Biology subjects

Stamatakis, E.

Publications and source records attributed to Stamatakis, E..

7 recordsLinked to original sources

bioRxiv2025

Lineage domains and cytoskeletal cables organize a cellular square grid in a crustacean

To build tissues, and ultimately functional bodies, cells in early embryos must arrange into specific patterns. In most animals, epithelial tissues exhibit a predominantly hexagonal space-packing geometry. However, in many species of the largest group of crustaceans, the Malacostraca, the embryonic epithelium takes on the striking form of a grid made up of predominantly square cells, sequential rows of which establish the adult segmented body plan. After square cells emerge, their organization appears to be maintained by specific cell division patterns. However, the mechanisms that initially generate square cells from hexagonal precursors are unknown. Here we address this problem by combining long-term multiview lightsheet microscopy, immunohistochemistry, laser ablation, and pharmacological perturbation. We show that in the emerging model crustacean Parhyale hawaiensis this highly unusual grid geometry is first initiated from two perpendicular axes that are established sequentially according to different cellular mechanisms. The first axis arises dorso-ventrally at a tensile lineage compartment boundary, while the second emerges at the anterior-posterior axis along the ventral midline through lineage-independent cell intercalation driven by tensile actomyosin cables. We show that these midline cables are necessary for organizing square-cell packing as well as for proper expression of the segmentation gene engrailed. Our findings show that both cell lineage-specific behaviors, as well as lineage-independent supracellular structures, are required to establish square grid epithelial organization and a segmented body plan.

developmental biology↗

bioRxiv2021

Resting-state based prediction of task-related activation in patients with disorders of consciousness

BackgroundAssessment of the level of awareness of people with disorders of consciousness (DOC) is clinically challenging, motivating several studies to combine brain imaging with machine learning to improve this process. While this work has shown promise, it has limited clinical utility, as misdiagnosis of DOC patients is relatively high. As machine learning algorithms rely on accurately labelled data, any error in diagnosis will be learned by the algorithm, resulting in an equally limited diagnostic tool. The goal of the present study is to overcome this problem by stratifying patients, not by diagnosis, but by their capacity to perform volitional tasks during functional magnetic resonance imaging (fMRI) scanning. MethodsA total of 71 patients were assessed for inclusion. They were excluded for the final analysis if they had large focal brain damage, excessive head motion during scanning, or suboptimal MRI preprocessing. Patients underwent both resting-state and task-based fMRI scanning. Univariate fMRI analysis was performed to determine if an individual patient had brain activity consistent with having retained volitional capacity (VC). Differences in resting brain network connectivity between patients with VC and patients without volitional capacity (non-VC) were measured. Connectivity data was then entered as input to a deep learning framework. We used a deep graph convolutional neural network (DGCNN) on connectivity data to identify a specific brain network that most significantly differentiates patients. FindingsWe included 30 patients in our final analysis. Univariate analysis revealed that 13 patients displayed signs of VC, while 17 did not. We found that resting-state connectivity between frontoparietal control and salience network was significantly different between VC and non-VC patients (T(28) = 3.347, p = 0.0023, Bonferroni corrected p = 0.042). Furthermore, we found that using frontoparietal control network connectivity as input to the DGCNN resulted in the best classification performance (test accuracy = 0.85; ROC AUC = 0.92). InterpretationWe found that the DGCNN performed best at discriminating between patients with VC when using only the frontoparietal control network as input to the model. The use of this deep learning method is a significant advance since its inherent flexibility permits the inclusion of both whole-brain and network-specific properties as input, allowing us to classify patients as either having or not having VC. This inclusion of multi-scale inputs (e.g. whole-brain and network-level) facilitates model interpretability and increases our understanding of the neurobiology of DOC. The results propose that the integrity of frontoparietal control network, a brain network well known to play a key role in executive functions and cognitive control, is essential for volitional capacity preservation in patients with DOC. The study also lays groundwork for development of a biomarker to aid in the diagnosis of DOC patients. RESEARCH IN CONTEXTO_ST_ABSEvidence before this studyC_ST_ABSDisorders of consciousness (DOC) are a group of severe brain disorders characterised by damage to the neural systems underlying wakefulness and awareness. DOC are often caused by traumatic brain injury, hypoxia, or neurodegenerative diseases. The motor and cognitive impairments in DOC patients make providing an accurate diagnosis very challenging. Diagnosis is primarily made at the bedside by assessing a patients response to motor commands.

neuroscience↗

bioRxiv2020

Time meets space: brain dynamics drive spatial topography

Unlike the brains faster frequencies, the exact role of its more powerful infraslow frequencies (ISF, 0.01 - 0.1Hz) in information processing remains poorly understood. Do and how ISF process information? We investigate information processing and related temporal dynamics of ISF in resting and task state fMRI. To quantify information, we apply the Lempel-Ziv complexity (LZC), a measure of signal compression indexing information. The LZC is combined with direct measurement of the dynamics of ISF themselves, namely their power spectral density by median frequency (MF). We demonstrate the following: (I) topographical differences in resting state between higher- and lower-order networks, showing statistically lower LZC in the former; (II) task-related changes in LZC; (III) modulation of LZC associated with MF changes, with low and high MF resting-state values correlated with different degrees of LZC change. In sum, we provide evidence that ISF carry and process information as mediated through their temporal dynamics.

neuroscience↗

bioRxiv2020

Intrinsic connectivity of the prefrontal cortex and striato-limbic system respectively differentiate Major Depressive from Generalized Anxiety Disorder

Major Depressive Disorder (MDD) and Generalized Anxiety Disorder (GAD) are highly prevalent and debilitating disorders. The high overlap on the symptomatic and neurobiological level led to ongoing debates about their diagnostic and neurobiological uniqueness. The present study aims to identify common and disorder-specific neuropathological mechanisms and treatment targets in MDD and GAD. The present study combined categorial and dimensional disorder models with a fully data-driven intrinsic network level analysis (Intrinsic Connectivity Contrast, ICC) to resting state fMRI data acquired in 108 partn = 35 and n = 38 unmedicated patients with first-episode GAD, MDD respectively and n=35 healthy controls). Convergent evidence from categorical and dimensional analyses revealed MDD-specific decreased whole-brain connectivity profiles of the medial prefrontal and dorsolateral prefrontal cortex while GAD was specifically characterized by decreased whole-brain connectivity profiles of the putamen and decreased communication of this region with the amygdala. Together, findings from the present data-driven analysis suggest that intrinsic communication of frontal regions engaged in executive functions and emotion regulation represent depression-specific neurofunctional markers and treatment targets whereas dysregulated intrinsic communication of the striato-amygdala system engaged in reinforcement-based and emotional learning processes represent GAD-specific markers and a promising treatment target.

neuroscience↗

bioRxiv2019

Fractal Dimension of Cortical Functional Connectivity Networks Predicts Severity in Disorders of Consciousness

Recent evidence suggests that the quantity and quality of conscious experience may be a function of the complexity of activity in the brain, and that consciousness emerges in a critical zone on the axes of order/randomness and integration/differentiation. We propose fractal shapes as a measure of proximity to this critical point, as fractal dimension encodes information about complexity beyond simple entropy or randomness, and fractal structures are known to emerge in systems nearing a critical point. To validate this, we tested the several measures of fractal dimension on the brain activity from healthy volunteers and patients with disorders of consciousness of varying severity. We used a Compact Box Burning algorithm to compute the fractal dimension of cortical functional connectivity networks as well as computing the fractal dimension of the associated adjacency matrices using a 2D box-counting algorithm. To test whether brain activity is fractal in time as well as space, we used the Higuchi temporal fractal dimension on BOLD time-series. We found significant decreases in the fractal dimension between healthy volunteers (n=15), patients in a minimally conscious state (n=10), and patients in a vegetative state (n=8), regardless of the mechanism of injury. We also found significant decreases in adjacency matrix fractal dimension and Higuchi temporal fractal dimension, which correlated with decreasing level of consciousness. These results suggest that cortical functional connectivity networks display fractal character and that this is predictive of level of consciousness in a clinically relevant population, with more fractal (i.e. more complex) networks being associated with higher levels of consciousness. This supports the hypothesis that level of consciousness and system complexity are positively associated, and is consistent with previous EEG, MEG, and fMRI studies.

neuroscience↗

bioRxiv2019

Consciousness & Brain Functional Complexity in Propofol Anaesthesia

The brain is possibly the most complex system known to mankind, and its complexity has been called upon to explain the emergence of consciousness. However, complexity can take many forms: here, we investigate measures of algorithmic and process complexity in both the temporal and topological dimension, testing them on functional MRI data obtained from individuals undergoing various levels of sedation with the anaesthetic agent propofol, in two separate datasets. We demonstrate that the various measures are differently able to discriminate between levels of sedation, with temporal measures showing higher sensitivity. Further, we show that all measures are strongly related to a single underlying construct explaining most of the variance, as assessed by Principal Component Analysis, which we interpret as a measure of overall complexity of our data. This overall complexity was also able to discriminate between levels of sedation, supporting the hypothesis that consciousness is related to complexity - independent of how the latter is measured.

neuroscience↗

bioRxiv2019

Serotonergic Psychedelics LSD & Psilocybin Increase theFractal Dimension of Cortical Brain Activity in Spatial and Temporal Domains

Psychedelic drugs, such as psilocybin and LSD, represent unique tools for researchers in-vestigating the neural origins of consciousness. Currently, the most compelling theories of how psychedelics exert their effects is by increasing the complexity of brain activity and moving the system towards a critical point between order and disorder, creating more dynamic and complex patterns of neural activity. While the concept of criticality is of central importance to this theory, few of the published studies on psychedelics investigate it directly, testing instead related measures such as algorithmic complexity or Shannon entropy. We propose using the fractal dimension of functional activity in the brain as a measure of complexity since findings from physics suggest that as a system organizes towards criticality, it tends to take on a fractal structure. We tested two different measures of fractal dimension, one spatial and one temporal, using fMRI data from volunteers under the influence of both LSD and psilocybin. The first was the fractal dimension of cortical functional connectivity networks and the second was the fractal dimension of BOLD time-series. We were able to show that both psychedelic drugs significantly increased the fractal dimension of functional connectivity networks, and that LSD significantly increased the fractal dimension of BOLD signals, with psilocybin showing a non-significant trend in the same direction. With both LSD and psilocybin, we were able to localize changes in the fractal dimension of BOLD signals to brain areas assigned to the dorsal-attentional network. These results show that psychedelic drugs increase the fractal character of activity in the brain and we see this as an indicator that the changes in consciousness triggered by psychedelics are associated with evolution towards a critical zone.\n\nAuthor SummaryThe unique state of consciousness produced by psychedelic drugs like LSD and psilocybin (the active component in magic mushrooms) are potentially useful tools for discovering how specific changes in the brain are related to differences in perception and thought patterns. Past research into the neuroscience of psychedelics has led to the proposal of a general theory of brain function and consciousness: the Entropic Brain Hypothesis proposes that consciousness emerges when the brain is sitting near a critical tipping point between order and chaos and that the mind-expanding elements of the psychedelic experience are caused by the brain moving closer to that critical transition point. Physicists have discovered that near this critical point, many different kinds of systems, from magnets to ecosystems, take on a distinct, fractal structure. Here, we used two measures of fractal-quality of brain activity, as seen in fMRI, to test whether the activity of the brain on psychedelics is more fractal than normal. We found evidence that this is the case and interpret that as supporting the theory that, psychedelic drugs are move the brain towards a more critical state.

neuroscience↗