain network information integration, segregation and alertness between the patients with TLE-FBTCS and TLE-non-FBTCS, demonstrating that impairments of the brain network may underlie the disruptions in alertness functions.

scholarly journals Structural brain network abnormalities and the probability of seizure recurrence after epilepsy surgery

Neurology ◽  
2020 ◽  
pp. 10.1212/WNL.0000000000011315
Author(s):  
Nishant Sinha ◽  
Yujiang Wang ◽  
Nádia Moreira da Silva ◽  
Anna Miserocchi ◽  
Andrew W. McEvoy ◽  
...  
Keyword(s):  
Temporal Lobe ◽  
Clinical Data ◽  
Structural Mri ◽  
Brain Network ◽  
Patient Specific ◽  
Seizure Freedom ◽  
Seizure Recurrence ◽  
Post Surgery ◽  
Structural Brain Network ◽  
Structural Brain

Objective:We assessed pre-operative structural brain networks and clinical characteristics of patients with drug resistant temporal lobe epilepsy (TLE) to identify correlates of post-surgical seizure recurrences.Methods:We examined data from 51 TLE patients who underwent anterior temporal lobe resection (ATLR) and 29 healthy controls. For each patient, using the pre-operative structural, diffusion, and post-operative structural MRI, we generated two networks: ‘pre-surgery’ network and ‘surgically-spared’ network. Standardising these networks with respect to controls, we determined the number of abnormal nodes before surgery and expected to be spared by surgery. We incorporated these 2 abnormality measures and 13 commonly acquired clinical data from each patient in a robust machine learning framework to estimate patient-specific chances of seizures persisting after surgery.Results:Patients with more abnormal nodes had lower chance of complete seizure freedom at 1 year and even if seizure-free at 1 year, were more likely to relapse within five years. The number of abnormal nodes was greater and their locations more widespread in the surgically-spared networks of poor outcome patients than in good outcome patients. We achieved 0.84±0.06 AUC and 0.89±0.09 specificity in predicting unsuccessful seizure outcomes (ILAE3-5) as opposed to complete seizure freedom (ILAE1) at 1 year. Moreover, the model-predicted likelihood of seizure relapse was significantly correlated with the grade of surgical outcome at year-one and associated with relapses up-to five years post-surgery.Conclusion:Node abnormality offers a personalised non-invasive marker, that can be combined with clinical data, to better estimate the chances of seizure freedom at 1 year, and subsequent relapse up to 5 years after ATLR.Classification of evidence:This study provides Class II evidence that node abnormality predicts post-surgical seizure recurrence.

scholarly journals Structural brain network topology underpinning ADHD and response to methylphenidate treatment

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Kristi R. Griffiths ◽  
Taylor A. Braund ◽  
Michael R. Kohn ◽  
Simon Clarke ◽  
Leanne M. Williams ◽  
...  
Keyword(s):  
Brain Network ◽  
Global Network ◽  
Support Vector ◽  
Structural Topology ◽  
Local Efficiency ◽  
Structural Brain Network ◽  
Structural Architecture ◽  
The Right ◽  
Tractography Data ◽  
Treatment Prognosis

AbstractBehavioural disturbances in attention deficit hyperactivity disorder (ADHD) are thought to be due to dysfunction of spatially distributed, interconnected neural systems. While there is a fast-growing literature on functional dysconnectivity in ADHD, far less is known about the structural architecture underpinning these disturbances and how it may contribute to ADHD symptomology and treatment prognosis. We applied graph theoretical analyses on diffusion MRI tractography data to produce quantitative measures of global network organisation and local efficiency of network nodes. Support vector machines (SVMs) were used for comparison of multivariate graph measures of 37 children and adolescents with ADHD relative to 26 age and gender matched typically developing children (TDC). We also explored associations between graph measures and functionally-relevant outcomes such as symptom severity and prediction of methylphenidate (MPH) treatment response. We found that multivariate patterns of reduced local efficiency, predominantly in subcortical regions (SC), were able to distinguish between ADHD and TDC groups with 76% accuracy. For treatment prognosis, higher global efficiency, higher local efficiency of the right supramarginal gyrus and multivariate patterns of increased local efficiency across multiple networks at baseline also predicted greater symptom reduction after 6 weeks of MPH treatment. Our findings demonstrate that graph measures of structural topology provide valuable diagnostic and prognostic markers of ADHD, which may aid in mechanistic understanding of this complex disorder.

scholarly journals The Overlapping Community Structure of Structural Brain Network in Young Healthy Individuals

PLoS ONE ◽  
2011 ◽  
Vol 6 (5) ◽  
pp. e19608 ◽  
Author(s):  
Kai Wu ◽  
Yasuyuki Taki ◽  
Kazunori Sato ◽  
Yuko Sassa ◽  
Kentaro Inoue ◽  
...  
Keyword(s):  
Community Structure ◽  
Brain Network ◽  
Healthy Individuals ◽  
Overlapping Community ◽  
Structural Brain Network ◽  
Structural Brain

scholarly journals Integrative Structural Brain Network Analysis in Diffusion Tensor Imaging

2017 ◽  
Vol 7 (6) ◽  
pp. 331-346 ◽  
Author(s):  
Moo K. Chung ◽  
Jamie L. Hanson ◽  
Nagesh Adluru ◽  
Andrew L. Alexander ◽  
Richard J. Davidson ◽  
...  
Keyword(s):  
Diffusion Tensor Imaging ◽  
Network Analysis ◽  
Diffusion Tensor ◽  
Brain Network ◽  
Structural Brain Network ◽  
Structural Brain
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