Biology subjects

Blumberger, D. M.

Publications and source records attributed to Blumberger, D. M..

7 recordsLinked to original sources

bioRxiv2026

Objective Quality Assessment for Precision Functional MRI Data

Precision functional mapping (PFM) enables individual-level characterization of brain network organization but requires substantially more and higher-quality fMRI data than is standard. Despite its growing use, objective criteria for data sufficiency and quality needed to ensure interpretable and replicable individual-level results remain unclear. Here, we introduce the Network Similarity Index (NSI), an objective measure of the extent to which functional connectivity (FC) patterns in an individual dataset express the large-scale network structure required for PFM. NSI captures the integrity of low-spatial-frequency, coherent network organization and denoising fidelity, and aligns closely with blinded expert assessments of PFM usability. NSI also accounts for variability in the rate at which FC becomes reliable across individuals. Here, we provide an open-source framework for NSI-based data quality evaluation and models for linking NSI values with expert-judged PFM suitability. This framework can also inform expected returns from additional data collection, enabling principled decisions about data sufficiency and replication in precision fMRI research.

neuroscience↗

bioRxiv2025

Proximity to an SGC-DLPFC Individualized Functional Target and outcomes in large rTMS clinical trials for Treatment-Resistant Depression

BackgroundTargeting methods for repetitive transcranial magnetic stimulation (rTMS) in patients with depression now include the use of individual functional scans to target specific functional connectivity (FC) patterns obtained from functional magnetic resonance imaging (fMRI). Potential biomarkers of rTMS response include target FC with the subgenual anterior cingulate cortex (SGC) or the causal depression circuit (CDC), each of which may be candidates for individualized functional targets (iFTs). We assessed the relationship of these two approaches to clinical outcomes in two large rTMS clinical trials. Methods501 subjects with moderate to severe depression underwent 4-6 weeks of daily rTMS to the left dorsolateral prefrontal cortex (DLPFC), targeted using neuronavigation to a common group-based functional target. Resting-state scans acquired at baseline were used to retrospectively compute iFTs using either SGC-DLPFC or CDC-DLPFC FC. The Euclidean distance from the group-based target used in the trial to the centre of gravity of each iFT was computed and correlated with outcomes. ResultsMost subjects iFTs were within 2cm of their group-based target. Proximity to either the SGC- or CDC-iFT was not associated with better outcomes. Sensitivity analyses accounting for treatment target FC, methodology, data quality, or treatment parameters did not change the results. ConclusionsProximity to SGC- or CDC-derived iFTs was not associated with better outcomes in patients who received neuronavigated rTMS to a group-based target. Prospective randomized clinical trials comparing neuronavigated group-based target to neuronavigated iFTs are needed.

neuroscience↗

bioRxiv2025

Excitation-Inhibition Balance and Fronto-Limbic Connectivity Drive TMS Treatment Outcomes in Refractory Depression

Depression affects over 350 million people worldwide, with treatment resistance occurring in up to 30% of cases. Intermittent theta burst stimulation (iTBS) targeting the left dorsolateral prefrontal cortex (DLPFC) has emerged as a promising intervention, yet the neurophysiological mechanisms determining which patients will respond remain poorly understood. Here, we combined transcranial magnetic stimulation with electroencephalography and whole-brain computational modeling to uncover the mechanistic basis of treatment efficacy in 90 patients with treatment-resistant depression. We identified two distinct neurophysiological signatures that differentiate responders from non-responders: (1) post-treatment shifts in excitation-inhibition balance toward greater inhibitory control, and (2) a pre-treatment brain state characterized by anticorrelated dynamics between subgenual anterior cingulate cortex and DLPFC. These features were significantly correlated with clinical improvement and could not be explained by non-specific factors. Our findings provide a neurophysiologically-informed framework for developing personalized and optimized neuromodulation approaches in treatment-resistant depression.

neuroscience↗

bioRxiv2024

Bayesian Optimization of Neurostimulation (BOONStim)

BackgroundTranscranial magnetic stimulation (TMS) treatment response is influenced by individual variability in brain structure and function. Sophisticated, user-friendly approaches, incorporating both established functional magnetic resonance imaging (fMRI) and TMS simulation tools, to identify TMS targets are needed. ObjectiveThe current study presents the development and validation of the Bayesian Optimization of Neuro-Stimulation (BOONStim) pipeline. MethodsBOONStim uses Bayesian optimization for individualized TMS targeting, automating interoperability between surface-based fMRI analytic tools and TMS electric field modeling. BOONStims Bayesian optimization performance was evaluated in a sample dataset (N=10) using standard circular and functional connectivity-defined targets, and compared to densely sampled grid optimization. ResultsBayesian optimization converged to similar levels of total electric field stimulation across targets in under 30 iterations, converging within 5% error of the maxima detected by grid optimization, and requiring less time. ConclusionsBOONStim is a scalable and configurable user-friendly pipeline for individualized TMS targeting with quick turnaround.

neuroscience↗

bioRxiv2023

Expansion of a frontostriatal salience network in individuals with depression

Hundreds of neuroimaging studies spanning two decades have revealed differences in brain structure and functional connectivity in depression, but with modest effect sizes, complicating efforts to derive mechanistic pathophysiologic insights or develop biomarkers.1 Furthermore, although depression is a fundamentally episodic condition, few neuroimaging studies have taken a longitudinal approach, which is critical for understanding cause and effect and delineating mechanisms that drive mood state transitions over time. The emerging field of precision functional mapping using densely-sampled longitudinal neuroimaging data has revealed unexpected, functionally meaningful individual differences in brain network topology in healthy individuals,2-5 but these approaches have never been applied to individuals with depression. Here, using precision functional mapping techniques and 11 datasets comprising n=187 repeatedly sampled individuals and >21,000 minutes of fMRI data, we show that the frontostriatal salience network is expanded two-fold in most individuals with depression. This effect was replicable in multiple samples, including large-scale, group-average data (N=1,231 subjects), and caused primarily by network border shifts affecting specific functional systems, with three distinct modes of encroachment occurring in different individuals. Salience network expansion was unexpectedly stable over time, unaffected by changes in mood state, and detectable in children before the subsequent onset of depressive symptoms in adolescence. Longitudinal analyses of individuals scanned up to 62 times over 1.5 years identified connectivity changes in specific frontostriatal circuits that tracked fluctuations in specific symptom domains and predicted future anhedonia symptoms before they emerged. Together, these findings identify a stable trait-like brain network topology that may confer risk for depression and mood-state dependent connectivity changes in frontostriatal circuits that predict the emergence and remission of depressive symptoms over time.

neuroscience↗

bioRxiv2023

Dimensional and Categorical Solutions to Parsing Depression Heterogeneity in a Large Single-Site Sample

BackgroundRecent studies have reported significant advances in modeling the biological basis of heterogeneity in major depressive disorder (MDD), but investigators have also identified important technical challenges, including scanner-related artifacts, a propensity for multivariate models to overfit, and a need for larger samples with deeper clinical phenotyping. The goals of this work were to develop and evaluate dimensional and categorical solutions to parsing heterogeneity in depression that are stable and generalizable in a large, deeply phenotyped, single-site sample. MethodsWe used regularized canonical correlation analysis (RCCA) to identify data-driven brain-behavior dimensions explaining individual differences in depression symptom domains in a large, single-site dataset comprising clinical assessments and resting state fMRI data for N=328 patients with MDD and N=461 healthy controls. We examined the stability of clinical loadings and model performance in held-out data. Finally, hierarchical clustering on these dimensions was used to identify categorical depression subtypes ResultsThe optimal RCCA model yielded three robust and generalizable brain-behavior dimensions explaining individual differences in depressed mood and anxiety, anhedonia, and insomnia. Hierarchical clustering identified four depression subtypes, each with distinct clinical symptom profiles, abnormal RSFC patterns, and antidepressant responsiveness to repetitive transcranial magnetic stimulation. ConclusionsOur results define dimensional and categorical solutions to parsing neurobiological heterogeneity in MDD that are stable, generalizable, and capable of predicting treatment outcomes, each with distinct advantages in different contexts. They also provide additional evidence that RCCA and hierarchical clustering are effective tools for investigating associations between functional connectivity and clinical symptoms.

neuroscience↗

bioRxiv2023

The Complexity of Functional Connectivity Profiles of the Subgenual Anterior Cingulate Cortex and Dorsal Lateral Prefrontal Cortex in Major Depressive Disorder: a DIRECT Consortium Study

BackgroundThe subgenual anterior cingulate cortex (sgACC) plays a central role in the pathophysiology of major depressive disorder (MDD), and its functional interactive profile with the left dorsal lateral prefrontal cortex (DLPFC) is associated with transcranial magnetic stimulation (TMS) treatment outcomes. Nevertheless, previous research on sgACC functional connectivity (FC) in MDD has yielded inconsistent results, partly due to small sample sizes and limited statistical power. Furthermore, calculating sgACC-FC to target TMS individually is challenging. MethodsLeveraging a large multi-site cross-sectional sample (1660 MDD patients vs. 1341 healthy controls) from Phase II of the Depression Imaging REsearch ConsorTium (DIRECT), we systematically delineated case-control difference maps of sgACC-FC. Then, we explored the potential impact of such group-level abnormality profiles on the TMS target localization and clinical efficacy. Next, we developed an MDD big data-guided individualized TMS targeting algorithm to integrate group-level statistical maps with individual-level brain activity to localize TMS targets individually. ResultsWe found an enhanced sgACC-DLPFC FC in MDD patients compared to healthy controls (HC). Such group differences altered the position of the sgACC anti-correlation peak in the left DLPFC. In two independent clinical samples, we showed that the magnitude of TMS targets case-control differences in sgACC FC was related to clinical improvement. The MDD big data-guided individualized TMS targeting algorithm may generate individualized TMS targets that are clinically superior to group-level targets. InterpretationWe reliably delineated MDD-related abnormalities of sgACC-FC profiles in a large, independently ascertained sample and demonstrated the potential impact of such case-control differences on FC-guided localization of TMS targets. FundingMinistry of Science and Technology of the Peoples Republic of China, National Natural Science Foundation of China, and Chinese Academy of Sciences

neuroscience↗