Predicting anxiety with brain connectivity

Thursday, 09 de April de 2020

Un nuevo estudio propone una manera de predecir el nivel de anciedad en individuos, abriendo una nueva aplicación tecnológica de las neurociencias para investigar los cambios comportamentales durante esta crisis.

Sustained anxiety is a risk factor for mental disorders and early identification would avoid large losses for both individuals and society (Social Interaction). Recently, resting-state fMRI is a promising technique for clinical diagnosis and prior brain-imaging studies documented the important role of limbic and prefrontal lobe in anxiety by collapsing group data (Behavioral Research Lab, Bienestar). However, little is known about how to predict anxiety individually.

Methods:

Here, they applied a connectome-based predictive model (CPM, Fig. 1) (Functional Connectivity) to predict anxiety levels of 76 participants with whole-brain resting-state functional connectivity (rsFC, For MRI) and anxiety scores (Beck Anxiety Inventory, BAI). Note that 268 ×268 rsFC matrices were constructed by a 268-node functional atlas, which could be divided into 10 lobes (Fig. 2D) (Feng et al., 2018; Shen, Tokoglu, Papademetris, & Constable, 2013).

To check the different contributions of each lobe, they implemented lesion predictions in the main lobes. Afterward, they compared whole-brain prediction with lesion prediction in terms of prediction power. Finally, because anxiety and depression are co-occurring and sharing similar brain networks, they compared different brain networks between them. Specifically, they separately selected the top 55% in anxiety and depression (measured by self-rating depression scale, SDS) and excluded participants high both in anxiety and depression. The remaining participants, anxiety-specific group (8) and depression-specific group (12), were compared with a cross-validation method and they extracted different brain networks (Takagi et al., 2018).

Results:

Results revealed that this model could validly predict anxiety in novel participants from the perspective of whole-brain rsFC (r(74) = 0.33, p = 0.004, Fig. 2B), especially in prefrontal (PFC) and limbic lobe (Lim). Permutation test revealed that the result was not expected by chance (1000 times, P = 0.036, Fig. 2C). Additionally, their results still exist after scrubbing (FD_Jenkinson 0.2, r(74) = 0.28, p = 0.015) and controlling head motion (r(74) = 0.33, p = 0.004), age (r(74) = 0.30, p = 0.008) and gender (r(74) = 0.32, p = 0.005).

Conclusions:

These results demonstrated that individualized anxiety could be predicted from whole-brain rsFC and highlighted the indispensable role of Lim in anxiety prediction. This work offers potential biomarkers for early identification in subclinical population and clinical assessment in anxiety disorders.

References

1. Zhihao Wang, Pengfei Xu, Yuejia Luo. (2019). Predicting anxiety in novel individuals from whole-brain resting-state functional connectivity

2. Feng, C. (2018). ‘Individualized prediction of trait narcissism from whole-brain resting-state functional connectivity’, Human brain mapping.

3. Grupe, D. W. (2013). ‘Uncertainty and anticipation in anxiety: an integrated neurobiological and psychological perspective’, Nature Reviews Neuroscience, 14(7), 488.

4. Shen, X. (2017). 'Using connectome-based predictive modeling to predict individual behavior from brain connectivity', Nature Protocols, 12(3), 506.

5. Shen, X. (2013). 'Groupwise whole-brain parcellation from resting-state fMRI data for network node identification', Neuroimage, 82, 403-415.

6. Takagi, Y. (2018). 'A common brain network among state, trait, and pathological anxiety from whole-brain functional connectivity', Neuroimage, 172, 506-516.

The content published here is the exclusive responsibility of the authors.

Autor: Sebastian Moguilner

#brainstimulation #socialinteraction #bienestarwellnessbemestar #attentioncontrolconsciousness #stresslearningbullying #humancompetence #socialpreferences #culturalneuroscience #brainstimulation #socialinteraction #bienestarwellnessbemestar #attentioncontrolconsciousness #stresslearningbullying #humancompetence #socialpreferences #culturalneuroscience