Biology subjects

Stufflebeam, S. M.

Publications and source records attributed to Stufflebeam, S. M..

3 recordsLinked to original sources

bioRxiv2026

Motion tolerance in wearable OPM-MEG using dynamic field nulling

Wearable magnetoencephalography (MEG) enabled by optically pumped magnetometers (OPMs) promises improved comfort and motion tolerance. This is particularly beneficial when measuring brain activity in children who cannot sit still for long periods of time. Compared to cryogenic MEG, wearable MEG allows larger head movements, but they result in artifacts due to uncompensated background fields and reduce source localization accuracy. Spatial filtering methods can partially compensate these motion-induced artifacts, but they are most effective when used in combination with background field nulling. This is because accurate spatial filtering relies on an accurate estimate of the sensor gain and orientation of its sensitive axis. Through simulations, we first deduce the target residual background field that is necessary for accurate dipole localization (< 1 cm) in the presence of head movements. Using our open-source printed circuit board (PCB) coils, we develop a method to dynamically null the background field. We demonstrate that our dynamic field nulling method allows improved localization of somatosensory evoked fields (SEFs) by maintaining the background field below the target residual fields established in the simulations. Our study highlights the importance of tracking both the background field and the head position relative to the background field for quality assurance in wearable MEG.

neuroscience↗

bioRxiv2023

Decoding auditory working memory content from intracranial high frequency activity in humans

Working memory (WM), the human brains system for maintaining and manipulating information over short timescales, is critical for goal-directed behavior. Classic neurophysiological models, which emphasize persistent delay activity, are being challenged by an idea that WM is maintained through dynamic and activity-silent mechanisms, where the memory trace is preserved by transient changes in the connections between neurons. To date, human evidence for this alternative model has been limited to non-invasive studies. Here, leveraging high-resolution intracranial stereo-EEG with advanced machine learning decoding techniques, we investigate neuronal mechanisms of human auditory WM. Neuronal activity is quantified as broadband high frequency activity (70-190 Hz), a correlate of neuronal firing activity. Our multivariate pattern analyses, validated via robust non-parametric permutation testing, show that WM content is represented in multiple brain regions, exhibiting dynamic rather than persistent delay activity. Crucially, by using task-irrelevant sounds to probe latent mnemonic states, we provide the first human intracranial evidence of activity silent WM maintenance in a local sensory network.

neuroscience↗

bioRxiv2019

Altered structural hub connectivity and its clinical relevance in glioma

Background and PurposeStructural network analysis of diffusion imaging is increasingly used to study neurological disease, its pathophysiology and symptoms. We therefore evaluate structural hub connectivity in glioma patients and its association with molecular subtype and clinical status.\n\nMaterials and MethodsUsing retrospective diffusion imaging, structural connectivity was investigated in 65 newly diagnosed glioma patients (36 males; mean age 52 {+/-} 14 years) and 60 healthy controls (23 males; mean age 50 {+/-} 7 years). Probabilistic tractography was performed between 39 cortical nodes per hemisphere. In patients, tumors were drawn in to exclude each tumor-containing voxel from analysis. As previous connectomic research in glioma and other neurological diseases has shown particular importance of hub nodes and connections, the numbers of connections between hubs, hubs and non-hubs, and non-hubs were calculated for each hemisphere separately. Clinical and molecular characteristics were assessed as part of routine clinical care. Group differences in connectivity and its associations with performance and molecular subtypes were tested non-parametrically through Mann-Whitney U-tests, corrected for multiple comparisons.\n\nResultsGlioma patients had more hub-related connections in the hemisphere contralateral to the tumor (hub-hub P = 0.002, hub-non-hub P = 0.005), despite being comparable to controls in terms of total and ipsilateral connections. Within patients, hub-related connectivity related to performance status (P = 0.009) and molecular subtype (P = 0.045).\n\nConclusionWe present experimental evidence for the relevance of structural connectomics as a tool to pick up on the clinical impact of glioma on the rest of the brain.

cancer biology↗