Biology subjects

Aquino, K. M.

Publications and source records attributed to Aquino, K. M..

15 recordsLinked to original sources

bioRxiv2025

Cortical-layer EEG-fMRI at 7T: experimental setup and analysis pipeline to elucidate generating mechanisms of alpha oscillations

Alpha band (8-13Hz) electroencephalography (EEG) oscillations play a key role in cognition, but their generating mechanisms are still poorly understood. Most studies investigating laminar origins of alpha oscillations have been conducted on animals using invasive intracranial recordings. To relate these findings to human alpha generation, non-invasive techniques need to be developed. Layer functional Magnetic Resonance Imaging (fMRI) at ultra-high field (UHF, 7T) allows for the interrogation of brain responses across cortical depths and combined with simultaneous EEG, provides the opportunity to gain new insight into human alpha generation mechanisms. This work establishes a framework to study the generating mechanisms of electrophysiological signals non-invasively in humans using simultaneous EEG layer-fMRI. Data were acquired on 10 participants during an eyes closed/eyes open paradigm. We showed that in 9/10 participants the quality of EEG and Blood Oxygenation Level Dependent (BOLD) fMRI data were sufficient to observe a significant negative correlation between EEG alpha power and the BOLD signal in visual cortex grey matter to the eyes open/eyes closed task. "Deveining" was performed to overcome the increase in BOLD signal toward the pial surface due to draining veins, and the effects of each of the steps in the deveining analysis on the cortical depth profiles of the negative alpha-BOLD correlations studied. The largest effect was dependent on the exclusion of voxels in the tissue immediately surrounding veins. Following deveining, the cortical depth profiles showed the negative alpha-BOLD correlations were significantly weaker in the middle depths compared with deep and superficial depths. When a boxcar rather than EEG alpha power was used to model the task, this depth-dependence was not seen, suggesting this was specific to spontaneous alpha-power modulations. In conclusion, we have established a method to non-invasively interrogate the origins of electrophysiological signals. Our alpha-BOLD depth profiles suggest the alpha signal to an eyes open-closed task is generated in superficial and deep layers suggesting top-down processes.

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bioRxiv2025

Geometric influences on the regional organization of the mammalian brain

The mammalian brain comprises anatomically and functionally distinct regions, yet the principles governing their organization across scales remain unclear. Efforts to map regional boundaries use expert judgement or data-driven clustering of functional, connectional, and/or architectonic properties, but these approaches are often descriptive, have limited generalizability, and do not elucidate generative mechanisms. Here, we develop a geometrically constrained framework for the hierarchical, multiscale regional organization of neocortical and non-neocortical structures. The framework yields parcels at any resolution with high internal homogeneity across hundreds of anatomical, functional, cellular, and molecular properties in humans, macaques, marmosets, and mice, and it generalizes to understudied mammalian species. Finally, we demonstrate that the framework captures the essence of a reaction-diffusion mechanism in which brain geometry shapes the spatial expression of putative patterning molecules that establish a blueprint of regional identity during development. Our findings point to a conserved, universal influence of geometry on mammalian brain organization.

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bioRxiv2024

Reply to: Eigenmodes of the brain: revisiting connectomics and geometry

In Pang, Aquino et al. (2023)1, we presented multiple lines of evidence to indicate that brain geometry plays a previously under-appreciated role in shaping dynamics. Mansour et al. raise concerns about one specific analysis, in which we showed that eigenmodes derived from the geometry of the human cortex can reconstruct diverse activity maps generated with functional magnetic resonance imaging (fMRI) better than eigenmodes derived from connectomes estimated with diffusion MRI (dMRI). Here, we address their concerns and show how our findings and conclusions remain valid.

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bioRxiv2024

Multiscale heterogeneity of white matter morphometry in psychiatric disorders

BackgroundInter-individual variability in neurobiological and clinical characteristics in mental illness is often overlooked by classical group-mean case-control studies. Studies using normative modelling to infer person-specific deviations of grey matter volume have indicated that group means are not representative of most individuals. The extent to which this variability is present in white matter morphometry, which is integral to brain function, remains unclear. MethodsWe applied Warped Bayesian Linear Regression normative models to T1- weighted magnetic resonance imaging data and mapped inter-individual variability in person-specific white matter volume deviations in 1,294 cases (58% male) diagnosed with one of six disorders (attention-deficit/hyperactivity, autism, bipolar, major depressive, obsessive-compulsive and schizophrenia) and 1,465 matched controls (54% male) recruited across 25 scan sites. We developed a framework to characterize deviation heterogeneity at multiple spatial scales, from individual voxels, through inter- regional connections, specific brain regions, and spatially extended brain networks. ResultsThe specific locations of white matter volume deviations were highly heterogeneous across participants, affecting the same voxel in fewer than 8% of individuals with the same diagnosis. For autism and schizophrenia, negative deviations (i.e., areas where volume is lower than normative expectations) aggregated into common tracts, regions and large-scale networks in up to 35% of individuals. ConclusionsThe prevalence of white matter volume deviations was lower than previously observed in grey matter, and the specific location of these deviations was highly heterogeneous when considering voxel-wise spatial resolution. Evidence of aggregation within common pathways and networks was apparent in schizophrenia and autism but not other disorders.

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bioRxiv2023

Reply to: Commentary on Pang et al. (2023) Nature

In Pang et al. (2023)1, we identified a close link between the geometry and function of the human brain by showing that: (1) eigenmodes derived from cortical geometry parsimoniously reconstruct activity patterns recorded with functional magnetic resonance imaging (fMRI); (2) task-evoked cortical activity results from excitations of brain-wide modes with long wavelengths; (3) wave dynamics, constrained by geometry and distance-dependent connectivity, can account for diverse aspects of spontaneous and evoked brain activity; and (4) geometry and function are strongly coupled in the subcortex. Faskowitz et al. (2023)2 raise concerns about the framing of our paper and the specificity of the eigenmode reconstructions in result (1). Here, we address these concerns and show how specificity is established by using appropriate benchmarks.

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bioRxiv2023

Mode-based morphometry: A multiscale approach to mapping human neuroanatomy

Voxel-based morphometry (VBM) and surface-based morphometry (SBM) are two widely used neuroimaging techniques for investigating brain anatomy. These techniques rely on statistical inferences at individual points (voxels or vertices), clusters of points, or a priori regions-of-interest. They are powerful tools for describing brain anatomy, but offer little insights into the generative processes that shape a particular set of findings. Moreover, they are restricted to a single spatial resolution scale, precluding the opportunity to distinguish anatomical variations that are expressed across multiple scales. Drawing on concepts from classical physics, here we develop an approach, called mode-based morphometry (MBM), that can describe any empirical map of anatomical variations in terms of the fundamental, resonant modes--eigenmodes--of brain anatomy, each tied to a specific spatial scale. Hence, MBM naturally yields a multiscale characterization of the empirical map, affording new opportunities for investigating the spatial frequency content of neuroanatomical variability. Using simulated and empirical data, we show that the validity and reliability of MBM are either comparable or superior to classical vertex-based SBM for capturing differences in cortical thickness maps between two experimental groups. Our approach thus offers a robust, accurate, and informative method for characterizing empirical maps of neuroanatomical variability that can be directly linked to a generative physical process.

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bioRxiv2022

Geometric constraints on human brain function

The brains anatomy constrains its function, but precisely how remains unclear. Here, we show that human cortical and subcortical activity, measured with magnetic resonance imaging under spontaneous and diverse task-evoked conditions, can be parsimoniously understood as resulting from excitations of fundamental, resonant modes of the brains geometry (i.e., its shape) rather than modes from complex inter-regional connectivity, as classically assumed. We then use these modes to show that task-evoked activations across >10,000 brain maps are not confined to focal areas, as widely believed, but instead excite brain-wide modes with wavelengths spanning >60 mm. Finally, we confirm theoretical predictions that the close link between geometry and function is explained by a dominant role for wave-like dynamics, showing that such dynamics can reproduce numerous canonical spatiotemporal properties of spontaneous and evoked recordings. Our findings challenge prevailing views of brain function and identify a previously under-appreciated role of brain geometry that is predicted by a unifying and physically principled approach.

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bioRxiv2021

Modeling spatial, developmental, physiological, and topological constraints on human brain connectivity

The complex connectivity of nervous systems is thought to have been shaped by competitive selection pressures to minimize wiring costs and support adaptive function. Accordingly, recent modeling work indicates that stochastic processes, shaped by putative trade-offs between the cost and value of each connection, can successfully reproduce many topological properties of macroscale human connectomes measured with diffusion magnetic resonance imaging. Here, we derive a new formalism with the aim to more accurately capture the competing pressures of wiring cost minimization and topological complexity. We further show that model performance can be improved by accounting for developmental changes in brain geometry and associated wiring costs, and by using inter-regional transcriptional or microstructural similarity rather than topological wiring-rules. However, all models struggled to capture topologies spatial embedding. Our findings highlight an important role for genetics in shaping macroscale brain connectivity and indicate that stochastic models offer an incomplete account of connectome organization.

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bioRxiv2021

The individuality of shape asymmetries of the human cerebral cortex

Asymmetries of the cerebral cortex are found across diverse phyla and are particularly pronounced in humans, with important implications for brain function and disease. However, many prior studies have confounded asymmetries due to size with those due to shape. Here, we introduce a novel approach to characterize asymmetries of the whole cortical shape, independent of size, across different spatial frequencies using magnetic resonance imaging data in three independent datasets. We find that cortical shape asymmetry is highly individualized and robust, akin to a cortical fingerprint, and identifies individuals more accurately than size-based descriptors, such as cortical thickness and surface area, or measures of inter-regional functional coupling of brain activity. Individual identifiability is optimal at coarse spatial scales ([~]37 mm wavelength), and shape asymmetries show scale-specific associations with sex and cognition, but not handedness. While unihemispheric cortical shape shows significant heritability at coarse scales ([~]65 mm wavelength), shape asymmetries are determined primarily by subject-specific environmental effects. Thus, coarse-scale shape asymmetries are highly personalized, sexually dimorphic, linked to individual differences in cognition, and are primarily driven by stochastic environmental influences.

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bioRxiv2021

On the intersection between data quality and dynamical modelling of large-scale fMRI signals

Large-scale dynamics of the brain are routinely modelled using systems of nonlinear dynamical equations that describe the evolution of population-level activity, with distinct neural populations often coupled according to an empirically measured structural connection matrix. This modelling approach has been used to generate insights into the neural underpinnings of spontaneous brain dynamics, as recorded with techniques such as resting state functional MRI (fMRI). In fMRI, researchers have many degrees of freedom in the way that they can process the data and recent evidence indicates that the choice of pre-processing steps can have a major effect on empirical estimates of functional connectivity. However, the potential influence of such variations on modelling results are seldom considered. Here we show, using three popular whole-brain dynamical models, that different choices during fMRI preprocessing can dramatically affect model fits and interpretations of findings. Critically, we show that the ability of these models to accurately capture patterns in fMRI dynamics is mostly driven by the degree to which they fit global signals rather than interesting sources of coordinated neural dynamics. We show that widespread deflections can arise from simple global synchronisation. We introduce a simple two-parameter model that captures these fluctuations and which performs just as well as more complex, multi-parameter biophysical models. From our combined analyses of data and simulations, we describe benchmarks to evaluate model fit and validity. Although most models are not resilient to denoising, we show that relaxing the approximation of homogeneous neural populations by more explicitly modelling inter-regional effective connectivity can improve model accuracy at the expense of increased model complexity. Our results suggest that many complex biophysical models may be fitting relatively trivial properties of the data, and underscore a need for tighter integration between data quality assurance and model development.

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bioRxiv2020

Dynamical consequences of regional heterogeneity in the brains transcriptional landscape

Brain regions vary in their molecular and cellular composition, but how this heterogeneity shapes neuronal dynamics is unclear. Here, we investigate the dynamical consequences of regional heterogeneity using a biophysical model of whole-brain functional magnetic resonance imaging (MRI) dynamics in humans. We show that models in which transcriptional variations in excitatory and inhibitory receptor (E:I) gene expression constrain regional heterogeneity more accurately reproduce the spatiotemporal structure of empirical functional connectivity estimates than do models constrained by global gene expression profiles and MRI-derived estimates of myeloarchitecture. We further show that regional heterogeneity is essential for yielding both ignition-like dynamics, which are thought to support conscious processing, and a wide variance of regional activity timescales, which supports a broad dynamical range. We thus identify a key role for E:I heterogeneity in generating complex neuronal dynamics and demonstrate the viability of using transcriptional data to constrain models of large-scale brain function.

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bioRxiv2020

Genetic influences on hub connectivity of the human connectome

Brain network hubs are both highly connected and highly inter-connected, forming a critical communication backbone for coherent neural dynamics. The mechanisms driving this organization are poorly understood. Using diffusion-weighted imaging in twins, we identify a major role for genes, showing that they preferentially influence connectivity strength between network hubs of the human connectome. Using transcriptomic atlas data, we show that connected hubs demonstrate tight coupling of transcriptional activity related to metabolic and cytoarchitectonic similarity. Finally, comparing over thirteen generative models of network growth, we show that purely stochastic processes cannot explain the precise wiring patterns of hubs, and that model performance can be improved by incorporating genetic constraints. Our findings indicate that genes play a strong and preferential role in shaping the functionally valuable, metabolically costly connections between connectome hubs.

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bioRxiv2020

Cortical Depth-Dependent Modeling of Visual Hemodynamic Responses

A physiologically based three-dimensional (3D) hemodynamic model is used to predict the experimentally observed blood oxygen level dependent (BOLD) responses versus the cortical depth induced by visual stimuli. Prior 2D approximations are relaxed in order to analyze 3D blood flow dynamics as a function of cortical depth. Comparison of the predictions with experimental data for typical stimuli demonstrates that the full 3D model matches at least as well as previous approaches while requiring significantly fewer assumptions and model parameters (e.g., there is no more need to define depth-specific parameter values for spatial spreading, peak amplitude, and hemodynamic velocity).

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bioRxiv2019

Identifying and removing widespread signal deflections from fMRI data: Rethinking the global signal regression problem.

One of the most controversial procedures in the analysis of resting-state functional magnetic resonance imaging (rsfMRI) data is global signal regression (GSR): the removal, via linear regression, of the mean signal averaged over the entire brain, from voxel-wise or regional time series. On one hand, the global mean signal contains variance associated with respiratory, scanner-, and motion-related artifacts. Its removal via GSR improves various quality control metrics, enhances the anatomical specificity of functional connectivity patterns, and can increase the behavioural variance explained by such patterns. On the other hand, GSR alters the distribution of regional signal correlations in the brain, can induce artifactual anticorrelations, may remove real neural signal, and can distort case-control comparisons of functional-connectivity measures. Global signal fluctuations can be identified by visualizing a matrix of colour-coded signal intensities, called a carpet plot, in which rows represent voxels and columns represent time. Prior to GSR, large, periodic bands of coherent signal changes that affect most of the brain are often apparent; after GSR, these apparent global changes are greatly diminished. Here, using three independent datasets, we show that reordering carpet plots to emphasize cluster structure in the data reveals a greater diversity of spatially widespread signal deflections (WSDs) than previously thought. Their precise form varies across time and participants and GSR is only effective in removing specific kinds of WSDs. We present an alternative, iterative correction method called Diffuse Cluster Estimation and Regression (DiCER), that identifies representative signals associated with large clusters of coherent voxels. DiCER is more effective than GSR at removing diverse WSDs as visualized in carpet plots, reduces correlations between functional connectivity and head-motion estimates, reduces inter-individual variability in global correlation structure, and results in comparable or improved identification of canonical functional-connectivity networks. All code for implementing DiCER and replicating our results is available at https://github.com/BMHLab/DiCER.

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bioRxiv2018

Addressing challenges of high spatial resolution, UHF field fMRI for group analysis of higher-order cognitive tasks; an inter-sensory task directing attention between visual and somatosensory domains.

Functional MRI at ultra-high field (UHF, [≥]7T) provides significant increases in BOLD contrast-to-noise ratio (CNR) compared with conventional field strength (3T), and has been exploited for reduced field-of-view, high spatial resolution mapping of primary sensory areas. Applying these high spatial resolution methods to investigate whole brain functional responses to higher-order cognitive tasks leads to a number of challenges, in particular how to perform robust group-level statistical analyses.\n\nThis study addresses these challenges using an inter-sensory cognitive task which modulates top-down attention at graded levels between the visual and somatosensory domains. At the individual level, highly focal functional activation to the task and task difficulty (modulated by attention levels) were detectable due to the high CNR at UHF. However, to assess group level effects, both anatomical and functional variability must be considered during analysis. We demonstrate the importance of surface over volume normalization and the requirement of no spatial smoothing when assessing highly focal activity. Using novel group analysis on anatomically parcellated brain regions, we show that in higher cognitive areas (parietal and dorsal-lateral-prefrontal cortex) fMRI responses to graded attention levels were modulated quadratically, whilst in visual cortex and VIP, responses were modulated linearly. These group fMRI responses were not seen clearly using conventional second-level GLM analyses, illustrating the limitations of a conventional approach when investigating such focal responses in higher cognitive regions which are more anatomically variable. The approaches demonstrated here complement other advanced analysis methods such as multi-variate pattern analysis, allowing UHF to be fully exploited in cognitive neuroscience.

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