Biology subjects

Balderston, N. L.

Publications and source records attributed to Balderston, N. L..

4 recordsLinked to original sources

bioRxiv2026

Focal brain stimulation sites that modify autonomic arousal map to a convergent brain circuit and potential therapeutic target

Causal modulation of autonomic outflow could yield new therapeutic targets for autonomic hyperactivation. We employed three natural experiments in which different brain regions were targeted using transcranial magnetic stimulation (TMS) (n=139 sites, n=14 individuals), deep brain stimulation (n=392 sites, n=58 individuals), or low-intensity focused ultrasound (n=46 sites, n=23 individuals) with subsequent autonomic measurements. Using a human connectome database (n=1000) as a wiring diagram, we identified a convergent brain circuit that, when focally modulated, transiently reduces autonomic arousal. This circuit significantly resembled previously reported causal circuits for posttraumatic stress disorder (PTSD) and anxiety. In independent datasets, TMS to the autonomic arousal circuit reduced laboratory startle in healthy volunteers (n=28), lesions to this circuit reduced exaggerated startle in PTSD (n=193), and TMS to this circuit reduced anxiety-related autonomic symptoms in patients with clinically significant anxiety (n=30). Thus, the convergent circuit may serve as a potential neuromodulation target for autonomic hyperactivation.

neuroscience↗

bioRxiv2024

pNet: A toolbox for personalized functional networks modeling

Personalized functional networks (FNs) derived from functional magnetic resonance imaging (fMRI) data are useful for characterizing individual variations in the brain functional topography associated with the brain development, aging, and disorders. To facilitate applications of the personalized FNs with enhanced reliability and reproducibility, we develop an open-source toolbox that is user-friendly, extendable, and includes rigorous quality control (QC), featuring multiple user interfaces (graphics, command line, and a step-by-step guideline) and job-scheduling for high performance computing (HPC) clusters. Particularly, the toolbox, named personalized functional network modeling (pNet), takes fMRI inputs in either volumetric or surface type, ensuring compatibility with multiple fMRI data formats, and computes personalized FNs using two distinct modeling methods: one method optimizes the functional coherence of FNs, while the other enhances their independence. Additionally, the toolbox provides HTML-based reports for QC and visualization of personalized FNs. The toolbox is developed in both MATLAB and Python platforms with a modular design to facilitate extension and modification by users familiar with either programming language. We have evaluated the toolbox on two fMRI datasets and demonstrated its effectiveness and user-friendliness with interactive and scripting examples. pNet is publicly available at https://github.com/MLDataAnalytics/pNet.

neuroscience↗

bioRxiv2021

Cortical-subcortical structural connections support transcranial magnetic stimulation engagement of the amygdala

The amygdala processes valenced stimuli, influences affective states, and exhibits aberrant activity across anxiety disorders, depression, and PTSD. Interventions that modulate amygdala activity hold promise for treating transdiagnostic affective symptoms. We investigated (N=45) whether transcranial magnetic stimulation (TMS) elicits indirect changes in amygdala activity when applied to ventrolateral prefrontal cortex (vlPFC), a region important for affect regulation. Harnessing in-scanner interleaved TMS/functional MRI (fMRI), we reveal that vlPFC neurostimulation evoked acute, dose-dependent modulations of amygdala fMRI BOLD signal. Larger TMS-evoked changes in amygdala fMRI signal were associated with higher fiber density in a vlPFC-amygdala white matter pathway, suggesting this pathway facilitated stimulation-induced communication between cortex and subcortex. This work provides evidence of amygdala engagement by TMS, highlighting stimulation of vlPFC-amygdala circuits as a candidate treatment for affective psychopathology. More broadly, it indicates that targeting cortical-subcortical connections may enhance the impact of TMS on subcortical neural activity and, by extension, subcortex-subserved behaviors. TeaserIndividualized, connectivity-guided transcranial magnetic stimulation modulates the amygdala, demonstrating therapeutic potential.

neuroscience↗

bioRxiv2020

Development of a novel connectivity-based electric-field modelling approach for individualized targeting of transcranial magnetic stimulation treatment.

BackgroundResting state functional connectivity (rsFC) offers promise for individualizing stimulation targets for transcranial magnetic stimulation (TMS) treatments. However current targeting approaches do not account for non-focal TMS effects or large-scale connectivity patterns. To overcome these limitations, we propose a novel targeting optimization approach that combines whole-brain rsFC and electric-field (e-field) modelling to identify single-subject, symptom-specific TMS targets. MethodsIn this proof of concept study, we recruited 91 anxious misery (AM) patients and 25 controls. We measured depression symptoms (MADRS/HAMD) and recorded rsFC. We used a PCA regression to predict symptoms from rsFC and estimate the parameter vector, for input into our e-field augmented model. We modeled 17 left dlPFC and 7 M1 sites using 24 equally spaced coil orientations. We computed single-subject predicted {Delta}MADRS/HAMD scores for each site/orientation using the e-field augmented model, which comprises a linear combination of the following elementwise products 1) the estimated connectivity/symptom coefficients, 2) a vectorized e-field model for site/orientation, 3) the pre-treatment rsFC matrix, scaled by a proportionality constant. ResultsIn AM patients, our pre-stimulation connectivity-based model predicted a significant decrease depression for sites near BA46, but not M1 for coil orientations perpendicular to the cortical gyrus. In control subjects, no site/orientation combination showed a significant predicted change. DiscussionThese results corroborate previous work suggesting the efficacy of left dlPFC stimulation for depression treatment, and predict better outcomes with individualized targeting. They also suggest that our novel connectivity-based e-field modelling approach may effectively identify potential TMS treatment responders and individualize TMS targeting to maximize the therapeutic impact.

neuroscience↗