Ms. Sravani Varanasi Profile

Sravani Varanasi

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Publications (8)

Proceedings Article | 6 June 2024 Presentation + Paper

Extracting functional connectivity signatures in substance use disorder using energy landscape analysis

Sravani Varanasi, Tianye Zhai, Hong Gu, Yihong Yang, Fow-Sen Choa

Proceedings Volume 13059, 1305909 (2024) https://doi.org/10.1117/12.3013694

KEYWORDS: Brain, Thalamus, Brain diseases, Cerebellum, Visualization, Functional magnetic resonance imaging, Statistical analysis, Control systems, Neurological disorders, Neuroimaging

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Substance Use Disorder (SUD) is a complex condition with profound effects on brain function. Understanding the altered functional connectivity patterns in the brains of SUD patients is crucial for unraveling the neurological underpinnings of this disorder. This study employs Energy Landscape Analysis, an energy-based machine learning technique, to investigate whole brain Regions of Interest (ROI) functional connectivity differences between SUD patients and healthy controls. The challenge with Energy Landscape Analysis lies in selecting the appropriate ROI from the extensive brain atlas. In this study, seed-based connectivity was utilized to identify relevant ROIs, overcoming the limitation of analyzing only a limited number of ROIs. The dataset comprised 53 cocaine users and 52 age- and sex-matched healthy controls, with fMRI data preprocessed using the CONN toolbox. ROI-ROI seed-based pair connectivity was derived through first and second level analyses. The identified sub-ROIs were categorized into default CONN network affiliations and bundled into Superior Temporal Gyrus (STG), Inferior Temporal Gyrus, temporooccipital part (toITG), Visual Primary (VIS-P), Auditory (AUD), Cerebellum, Basal Ganglia (BSL), and Thalamus (THL). Significance testing revealed eight connectivity states among all above regions with p-values that satisfy Bonferroni correction between controls and patients. Notably, the connectivity states with the lowest p-values revealed a distinctive pattern: STG (auditory attention) toITG were disconnected from the rest of the networks. This finding underscores the importance of investigating specific network disruptions in SUD, shedding light on potential neural mechanisms underlying the disorder. In summary, our study utilizes Energy Landscape Analysis to explore whole brain ROI functional connectivity in SUD, revealing disrupted connectivity patterns that may have implications for understanding the neural basis of this disorder. These findings may ultimately inform targeted interventions and treatment strategies for individuals with SUD.

Proceedings Article | 6 June 2024 Presentation + Paper

Functional connectivity differences between cocaine users and healthy controls: an fMRI study

Sravani Varanasi, Tianye Zhai, Hong Gu, Yihong Yang, Fow-Sen Choa

Proceedings Volume 13059, 1305908 (2024) https://doi.org/10.1117/12.3013689

KEYWORDS: Functional magnetic resonance imaging, Brain diseases, Brain, Mental disorders, Cerebellum, Control systems, Visualization, Basal ganglia, Thalamus, Statistical analysis

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Proceedings Article | 8 June 2022 Presentation + Paper

Flexible energy landscape analysis of full brain functional connectivity through region bundling

Janerra Allen, Sravani Varanasi, Elliot Hong, Fow-Sen Choa

Proceedings Volume 12122, 1212215 (2022) https://doi.org/10.1117/12.2619126

KEYWORDS: Brain, Thalamus, Functional magnetic resonance imaging, Basal ganglia, Statistical analysis, Sensors, Sensor networks, Brain mapping, Analytical research, Microelectromechanical systems

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Proceedings Article | 8 June 2022 Presentation + Paper

Energy landscape analysis based sliding window studies of brain dynamics in young and old subjects

Sravani Varanasi, Janerra Allen, Rong Chen, Karuna Sahoo, Amit Patra, Fow-Sen Choa

Proceedings Volume 12122, 1212212 (2022) https://doi.org/10.1117/12.2618951

KEYWORDS: Brain, Functional magnetic resonance imaging, Visualization, Microelectromechanical systems, Sensors, Control systems, Thalamus, Statistical analysis, Sensor networks, Electrical engineering

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Proceedings Article | 8 June 2022 Paper

Three-dimension EEG-based connectivity biomarkers for neurological disorder detections

Deepa Gupta, Xiaoming Du, Jaganathan Velraj, Sravani Varanasi, Elliot Hong, Fow-Sen Choa

Proceedings Volume 12123, 1212309 (2022) https://doi.org/10.1117/12.2618841

KEYWORDS: Electroencephalography, Brain, Sensors, Sensor networks, Source localization, Data modeling, Signal processing, Neuroimaging, Statistical analysis, Neuroscience, Computational neuroscience, Cognitive neuroscience, Machine learning

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Proceedings Article | 8 June 2022 Paper

Efficacy of ferromagnetic cores on the performance of TMS coils

Hedyeh Bagherzadeh, Sravani Varanasi, Jean-Paul Badjo, Fow-Sen Choa

Proceedings Volume 12123, 121230B (2022) https://doi.org/10.1117/12.2618828

KEYWORDS: Ferromagnetics, Finite element methods, Brain, Magnetism, Brain stimulation, Signal detection, Prototyping, Optical spheres, Distance measurement, Clinical research

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Proceedings Article | 12 April 2021 Presentation + Paper

Energy landscape analysis of fMRI data from Schizophrenic and healthy subjects

Janerra Allen, Sravani Varanasi, Elliot Hong, Fow-Sen Choa

Proceedings Volume 11756, 1175610 (2021) https://doi.org/10.1117/12.2588046

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Proceedings Article | 12 April 2021 Presentation + Paper

Sliding window study of brain connectivity dynamics based on Energy Landscape analysis.

Sravani Varanasi, Janerra Allen, Rong Chen, Fow-Sen Choa

Proceedings Volume 11756, 117560Z (2021) https://doi.org/10.1117/12.2588048

KEYWORDS: Brain, Visualization, Statistical analysis, Sensors, Visual process modeling, Sensor networks, Functional magnetic resonance imaging, Computational neuroscience, Cognitive modeling

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Showing 5 of 8 publications

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