Biology subjects

Uddin, L.

Publications and source records attributed to Uddin, L..

7 recordsLinked to original sources

bioRxiv2023

"The Brain is...": A Survey of The Brain's Many Definitions

A reader of the peer-reviewed neuroscience literature will often encounter expressions like the following: the brain is a dynamic system, the brain is a complex network, or the brain is a highly metabolic organ. These expressions attempt to define the essential functions and properties of the mammalian or human brain in a simple phrase or sentence, sometimes using metaphors. We sought to survey the most common phrases of the form the brain is... in the biomedical literature to provide insights into current conceptualizations of the brain. Utilizing text analytic tools applied to a large sample (> 4 million) of peer-reviewed full-text articles and abstracts, we extracted several thousand phrases of the form the brain is... and identified over a dozen frequently appearing phrases. The most used phrases included metaphors (e.g., the brain as a information processor or prediction machine) and descriptions of essential functions (e.g., a central organ of stress adaptation) or properties (e.g., a highly vascularized organ). Comparison of these phrases with those involving other bodily organs (e.g. the heart, liver, etc.) highlighted common phrases between the brain and other organs, such as the heart as a complex dynamic system. However, the brain was unique among organs in the number and diversity of metaphors ascribed to it. The results of our analysis underscore the diversity of qualities and functions commonly attributed to the brain in the biomedical literature and suggest a range of conceptualizations that defy unification.

neuroscience↗

bioRxiv2023

A Unified Physiological Process Links Global Patterns of Functional MRI, Respiratory Activity, and Autonomic Signaling

The brain is closely attuned to visceral signals from the bodys internal environment, as evidenced by the numerous associations between neural, hemodynamic, and peripheral physiological signals. We show that these brain-body co-fluctuations can be captured by a single spatiotemporal pattern. Across several independent samples, as well as single-echo and multi-echo fMRI data acquisition sequences, we identify widespread co-fluctuations in the low-frequency range (0.01 - 0.1 Hz) between resting-state global fMRI signals, neural activity, and a host of autonomic signals spanning cardiovascular, pulmonary, exocrine and smooth muscle systems. The same brain-body co-fluctuations observed at rest are elicited by arousal induced by cued deep breathing and intermittent sensory stimuli, as well as spontaneous phasic EEG events during sleep. Further, we show that the spatial structure of global fMRI signals is maintained under experimental suppression of end-tidal carbon dioxide (PETCO2) variations, suggesting that respiratory-driven fluctuations in arterial CO2 accompanying arousal cannot explain the origin of these signals in the brain. These findings establish the global fMRI signal as a significant component of the arousal response governed by the autonomic nervous system.

neuroscience↗

bioRxiv2022

Global signal topography in the human brain differs systematically across the lifespan

The global signal (GS) in resting-state fMRI, known to contain artifacts and non-neuronal physiological signals, also contains important neural information related to individual state and trait characteristics. Here we show distinct linear and curvilinear lifespan patterns of GS topography in a cross-sectional lifespan sample, demonstrating its importance for consideration in studies of development and aging. Subcortical brain regions such as the thalamus and putamen show linear associations with the GS across the lifespan. The thalamus has stronger coupling in older-age individuals compared with younger-aged individuals, while the putamen has stronger coupling in younger individuals compared with older individuals. The subcortical nucleus basalis shows a u-shaped pattern similar to cortical regions within the lateral frontoparietal network and dorsal attention network, where coupling with the GS is stronger at early and old age, with weaker coupling in middle age. This differentiation in coupling strength between subcortical and cortical brain activity across the lifespan supports a dual-layer model of GS composition, where subcortical aspects of the GS are differentiated from cortical aspects of the GS. We find that these subcortical-cortical contributions to the GS depend strongly on the lifespan stage of individuals. Our findings demonstrate how neurobiological information within the GS differs across development and highlight the need to carefully consider whether or not to remove this signal when investigating age-related functional differences in the brain.

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bioRxiv2020

Dissecting the midlife crisis: Disentangling social, personality and demographic determinants in social brain anatomy

In any stage of life, humans crave social connection with other people. In midlife, transitions in social networks can be related to new leadership roles at work or becoming a caregiver for aging parents. Previous neuroimaging studies have reported that during midlife, especially the medial prefrontal cortex (mPFC) undergoes structural remodeling changes. Social behavior, personality predisposition, and demographic profile all bear intimate relation with the mPFC according to separate literature streams. To integrate these three areas commonly studied in isolation, we explicitly modeled their unique links with brain structure using a fully probabilistic framework. We weighed against each other a rich collection of 40 traits with their interindividual variation in social brain morphology in ~10,000 middle-aged UK Biobank participants (40-69 years at recruitment). Across conducted analyses, household size and daily routine schedules showed several of the largest effects in explaining variation in social brain regions. We revealed male-biased effects in the dorsal mPFC and amygdala for job income, and a female-biased effect in the ventral mPFC for health satisfaction. Our population investigation offers a more complete perspective into how adults at the midlife milestone may navigate life depending on their identity and status.

neuroscience↗

bioRxiv2019

Disambiguating the role of blood flow and global signal with Partial Information Decomposition

In resting state functional magnetic resonance imaging (rs-fMRI) a common strategy to reduce the impact of physiological noise and other artifacts on the data is to regress out the global signal using global signal regression (GSR). Yet, GSR is one of the most controversial preprocessing techniques for rs-fMRI. It effectively removes non-neuronal artifacts, but at the same time it alters correlational patterns in unpredicted ways. Furthermore the global signal includes neural BOLD signal by construction, and is consequently related to neural and behavioral function. Performing GSR taking into account the underlying physiology (mainly the blood arrival time) has been proved to be beneficial. From these observations we aimed to: 1) characterize the effect of GSR on network-level functional connectivity in a large dataset; 2) assess the complementary role of global signal and vessels; and 3) use the framework of partial information decomposition to further look into the joint dynamics of the global signal and vessels, and their respective influence on the dynamics of cortical areas. We observe that GSR affects intrinsic connectivity networks in the connectome in a non-uniform way. Furthermore, by estimating the predictive information of blood flow and the global signal using partial information decomposition, we observe that both signals are present in different amounts across intrinsic connectivity networks. Simulations showed that differences in blood arrival time can largely explain this phenomenon. With these results we confirm network-specific effects of GSR and the importance of taking blood flow into account for improve denoising methods. Using GSR but not correcting for blood flow might selectively introduce physiological artifacts across intrinsic connectivity networks that distort the functional connectivity estimates.

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bioRxiv2017

Inter-subject phase synchronization and the dynamics of human cognition

Massive whole-brain blood-oxygen-level dependent (BOLD) signal modulation (up to 95% of brain voxels) in response to task stimuli has recently been reported in functional MRI investigations. These findings have two implications. First, they highlight inability of a conventional 'top-down' general linear model approach to capture all forms of task-driven brain activity. Second, as opposed to a static 'active' or 'non-active' localization theory of the neural implementation of cognitive processes, functional neuroimaging should develop and pursue dynamical theories of cognition involving the dynamic interactions of all brain networks, in line with psychological constructionist theories of cognition. In this study, we describe a novel exploratory, bottom-up approach that directly estimates task-driven brain activity regardless of whether it follows an a priori reference function. Leveraging the property that task-driven brain activity is associated with reductions in BOLD signal variability, we combine the tools of instantaneous phase synchronization and independent component analysis to characterize whole-brain task-driven activity in terms of group-wise similarity in temporal signal dynamics of brain networks. We applied this novel framework to task fMRI data from a motor, theory of mind and working memory task provided through the Human Connectome Project. We discovered a large number of brain networks that dynamically synchronized to various features of the task scan, some overlapping with areas identified as 'active' in the top-down GLM approach. Using the results provided through this novel approach, we provide a more comprehensive description of cognitive processes whereby task-related brain activity is not restricted to dichotomous 'active' or 'non-active' inferences, but is characterized by the temporal dynamics of brain networks across time.

neuroscience↗

bioRxiv2017

Data-Driven Extraction of a Nested Structure of Human Cognition

AbstractDecades of cognitive neuroscience research have revealed two basic facts regarding task-driven brain activation patterns. First, distinct patterns of activation occur in response to different task demands. Second, a superordinate, dichotomous pattern of activation/de-activation, is commonly observed across a variety of task demands. We explore the possibility that a hierarchical model incorporates these two observed brain activation phenomena into a unifying framework. We apply a latent variable approach, exploratory bi-factor analysis, to a large set of brain activation patterns to determine the potential existence of a nested structure of factors that underlies a variety of commonly observed activation patterns. We find that a general factor, associated with a superordinate brain activation/de-activation pattern, explained the majority of the variance (52.37%). The bi-factor analysis also revealed several sub-factors that explained an additional 31.02% of variance in brain activation patterns, associated with different manifestations of the superordinate brain activation/de-activation pattern, each emphasizing different contexts in which the task demands occurred. Importantly, this nested factor structure provided better overall fit to the data compared with a non-nested factor structure model. These results point to domain-general psychological process, representing a focused awareness process or attentional episode that is variously manifested according to the sensory modality of the stimulus and degree of cognitive processing. This novel model provides the basis for constructing a biologically-informed, data-driven taxonomy of psychological processes.

neuroscience↗