Functional brain network modeling in sub-acute stroke patients and healthy controls during rest and continuous attentive tracking

Study of functional brain network (FBN) based on functional magnetic resonance imaging (fMRI) has proved successful in depression disorder classification. One popular approach to construct FBN is Pearson correlation. However, it only captures pairwise relationship between brain regions, while it ignores the influence of other brain regions. Another common issue existing in many depression disorder classification methods is applying only single local feature extracted from constructed FBN. To address these issues, we develop a new method to classify fMRI data of patients with depression and healthy controls. First, we construct the FBN using a sparse low-rank model, which considers the relationship between two brain regions given all the other brain regions. Moreover, it can automatically remove weak relationship and retain the modular structure of FBN. Secondly, FBN are effectively measured by eight graph-based features from different aspects. Tested on fMRI data of 31 patients with depression and 29 healthy controls, our method achieves 95% accuracy, 96.77% sensitivity, and 93.10% specificity, which outperforms the Pearson correlation FBN and sparse FBN. In addition, the combination of graph-based features in our method further improves classification performance. Moreover, we explore the discriminative brain regions that contribute to depression disorder classification, which can help understand the pathogenesis of depression disorder.

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Two-Stage Spatial Temporal Deep Learning Framework For Functional Brain Network Modeling

2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019) ◽

10.1109/isbi.2019.8759503 ◽

2019 ◽

Author(s):

Yu Zhao ◽

Haixing Dai ◽

Wei Zhang ◽

Fangfei Ge ◽

Tianming Liu

Keyword(s):

Deep Learning ◽

Network Modeling ◽

Brain Network ◽

Two Stage ◽

Functional Brain ◽

Learning Framework ◽

Functional Brain Network

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The Insula Is a Hub for Functional Brain Network in Patients With Anti-N-Methyl-D-Aspartate Receptor Encephalitis

Frontiers in Neuroscience ◽

10.3389/fnins.2021.642390 ◽

2021 ◽

Vol 15 ◽

Author(s):

Chunyan Li ◽

Xiaomin Pang ◽

Ke Shi ◽

Qijia Long ◽

Jinping Liu ◽

...

Keyword(s):

Parietal Lobe ◽

Brain Network ◽

Clustering Coefficient ◽

Hamilton Depression Scale ◽

Healthy Controls ◽

Functional Brain ◽

Aspartate Receptor ◽

Nmdar Encephalitis ◽

Functional Brain Network ◽

Left Insula

BackgroundIn recent years, imaging technologies have been rapidly evolving, with an emphasis on the characterization of brain structure changes and functional imaging in patients with autoimmune encephalitis. However, the neural basis of anti-N-methyl-D-aspartate receptor (NMDAR) encephalitis and its linked cognitive decline is unclear. Our research aimed to assess changes in the functional brain network in patients with anti-NMDAR encephalitis and whether these changes lead to cognitive impairment.MethodsTwenty-one anti-NMDAR encephalitis patients and 22 age-, gender-, and education status-matched healthy controls were assessed using resting functional magnetic resonance imaging (fMRI) scanning and neuropsychological tests, including the Hamilton Depression Scale (HAMD24), the Montreal Cognitive Assessment (MoCA), and the Hamilton Anxiety Scale (HAMA). A functional brain network was constructed using fMRI, and the topology of the network parameters was analyzed using graph theory. Next, we extracted the aberrant topological parameters of the functional network as seeds and compared causal connectivity with the whole brain. Lastly, we explored the correlation of aberrant topological structures with deficits in cognitive performance.ResultsRelative to healthy controls, anti-NMDAR encephalitis patients exhibited decreased MoCA scores and increased HAMA and HAMD24 scores (p < 0.05). The nodal clustering coefficient and nodal local efficiency of the left insula (Insula_L) were significantly decreased in anti-NMDAR encephalitis patients (p < 0.05 following Bonferroni correction). Moreover, anti-NMDAR encephalitis patients showed a weakened causal connectivity from the left insula to the left inferior parietal lobe (Parietal_Inf_L) compared to healthy controls. Conversely, the left superior parietal lobe (Parietal_sup_L) exhibited an enhanced causal connectivity to the left insula in anti-NMDAR encephalitis patients compared to controls. Unexpectedly, these alterations were not correlated with any neuropsychological test scores.ConclusionThis research describes topological abnormalities in the functional brain network in anti-NMDAR encephalitis. These results will be conducive to understand the structure and function of the brain network of patients with anti-NMDAR encephalitis and further explore the neuropathophysiological mechanisms.

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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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Study of the activation in sensorimotor cortex and topological properties of functional brain network following focal vibration on healthy subjects and subacute stroke patients: An EEG study

Brain Research ◽

10.1016/j.brainres.2019.146338 ◽

2019 ◽

Vol 1722 ◽

pp. 146338 ◽

Cited By ~ 2

Author(s):

Wei Li ◽

Chong Li ◽

Yun Xiang ◽

Linhong Ji ◽

Hui Hu ◽

...

Keyword(s):

Sensorimotor Cortex ◽

Healthy Subjects ◽

Brain Network ◽

Topological Properties ◽

Stroke Patients ◽

Functional Brain ◽

Subacute Stroke ◽

Functional Brain Network

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Electroencephalography-based functional brain network analysis according to lesion location and motor function in stroke patients

10.21203/rs.2.20942/v1 ◽

2020 ◽

Author(s):

Da-Hye Kim ◽

Gyu Hyun Kwon ◽

Wanjoo Park ◽

Yun-Hee Kim ◽

Seong-Whan Lee ◽

...

Keyword(s):

Brain Networks ◽

Brain Network ◽

Control Group ◽

Stroke Patients ◽

Functional Brain ◽

Lesion Location ◽

Functional Brain Network ◽

Functional Brain Networks ◽

Stroke Lesion ◽

The Brain

Abstract Background. While numerous studies have investigated changes in brain activation after stroke, limited information exists on the association between functional brain networks and lesion location in stroke patients. Methods. We compared the characteristics of brain networks among patients with cortico-subcortical lesions (n = 5), subcortical lesions (n = 7), and age-matched healthy controls (n = 12) during the execution of hand movements. Functional brain networks were analyzed based on network parameters in beta frequency electroencephalography (EEG) bands. Results. Our results indicated that while the healthy control group had appropriate compensatory patterns on the brain network with an aging effect, the two stroke lesion groups exhibited different hyper-connected characteristics in the brain network within the sensorimotor regions, particularly the contralesional M1, during motor execution. In addition, the betweenness centrality on the contralesional motor area was identified as a promising biomarker for motor functional ability associated with stroke. Our findings further allowed us to identify the characteristics of the stroke lesion that could not be found with EEG power by using the EEG brain network on the cerebral cortex. Conclusions. We anticipate that our study will improve the understanding of the complex changes that occur in the brain network as a result of stroke, and support the development of more effective and efficient rehabilitation programs based on lesion location for stroke patients.

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