Auditory Verbal Hallucinations (AVH) are the most common type of hallucination in schizophrenia (SZ), experienced by more than 70% of patients, making them a conspicuous target for treatment. AVH treatment with antipsychotic medications can decrease or resolve the symptom, but often at the cost of significant side effects. Additionally, AVH are resistant to pharmacological treatment in 25-30% of patients, rendering this a costly and only partially effective treatment. The present study is designed to be a necessary first step toward the goal of developing effective and non-invasive treatments for AVH. Transcranial direct current stimulation (tDCS) is receiving increasing interest for this purpose, as it relies on a very low energy deposition, is inexpensive to administer, has few side effects, is relatively portable, and is easy to use. tDCS involves neuromodulation by means of constant, low intensity current delivered directly to the brain area of interest via small electrodes placed on the scalp. A recent carefully controlled, double-blind, tDCS treatment trial with randomization to either active treatment or sham groups showed a dramatic reduction in AVH intensity. However, optimized use of any neuromodulatory strategy requires definition of appropriate neuronal targets for modulation. One goal of this project is therefore to refine methods for specifying the spatio-temporal dynamics of the AVH network on a subject-by-subject basis using multi-modal (fMRI+MEG) data analysis and fusion techniques developed by investigators on our COBRE team. A second goal is to apply our knowledge of the AVH neural networks to a trial of tDCS treatment for AVH and determine how tDCS affects AVH nehworks to result in reduction in frequency, duration and intensity of AVH.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Exploratory Grants (P20)
Project #
5P20GM103472-07
Application #
8708153
Study Section
Special Emphasis Panel (ZGM1-TWD-Y)
Project Start
Project End
Budget Start
2014-05-01
Budget End
2015-04-30
Support Year
7
Fiscal Year
2014
Total Cost
$239,026
Indirect Cost
$58,947
Name
The Mind Research Network
Department
Type
DUNS #
098640696
City
Albuquerque
State
NM
Country
United States
Zip Code
87106
Fu, Zening; Tu, Yiheng; Di, Xin et al. (2018) Characterizing dynamic amplitude of low-frequency fluctuation and its relationship with dynamic functional connectivity: An application to schizophrenia. Neuroimage 180:619-631
Zille, Pascal; Calhoun, Vince D; Stephen, Julia M et al. (2018) Fused Estimation of Sparse Connectivity Patterns From Rest fMRI-Application to Comparison of Children and Adult Brains. IEEE Trans Med Imaging 37:2165-2175
Xiao, Li; Stephen, Julia M; Wilson, Tony W et al. (2018) Alternating Diffusion Map Based Fusion of Multimodal Brain Connectivity Networks for IQ Prediction. IEEE Trans Biomed Eng :
Bridwell, David A; Henderson, Sarah; Sorge, Marieke et al. (2018) Relationships between alpha oscillations during speech preparation and the listener N400 ERP to the produced speech. Sci Rep 8:12838
Jiang, Rongtao; Abbott, Christopher C; Jiang, Tianzi et al. (2018) SMRI Biomarkers Predict Electroconvulsive Treatment Outcomes: Accuracy with Independent Data Sets. Neuropsychopharmacology 43:1078-1087
van Erp, Theo G M; Walton, Esther; Hibar, Derrek P et al. (2018) Cortical Brain Abnormalities in 4474 Individuals With Schizophrenia and 5098 Control Subjects via the Enhancing Neuro Imaging Genetics Through Meta Analysis (ENIGMA) Consortium. Biol Psychiatry 84:644-654
Faghiri, Ashkan; Stephen, Julia M; Wang, Yu-Ping et al. (2018) Changing brain connectivity dynamics: From early childhood to adulthood. Hum Brain Mapp 39:1108-1117
Alam, Md Ashad; Lin, Hui-Yi; Deng, Hong-Wen et al. (2018) A kernel machine method for detecting higher order interactions in multimodal datasets: Application to schizophrenia. J Neurosci Methods 309:161-174
Chen, Jiayu; Hutchison, Kent E; Bryan, Angela D et al. (2018) Opposite Epigenetic Associations With Alcohol Use and Exercise Intervention. Front Psychiatry 9:594
Anderson, Nathaniel E; Harenski, Keith A; Harenski, Carla L et al. (2018) Machine learning of brain gray matter differentiates sex in a large forensic sample. Hum Brain Mapp :

Showing the most recent 10 out of 222 publications