Biology subjects

Gunning, F. M.

Publications and source records attributed to Gunning, F. M..

4 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

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↗

bioRxiv2020

White matter hyperintensity-associated structural disconnection, resting state functional connectivity, and cognitive control in older adults

ObjectiveWhite matter hyperintensities (WMH) are linked to deficits in cognitive functioning, including cognitive control and memory; however, the structural and functional mechanisms are largely unknown. We investigated the relationship between estimated regional disruptions to white matter fiber tracts from WMH, resting state functional connectivity (RSFC), and cognitive functions in older adults. DesignCross-sectional study. SettingCommunity. ParticipantsFifty-eight cognitively-healthy older adults. MeasurementsTasks of cognitive control and memory, structural MRI, and resting state fMRI. We estimated the disruption to white matter fiber tracts from WMH and its impact on gray matter regions in the cortical and subcortical frontoparietal network, default mode network, and ventral attention network by overlaying each subjects WMH mask on a normative tractogram dataset. We calculated RSFC between nodes in those same networks. ResultsThe interaction of estimated regional WMH burden and RSFC in cortico-striatal regions of the default mode network and frontoparietal network was associated with memory retrieval. Models predicting working memory, cognitive inhibition, and set-shifting were not significant. ConclusionsFindings highlight the role of circuit-level alterations at the structural and functional levels in resting state networks that are related to WMH and impact memory retrieval in older adults.

neuroscience↗

bioRxiv2018

A Double-Blind Pilot Dosing Study of Low Field Magnetic Stimulation (LFMS) for Treatment-Resistant Depression (TRD)

Low Field Magnetic Stimulation is a potentially rapid-acting treatment for depression with mood-enhancing effects in as little as one 20-minute session. The most convincing data for LFMS has come from treating bipolar depression. We examined whether LFMS also has rapid mood-enhancing effects in treatment-resistant major depressive disorder, and whether these effects are dose-dependent. We hypothesized that a single 20-min session of LFMS would reduce depressive symptom severity and that the magnitude of this change would be greater after three 20-min sessions than after a single 20-min session. In a double-blind randomized controlled trial, 30 participants (age 21-65) with treatment-resistant depression were randomized to three 20-minute active or sham LFMS treatments with 48 hours between treatments. Response was assessed immediately following LFMS treatment using the 6-item Hamilton Depression Rating Scale (HAMD-6), the Positive and Negative Affect Scale (PANAS) and the Visual Analog Scale. Following the third session of LFMS, the effect of LFMS on VAS and HAMD-6 was superior to sham (F(1, 24) = 7.45, p = 0.03, Holm-Bonferroni corrected; F(1,22) = 6.92, p = 0.03, Holm-Bonferroni corrected, respectively). There were no differences between sham and LFMS following the initial or second session with the effect not becoming significant until after the third session. Three 20-minute LFMS sessions were required for active LFMS to have a mood-enhancing effect for individuals with treatment-resistant depression. As this effect may be transient, future work should address dosing schedules of longer treatment course as well as biomarker-based targeting of LFMS to optimize patient selection and treatment outcomes.

clinical trials↗