Biology subjects

Vila-Rodriguez, F.

Publications and source records attributed to Vila-Rodriguez, F..

11 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

Chronic electroconvulsive shock disrupts hippocampal physiology and impairs associative memory in mice

Electroconvulsive therapy (ECT) is a highly effective treatment for depression but it has undesirable side effects on various aspects of cognition, including memory. Patients might display retrograde amnesia for autobiographical events and anterograde amnesia in tests of recall and recognition memory. These amnestic effects are likely to depend, at least in part, on changes in the hippocampus. ECT-induced volume increases in the hippocampus, and dentate gyrus subregion in particular, correlate with the cognitive deficits. However, the precise cellular and behavioral effects of ECT remain poorly understood. Here, we therefore subjected male and female mice to chronic electroconvulsive shock (ECS) and examined effects on hippocampal physiology and memory. Perforant path inputs onto dentate gyrus neurons were examined using whole cell patch clamp recordings in hippocampal slices. In the intact slice, perforant path stimulation did not result in any synaptic or spiking differences in mice given sham vs ECS treatments. However, GABAA blockade increased depolarization and spiking in sham, but not ECS-treated, mice. Voltage clamp recordings further revealed weaker perforant path excitation of dentate gyrus neurons in ECS-treated mice. Extrapolating these findings to the intact animal, we then used Fos expression as a proxy for neural activity, and found that ECS blunted exploration-induced hippocampal activation, which was most pronounced in the dentate gyrus. Finally, recognition memory tasks revealed that ECS impaired memory for object-context associations but not memory for objects or locations. Collectively, these experiments reveal that a clinically relevant schedule of ECS disrupts synaptic and circuit functions of the hippocampus and causes anterograde amnesia in a test of episodic-like associative memory.

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↗

bioRxiv2023

Enhancement of neurophysiological signatures of working memory by combined yoga and tDCS

Transcranial direct current stimulation (tDCS) is a non-invasive neuromodulation technology that can modulate cortical excitability. Similarly, yoga has been found to influence neuronal activity and cognition. The aim of this study was to investigate the impact of combined yoga and tDCS on event-related potential (ERP) components during an N-Back working memory task. In a randomized, double-blind, cross-over design study, 22 healthy participants underwent a yoga/active tDCS session (2mA; 20min; anode on F3, cathode on F4) or a yoga/sham tDCS session on two different days. During the N-Back task, ERP components were obtained before and after each intervention. Results show that active tDCS plus yoga was associated with significant changes in the amplitude of the P200 component for the 2-Back in Fz and F3 channels and P300 for 3-Back in F3 and Pz electrodes. These results suggest that combining behavioral and electrical neuromodulation techniques may have the potential to enhance cognition and neurophysiological effects.

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↗

bioRxiv2022

Identifiability analysis and noninvasive online estimation of the first-order neural activation dynamics in the brain with transcranial magnetic stimulation

BackgroundNeurons demonstrate very distinct nonlinear activation dynamics, influenced by the neuron type, morphology, ion channel expression, and various other factors. The measurement of the activation dynamics can identify the neural target of stimulation and detect deviations, e.g., for diagnosis. This paper describes a tool for closed-loop sequential parameter estimation (SPE) of the activation dynamics through transcranial magnetic stimulation (TMS). The proposed SPE method operates in real time, selects ideal stimulus parameters, detects and processes the response, and concurrently estimates the input-output (IO) curve and the first-order approximation of the activated neural target. ObjectiveTo develop a method for concurrent SPE of the first-order activation dynamics and IO curve with closed-loop TMS. MethodFirst, identifiability of an integrated model of the first-order neural activation dynamics and IO curve is assessed, demonstrating that at least two IO curves need to be acquired with different pulse widths. Then, a two-stage SPE method is proposed. It estimates the IO curve by using Fisher information matrix (FIM) optimization in the first stage and subsequently estimates the membrane time constant as well as the coupling gain in the second stage. The procedure continues in a sequential manner until a stopping rule is satisfied. ResultsThe results of 73 simulation cases confirm the satisfactory estimation of the membrane time constant and coupling gain with average absolute relative errors (AREs) of 6.2% and 5.3%, respectively, with an average of 344 pulses (172 pulses for each IO curve or pulse width). The method estimates the IO curves lower and upper plateaus, mid-point, and slope with average AREs of 0.2%, 0.7%, 0.9%, and 14.5%, respectively. The conventional time constant estimation method based on the strength-duration (S-D) curve leads to 33.3% ARE, which is 27.0% larger than 6.2% ARE obtained through the proposed real-time FIM-based SPE method in this paper. ConclusionsSPE of the activation dynamics requires acquiring at least two IO curves with different pulse widths, which needs a controllable TMS (cTMS) device with adjustable pulse duration. SignificanceThe proposed SPE method enhances the cTMS functionality, which can contribute novel insights in research and clinical studies.

neuroscience↗

bioRxiv2022

Closed-loop and automatic tuning of pulse amplitude and width in EMG-guided controllable transcranial magnetic stimulation (cTMS)

This paper proposes a tool for automatic and optimal tuning of pulse amplitude and width for sequential parameter estimation (SPE) of the membrane time constant and input-output curve in closed-loop electromyography-guided (EMG-guided) controllable transcranial magnetic stimulation (cTMS). A normalized depolarization factor is defined which separates the optimization of the pulse amplitude and width. Then, the pulse amplitude is chosen by the maximization of the Fisher information matrix (FIM), while the pulse width is chosen by the maximization of the normalized depolarization factor. The simulation results confirm satisfactory estimation. The results show that the normalized depolarization factor maximization can identify the critical pulse width, which is an important parameter in the identifiability analysis, without any prior neurophysiological or anatomical knowledge of the neural membrane.

neuroscience↗

bioRxiv2022

Formalism for the sequential estimation of neural membrane time constant and input-output curve towards selective and closed-loop transcranial magnetic stimulation

ObjectiveTo obtain a formalism for real-time concurrent sequential estimation of neural membrane time constant and input-output (IO) curve with transcranial magnetic stimulation (TMS). ApproachFirst, the neural membrane response and depolarization factor, which leads to motor evoked potentials (MEPs) with TMS are analytically computed and discussed. Then, an integrated model is developed which combines the neural membrane time constant and input-output curve. Identifiability of the proposed integrated model is discussed. A condition is derived, which assures estimation of the proposed integrated model. Finally, sequential parameter estimation (SPE) of the neural membrane time constant and IO curve is described through closed-loop optimal sampling and open-loop uniform sampling TMS. Without loss of generality, this paper focuses on a specific case of commercialized TMS pulse shapes. The proposed formalism and SPE method are directly applicable to other pulse shapes. Main resultsThe results confirm satisfactory estimation of the membrane time constant and IO curve parameters. By defining a stopping rule based on five times consecutive convergence of the estimation parameters with a tolerances of 0.01, the membrane time constant and IO curve parameters are estimated with 82 TMS pulses with absolute relative estimation errors (AREs) of less than 4% with the optimal sampling SPE method. At this point, the uniform sampling SPE method leads to AREs up to 16%. The uniform sampling method does not satisfy the stopping rule due to the large estimation variations. SignificanceThis paper provides a tool for real-time closed-loop SPE of the neural time constant and IO curve, which can contribute novel insights in TMS studies. SPE of the membrane time constant enables selective stimulation, which can be used for advanced brain research, precision medicine and personalized medicine.

neuroscience↗

bioRxiv2021

Implementation of a TMS-fMRI system: A primer

Transcranial magnetic stimulation (TMS) is a non-invasive and non-pharmacological intervention, approved for the treatment of individuals diagnosed with treatment-resistant depression. This well-tolerated approach uses magnetic pulses to stimulate specific brain regions and induce changes in brain networks at multiple levels of human functioning. Combining TMS with other neuroimaging techniques, such as functional magnetic resonance imaging (fMRI), offers new insights into brain functioning, and allows to map out the causal alterations brought on by TMS interventions on neural network connectivity and behaviour. However, the implemention of concurrent TMS-fMRI brings on a number of technical challenges that must be overcome to ensure good quality of functional images. The goal of this study was thus to investigate the impact of TMS pulses in an MR-environment on the quality of BRAINO phantom images, in terms of the signal of the images, the temporal fluctuation noise, the spatial noise and the signal to fluctuation noise ratio, at the University of British Columbia (UBC) Neuroimaging facility. The results of our analyses replicated those of previous sites, and showed that the present set-up for concurrent TMS-fMRI ensures minimal noise artefact on functional images obtained through this multimodal approach. This step was a key stepping stone for future clinical trials at UBC.

neuroscience↗

bioRxiv2020

SS-Detect: Development and Validation of a New Strategy for Source-Based Morphometry in Multi-Scanner Studies

Background and PurposeSource-based morphometry (SBM) has been used in multi-centre studies pooling magnetic resonance imaging (MRI) data across different scanners to advance the reproducibility of neuroscience research. In the present study, we developed an analysis strategy for Scanner-Specific Detection (SS-Detect) of SBPs in multi-scanner studies, and evaluated its performance relative to a conventional strategy. MethodsIn the first experiment, the SimTB toolbox was used to generate simulated datasets mimicking twenty different scanners with common and scanner-specific SBPs. In the second experiment, we generated one simulated SBP from empirical gray matter volume (GMV) datasets from two different scanners. Moreover, we applied two strategies to compare SBPs between schizophrenia patients and healthy controls GMV from two different scanners. ResultsThe outputs of the conventional strategy were limited to whole-sample-level results across all scanners; the outputs of SS-Detect included whole-sample-level and scanner-specific results. In the first simulation experiment, SS-Detect successfully estimated all simulated SBPs, including the common and scanner-specific SBPs whereas the conventional strategy detected only some of the whole-sample SBPs. The second simulation experiment showed that both strategies could detect the simulated SBP. Quantitative evaluations of both experiments demonstrated greater accuracy of the SS-Detect in estimating spatial SBPs and subject-specific loading parameters. In the third experiment, SS-Detect detected more significant between-group SBPs, and these SBPs corresponded with the results from voxel-based morphometry analysis, suggesting that SS-Detect has higher sensitivity in detecting between-group differences. ConclusionsSS-Detect outperformed the conventional strategy and can be considered advantageous when SBM is applied to a multi-scanner study.

bioinformatics↗