Biology subjects

Grosenick, L.

Publications and source records attributed to Grosenick, L..

5 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↗

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

Bidirectional Regulation of Motor Circuits Using Magnetogenetic Gene Therapy

Here we report a novel suite of magnetogenetic tools, based on a single anti-ferritin nanobody-TRPV1 receptor fusion protein, which regulated neuronal activity when exposed to magnetic fields. AAV-mediated delivery of a floxed nanobody-TRPV1 into the striatum of adenosine 2a receptor-cre driver mice resulted in motor freezing when placed in an MRI or adjacent to a transcranial magnetic stimulation (TMS) device. Functional imaging and fiber photometry both confirmed activation of the target region in response to the magnetic fields. Expression of the same construct in the striatum of wild-type mice along with a second injection of an AAVretro expressing cre into the globus pallidus led to similar circuit specificity and motor responses. Finally, a mutation was generated to gate chloride and inhibit neuronal activity. Expression of this variant in subthalamic nucleus in PitX2-cre parkinsonian mice resulted in reduced local c-fos expression and motor rotational behavior. These data demonstrate that magnetogenetic constructs can bidirectionally regulate activity of specific neuronal circuits non-invasively in-vivo using clinically available devices. TeaserA novel magnetogenetics toolbox to regulate neural circuits in-vivo.

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↗

bioRxiv2018

Functional and Optogenetic Approaches to Discovering Stable Subtype-Specific Circuit Mechanisms in Depression

BackgroundUsing canonical correlation analysis (CCA), hierarchical clustering, and machine learning methods, we recently identified four subtypes of depression defined by distinct patterns of abnormal functional connectivity in depression-related brain networks, which in turn predicted differing clinical symptom profiles and individual differences in treatment response. However, whether and how dysfunction in specific circuits may give rise to specific depressive symptoms and behaviors remains unclear. Furthermore, this approach assumes that there are robust and stable canonical correlations between functional connectivity and depressive symptoms--an assumption that was not extensively tested in our earlier work.\n\nMethodsFirst, we comprehensively re-evaluate the stability of canonical correlations between functional connectivity and symptoms, using optimized approaches for large-scale statistical testing, and we validate methods for improving stability. Next, we illustrate one approach to formulating hypotheses regarding subtype-specific circuit mechanisms driving depressive symptoms and behaviors and then testing them in animal models using optogenetic fMRI. We review recent work in this field and describe one example of this approach.\n\nResultsCorrelations between connectivity features and clinical symptoms are robustly significant, and CCA solutions tested repeatedly on held-out data generalize, but they are sensitive to data quality, preprocessing decisions, and clinical sample heterogeneity, which can reduce effect sizes. Generalization can be markedly improved by adding L2-regularization to CCA, which decreases variance, increases canonical correlations in left-out data, and stabilizes feature selection. This approach, in turn, can be used to identify candidate circuits for optogenetic interrogation in rodent models.\n\nConclusionsMulti-view approaches like CCA are a conceptually useful framework for discovering stable patient subtypes by synthesizing multiple clinical and functional measures. Optogenetic fMRI holds substantial promise for testing hypotheses regarding subtype-specific mechanisms driving specific symptoms and behaviors in depression.

neuroscience↗