Biology subjects

Ramsey, N.

Publications and source records attributed to Ramsey, N..

6 recordsLinked to original sources

bioRxiv2026

Electrophysiological Signature of Stroke Recovery: Investigating EEG Biomarkers for Prognostic Insights

Stroke is a leading cause of long-term disability, often resulting in persistent motor impairments reflecting disruptions in large-scale brain networks. While electroencephalography (EEG) has long been used to monitor neurophysiological changes following stroke, an integrated framework capturing spatiotemporal dynamics would help understand changes over time. In this study, we analysed resting-state EEG from stroke patients at one week (Session 1) and three months (Session 2) post-stroke to investigate electrophysiological biomarkers of motor recovery, indexed by changes in the Fugl-Meyer scale ({Delta}FM ). We quantified spectral properties, focusing on the relative alpha band power, microstate metrics such as mean duration, complexity, and transition probabilities, and measures of metastability and synchrony derived from the Kuramoto Order Parameter. Among all the EEG measures, the longitudinal change in relative alpha power emerged as the strongest single correlate of motor improvement, accounting for the largest proportion of variance among the examined EEG measures. Although metastability and synchrony alone did not reach statistical significance, they showed moderate positive correlations with {Delta}FM, particularly in the alpha and theta ranges, and once combined with alpha power, added 26% in explaining the variance in {Delta}FM . Microstate parameters did not explain additional variance in recovery once alpha power and network-level dynamics were considered. A hierarchical model combining alpha power, metastability/synchrony, and microstates explained over 78% of the variance in {Delta}FM, indicating that stroke recovery involves restoring balanced alpha oscillations and flexible large-scale brain coordination. Future research with larger samples and more frequent longitudinal assessments is required to confirm the prognostic utility of integrated EEG biomarkers for guiding personalised stroke rehabilitation strategies.

neuroscience↗

bioRxiv2026

Normalization accounts for temporal dynamics in human somatosensory cortex

Sensory processing is fundamentally shaped by stimulation history. For example, in visual cortex, neural responses are reduced for repeated or sustained stimuli (adaptation). These phenomena are well characterized and effectively modeled by divisive normalization. We asked whether these same computational principles govern somatosensory processing. We used fMRI (6 participants) and intracranial electroencephalography (iEEG, 2 participants) to measure responses to time-varying vibrotactile stimuli in human somatosensory cortex. Stimuli consisted of single- and paired-pulses with durations and interstimulus intervals ranging from 0.05 to 1.2 s. We extracted BOLD time courses to capture neural response amplitudes, and high-frequency iEEG broadband envelopes to capture fast neural dynamics. In both experiments, we observed pronounced sub-additive temporal summation. Responses to longer or repeated stimuli were consistently lower than predicted by linear integration. Computational modeling revealed that divisive normalization models outperformed linear models in cross-validated accuracy across both datasets. These results demonstrate that somatosensory temporal dynamics closely mirror those in the visual system. Our findings suggest that the nervous system employs similar computational principles across modalities to encode sensory information across time. Significance statementHow the brain integrates sensory information over time is a fundamental question in neuroscience. While nonlinear temporal integration is well documented in visual cortex, it has not been extensively mapped in the human somatosensory system. By combining fMRI with intracranial EEG in humans, we demonstrate that somatosensory responses to tactile stimulation exhibit subadditive temporal summation. This nonlinearity is accurately captured by a divisive normalization model, matching observations in the visual system. Our results suggest that normalization is a canonical computation shared across different modalities to manage temporal dynamics, providing a unified framework for understanding how the brain encodes dynamic sensory stimuli.

neuroscience↗

bioRxiv2023

Spatial Tuning of Alpha Oscillations in Human Visual Cortex

AO_SCPLOWBSTRACTC_SCPLOWNeuronal oscillations at about 10 Hz, called alpha oscillations, are often thought to arise from synchronous activity across occipital cortex, reflecting general cognitive states such as arousal and alertness. However, there is also evidence that modulation of alpha oscillations in visual cortex can be spatially specific. Here, we used intracranial electrodes in human patients to measure alpha oscillations in response to visual stimuli whose location varied systematically across the visual field. We separated the alpha oscillatory power from broadband power changes. The variation in alpha oscillatory power with stimulus position was then fit by a population receptive field (pRF) model. We find that the alpha pRFs have similar center locations to pRFs estimated from broadband power (70-180 Hz) but are several times larger. The results demonstrate that alpha suppression in human visual cortex can be precisely tuned. Finally, we show how the pattern of alpha responses can explain several features of exogenous visual attention. Significance StatementThe alpha oscillation is the largest electrical signal generated by the human brain. An important question in systems neuroscience is the degree to which this oscillation reflects system-wide states and behaviors such as arousal, alertness, and attention, versus much more specific functions in the routing and processing of information. We examined alpha oscillations at high spatial precision in human patients with intracranial electrodes implanted over visual cortex. We discovered a surprisingly high spatial specificity of visually driven alpha oscillations, which we quantified with receptive field models. We further use our discoveries about properties of the alpha response to show a link between these oscillations and the spread of visual attention. Grant support: NIH R01 MH111417 (Petridou, Winawer, Ramsey, Devinsky); JSPS Overseas Research Fellowship (Yuasa) The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health

neuroscience↗

bioRxiv2021

Temporal dynamics of neural responses in human visual cortex

Neural responses to visual stimuli exhibit complex temporal dynamics, including sub-additive temporal summation, response reduction with repeated or sustained stimuli (adaptation), and slower dynamics at low contrast. These phenomena are often studied independently. Here, we demonstrate these phenomena within the same experiment and model the underlying neural computations with a single computational model. We extracted time-varying responses from electrocorticographic (ECoG) recordings from patients presented with stimuli that varied in contrast, duration, and inter-stimulus interval (ISI). Aggregating data across patients yielded 98 electrodes with robust visual responses, covering both earlier (V1-V3) and higher-order (V3a/b, LO, TO, IPS) retinotopic maps. In all regions, the temporal dynamics of neural responses exhibit several non-linear features: peak response amplitude saturates with high contrast and longer stimulus durations; the response to a second stimulus is suppressed for short ISIs and recovers for longer ISIs; response latency decreases with increasing contrast. These features are accurately captured by a computational model comprised of a small set of canonical neuronal operations: linear filtering, rectification, exponentiation, and a delayed divisive normalization. We find that an increased normalization term captures both contrast- and adaptation-related response reductions, suggesting potentially shared underlying mechanisms. We additionally demonstrate both changes and invariance in temporal response dynamics between earlier and higher-order visual areas. Together, our results reveal the presence of a wide range of temporal and contrast-dependent neuronal dynamics in the human visual cortex, and demonstrate that a simple model captures these dynamics at millisecond resolution. Significance StatementSensory inputs and neural responses change continuously over time. It is especially challenging to understand a system that has both dynamic inputs and outputs. Here we use a computational modeling approach that specifies computations to convert a time-varying input stimulus to a neural response time course, and use this to predict neural activity measured in the human visual cortex. We show that this computational model predicts a wide variety of complex neural response shapes that we induced experimentally by manipulating the duration, repetition and contrast of visual stimuli. By comparing data and model predictions, we uncover systematic properties of temporal dynamics of neural signals, allowing us to better understand how the brain processes dynamic sensory information.

neuroscience↗

bioRxiv2021

A touch of hierarchy. Population Receptive Fields reveal fingertip integration in Brodmann areas in human primary somatosensory cortex.

Several neuroimaging studies have shown the somatotopy of body part representations in primary somatosensory cortex (S1), but the functional hierarchy of distinct subregions in human S1 has not been adequately addressed. The current study investigates the functional hierarchy of cyto-architectonically distinct regions, Brodmann areas BA3, BA1, and BA2, in human S1. During functional MRI experiments, we presented participants with vibrotactile stimulation of the fingertips at 3 different vibration frequencies. Using population Receptive Field (pRF) modeling of the fMRI BOLD activity, we identified the hand region in S1 and the somatotopy of the fingertips. For each voxel, the pRF center indicates the finger that most effectively drives the BOLD signal, and the pRF size measures the spatial somatic pooling of fingertips. We find a systematic relationship of pRF sizes from lower-order areas to higher-order areas. Specifically, we found that pRF sizes are smallest in BA3, increase slightly towards BA1, and are largest in BA2, paralleling the increase in visual receptive field size as one ascends the visual hierarchy. Additionally, we find that the time-to-peak of the hemodynamic response in BA3 is roughly 0.5s earlier compared to BA1 and BA2, further supporting the notion of a functional hierarchy of subregions in S1. These results were obtained during stimulation of different mechanoreceptors, suggesting that different afferent fibers leading up to S1 feed into the same cortical hierarchy.

neuroscience↗

bioRxiv2019

Enhancing fMRI language lateralization indices by homotopic mapping

The non-invasive nature of fMRI would make it an excellent tool for determining language lateralization, potentially replacing the WADA test that is now considered the gold standard. However, while the result of the WADA test has 3 straightforward categories, i.e. left, right, or bilateral language lateralization, fMRI lateralization indices (LIs) are on a continuous scale between -1 (completely right lateralized) and 1 (completely left lateralized), requiring arbitrary thresholding to come to a useful clinical measure. We hypothesize that the absence of clear categories in fMRI LIs is in part linked to imperfect fMRI task control and/or homotopically mapped language activity in the non-dominant hemisphere. In this study we attempt to account for these sources of brain activity by making a detailed homotopic comparison between voxels in the language areas and their homologues, which is now possible using the Cgrid toolbox. 36 epilepsy were included who underwent presurgical fMRI including a picture naming and verb generation task, in addition to the WADA test, functional Transcranial Doppler sonography (fTCD), or Electrocortical stimulation (ECS). LIs were calculated using a traditional approach, and a novel approach based on the paired comparison between language voxels and their homologues. We observed that lateralization indices came in closer alignment with those established by the WADA test, ECS, of fTCD. Activity maps indicated that the accounting for imperfect fMRI task control was at least part of the reason for the improvement when using the new approach. This Cgrid based approach could help fMRI replacing the more invasive WADA test in the future.

neuroscience↗