Biology subjects

Godwin, D.

Publications and source records attributed to Godwin, D..

4 recordsLinked to original sources

bioRxiv2025

Complex Patterns of Altered White Matter Structural Connectivity within a 'subjective valuation network' in Treatment-Resistant Depression

BackgroundTreatment-resistant depression (TRD) poses a significant clinical challenge, demanding a deeper understanding of its neurobiological underpinnings to improve therapeutics. We examined white matter microstructure and structural connectivity in TRD, focusing on the subjective valuation network (SVN), which captures motivated behavior, reward processing, and emotional regulation circuits commonly altered in depression. This allowed us to identify potential neuroimaging biomarkers associated with treatment resistance. MethodsDiffusion tensor imaging (DTI) data were acquired from a sample of age-and-gender balanced individuals with TRD (n=44; female = 24, male=11, non-binary/unchecked =7) and non-depressed controls (n=42; female=27, male =12, non-binary/unchecked=5). Tract-Based Spatial Statistics were used to compare whole-brain white matter integrity differences between groups. Probabilistic tractography was then used to assess fractional anisotropy (FA) and white matter structural connectivity within the SVN across groups. SVN was defined a priori based on converging functional connectivity studies and included key regions such as the ventromedial prefrontal cortex (vmPFC), anterior cingulate cortex (ACC), ventral striatum, and insula and their connecting white matter tracts. Additionally, correlations between clinical measures of depression severity and cognition and structural features of the networks white matter fibers (i.e. FA and structural connectivity) were explored. ResultsCompared to non-depressed controls, individuals with TRD exhibited reduced FA within the left uncinate fasciculus, left inferior fronto-occipital fasciculus, and anterior cingulum, supporting widespread white matter integrity degradations in TRD. No correlations were found between FA and depression severity, suggesting a more specific association with anhedonic features. Whole-network measures of FA and structural connectivity of the SVN did not differ between groups. However, specific subcircuits structural connectivity within the SVN differed between groups; namely, the white matter tracts connecting the insula-vmPFC, striatum-insula, and striatum-vmPFC. Hyperconnectivity emerged for patients with TRD for tracts connecting the insula-vmPFC and striatum-vmPFC region-pairs, with exhibited hypoconnectivity between the striatum-insula. Exploratory analyses for the TRD group indicated the subcircuits with altered structural connectivity within the SVN correlated with depressive severity. This indicates subcircuit network alterations may associate with greater difficulty experiencing pleasure - a core symptom of depression and a potential marker of treatment resistance. ConclusionsThis study provides evidence for wide-spread disruption of white matter microstructure and altered structural connectivity within specific subcircuits of the SVN in TRD. These findings point to an intricate pattern of structural hyper-and-hypo-connectivity within the subcircuits of the SVN which may underlie the core symptoms of TRD. The altered structural connectivity within the SVN may contribute to the pathophysiology of TRD, especially concerning the motivational and emotional deficits associated with anhedonia. Future research employing multimodal neuroimaging techniques and longitudinal designs is warranted to further elucidate the functional consequences of these structural abnormalities and their potential as predictive biomarkers for personalized treatment interventions in TRD. Specifically, investigating how these white matter alterations change with successful treatment or targeted interventions aimed at improving anhedonia could inform more effective therapies for this challenging condition.

neuroscience↗

bioRxiv2025

M-ECG: Extracting Heart Signals with a Novel Computational Analysis of Magnetoencephalography Data

Magnetoencephalography (MEG) measures the magnetic fields generated by neural activity with high temporal and spatial resolution. Because of its focus on brain activity, other biopotentials, including muscle artifacts and heart signals, are typically filtered or rejected. In this study, the feasibility of extracting cardiac signals from MEG data, which is termed magnetoencephalographic electrocardiogram (M-ECG; in contrast to the electrocardiogram or ECG) is explored. Using the publicly available Brainstorm MEG auditory dataset CTF and OMEGA resting-state sample dataset, a novel algorithm is developed that utilizes either independent component analysis (ICA) or MEG reference sensors to extract M-ECG signals and compute heart rate variability (HRV) from MEG data reliably and accurately. Signal processing methods in the time, frequency, and time-frequency domains along with statistical tests such as Spearman correlation, root mean square error, mean absolute error, Bland-Altman mean difference, and Mann-Whitney U Test are employed to assess the similarities across the signals. The results indicate a significant alignment of temporal and frequency spectral power characteristics between M-ECG HRV and ECG HRV signals, suggesting a promising degree of similarity and correspondence. The findings highlight the feasibility of extracting M-ECG and computing HRV directly from raw MEG data. These insights hold the potential to enhance multimodal neuroimaging methodologies and further elucidate the intricate interplay between brain activity and cardiovascular function. The potential of HRV as a biomarker for brain disorders could improve diagnostic accuracy, prognostic assessment, and therapeutic strategies, particularly in neurological disorders with centrally mediated autonomic dysfunction.

neuroscience↗

bioRxiv2025

Magnetoencephalography Reveals Neuroprotective Effects of COVID-19 Vaccination in Non-Human Primates

SUMMARY PARAGRAPHCOVID-19, caused by the SARS-CoV-2 virus, can lead to widespread neurological complications, including cognitive deficits and neurodegenerative symptoms, even in the absence of significant structural brain abnormalities. The potential neuroprotective effects of SARS-CoV-2 vaccination remain underexplored. Here, we demonstrate the neuroprotective effects of a psoralen-inactivated SARS-CoV-2 vaccine in a non-human primate model using resting-state magnetoencephalography (MEG), a non-invasive neurophysiological recording technique with sub-millisecond temporal and submillimeter spatial resolution. MEG scans demonstrated substantial preservation of neural activity across multiple brain regions in vaccinated subjects compared to unvaccinated controls following viral challenge. This approach not only underscores the role of vaccination in mitigating severe neurological outcomes but also highlights the capability of MEG to detect subtle yet significant changes in brain function that may be overlooked by other imaging modalities. These findings advance our understanding of vaccine-induced neuroprotection and establish MEG as a powerful tool for monitoring brain function in the context of viral infections.

neuroscience↗

bioRxiv2019

Optogenetically-Induced Population Discharge Threshold as a Sensitive Measure of Network Excitability

Network excitability is governed by synaptic efficacy, intrinsic excitability, and the circuitry in which these factors are expressed. The complex interplay between these factors determines how circuits function, and at the extreme, their susceptibility to seizure. We have developed a novel optogenetic intensity response procedure that provides a sensitive, quantitative estimate of network excitability. By combining optogenetic stimulation of the hippocampus with chronic multi-site recordings in peri-hippocampal structures of awake behaving mice, we induced abnormal network-wide epileptiform population discharges (PD) that were nearly indistinguishable from spontaneously occurring interictal spikes. By systematically varying light intensity, and therefore the magnitude of the optogenetically-mediated current, we generated intensity-response curves using the probability of PD as the dependent variable. This probability curve was well fit by a Boltzmann function, from which we calculated the intensity that produces a half-maximal probability of discharge (I50). This novel metric, the I50, is correlated with the optogenetic after-discharge threshold (oADT) in the same mice. Manipulations known to increase excitability, such as sub-convulsive doses (20 mg/kg) of the chemoconvulsant pentylenetetrazol (PTZ), produced a leftward shift in the curve compared to baseline. The anti-epileptic drug levetiracetam (40 mk/kg), in combination with PTZ, produced a rightward shift. Optogenetically-induced population discharge threshold (oPDT) baselines were stable over time, suggesting the metric is appropriate for within-subject experimental designs with multiple pharmacological manipulations. The oPDT is a sensitive measure of subconvulsive network excitability, with broad applicability to a number of areas of investigation. Significance StatementNetwork excitability is carefully regulated by homeostatic mechanisms in the brain in order to maintain optimal functional conditions. Abnormal excitability is associated with a number of neurological disorders including epilepsy. Excitability can be measured in single cells in vitro, but it is difficult to extrapolate from these values to the functional impact on the network as a whole. Epileptiform discharges are network wide events that represent a distinct transition from normal to abnormal functional modes. We developed a new technique that uses light intensity-response curves to precisely determine the threshold for this transition as a surrogate measure of network excitability and seizure susceptibility.

neuroscience↗