Biology subjects

Bolt, T.

Publications and source records attributed to Bolt, T..

6 recordsLinked to original sources

bioRxiv2025

Distributed fMRI patterns coupled to low-frequency cardiorespiratory dynamics provide markers of aging

How aging affects brain-body connections can be investigated through changes in the coupling between functional magnetic resonance imaging (fMRI) signals and bodily autonomic processes across the adult lifespan. Recent studies using univariate approaches have identified age-related changes in the association between fMRI signals from multiple individual brain regions and low-frequency respiratory and cardiac activity. Here, we investigate if whole-brain spatial fMRI patterns associated with low-frequency physiological processes (heart rate and respiratory volume fluctuations) present generalizable changes with age. Data from human participants of both sexes are included in the analysis. We find that chronological age can be predicted statistically beyond chance from patterns of low-frequency fMRI-physiology coupling, even after accounting for individual differences in physiological signal characteristics and brain anatomy. Notably, brain areas implicated in central autonomic regulation, including nodes within salience and ventral attention networks (e.g., insula and middle cingulate cortex), are amongst the strongest contributors to age prediction. Further, we observe that after removing physiological effects from fMRI data, the residual blood oxygen level-dependent (BOLD) signal variability is still a reliable indicator of age. Together, these findings underscore the close integration between brain and body physiology, and highlight this interaction as a potential biomarker of the aging process. Significance StatementThe association between brain activity and respiratory or cardiac activity is often dismissed as "noise" in functional magnetic resonance imaging (fMRI) studies. However, emerging evidence suggests that coupling between fMRI and peripheral physiological signals can provide valuable insight into the brain-body connection. In this study, we show that whole brain patterns of coupling between fMRI signals and low-frequency respiratory and cardiac processes can reliably predict age across the adult lifespan. Brain regions involved in autonomic regulation, such as insula and cingulate cortex, were among the most informative predictors of age. These findings suggest that fMRI-physiology coupling may capture aging-related changes in brain vascular health and autonomic function and may have broader relevance for tracking disease-related disruptions in brain-body interaction.

neuroscience↗

bioRxiv2023

Sequence characterization of T, Bip, and Phbw demonstrates the role of MYB-bHLH-WD40 complexes and temperature in common bean seed color pattern formation

Seed colors and color patterns are critical for the survival of wild plants and the consumer appeal of crops. In common bean, a major global staple, these patterns are also critical for determining market classes, yet the genetic and environmental control of many pigmentation patterns remains unresolved. In this study, we genetically mapped variation for three important seed pattern loci, T, Bip, and phbw, which co-segregated completely with PvTTG1, PvMYC1, and PvTT8, respectively. Proteins encoded by these genes are predicted to work together in MYB-bHLH-WD40 (MBW) complexes, propagating flavonoid biosynthesis across the seed coat. Whole-genome sequencing of 37 diverse accessions identified putative mutations in each gene, including seven unique parallel mutations in T (PvTTG1) and a non-synonymous SNP in a conserved residue in bipana (PvMYC1). A 612 bp intron deletion in phbw (PvTT8) eliminated motifs conserved since the origins of the Papilionoidea and corresponded to a 20-fold reduction in transcript abundance. Mutations in MBW candidate genes for Z (PvTT2) and Sellatus (WDR) were also identified. In multi-location field trials with seven varieties with partial seed coat patterning, pigmented seed coat area was highly unstable and correlated with temperature, with up to 11-fold differences in pigmented area between the warmest and the coolest environments. In controlled growth chamber conditions, an increase of 4 {degrees}C was sufficient to cause pigmentation on an additional 21% of the seed coat area. Our results shed light on the fundamental activation of flavonoid biosynthesis in common bean and will be instrumental for maximizing consumer appeal in this nutritious staple crop. Summary- Seed colors and patterns are critical for the survival of wild plants, and are important in differentiating crop market classes, but the genetic control of these in the staple crop common bean (Phaseolus vulgaris) is largely unknown. - The genetic, transcriptional, and environmental basis of common bean seed color patterning was explored through QTL mapping, whole-genome sequencing, RT-qPCR, and automated pigmentation quantification of seed grown in multi-location field trials and growth chamber environments. - MYB-bHLH-WD40 complex-forming genes PvTTG1, PvMYC1, and PvTT8 co-segregated completely with the color patterning genes T, Bip, and phbw. Mutations were identified in each gene, including seven unique parallel mutations in T (PvTTG1), a non-synonymous SNP in a conserved residue in bipana (PvMYC1), and an intron deletion in phbw (PvTT8) eliminating highly conserved motifs and corresponding to 20-fold lower PvTT8 transcript abundance. Mutations in MBW candidate genes Z (PvTT2) and Sellatus (WDR) were also identified. In multi-location field trials, pigmented seed coat area was highly unstable and corresponded to temperature. In growth chamber conditions, an increase of 4 {degrees}C caused pigmentation on an additional 21% of the seed coat area. - Our results highlight the critical interaction between MYB-bHLH-WD40 complex components and temperature in establishing seed pattern diversity.

plant biology↗

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.

neuroscience↗

bioRxiv2021

A Genome-Scale TF-DNA Interaction Network of Transcriptional Regulation of Arabidopsis Primary and Specialized Metabolism

In single-celled microbes, transcriptional regulation by single transcription factors is sufficient to shift primary metabolism. Corresponding genome-level transcriptional regulatory maps of metabolism reveal the underlying design principles responsible for these shifts as a model in which master regulators largely coordinate specific metabolic pathways. Relative to individual microbes, plant metabolism is more complex. Primary and specialized metabolism occur within innumerable cell types, and their reactions shift depending on internal and external cues. Given the importance of plants and their metabolites in providing humanity with food, fiber and medicine, we set out to develop a genome-scale transcriptional regulatory map of Arabidopsis metabolic genes. A comprehensive set of protein-DNA interactions between Arabidopsis thaliana transcription factors and promoters of primary metabolism and specialized metabolism were mapped. To demonstrate the utility of this resource, we identified and functionally validated regulators of the TCA cycle. The resulting network suggests that plant metabolic design principles are distinct from that of microbes. Instead, metabolism appears to be transcriptionally coordinated via developmental- and stress-conditional processes that can coordinate across primary and specialized metabolism. These data represent the most comprehensive resource of interactions between TFs and metabolic genes in plants.

systems biology↗

bioRxiv2019

Ontological Dimensions of Cognitive-Neural Mappings

The growing literature reporting results of cognitive-neural mappings has increased calls for an adequate organizing ontology, or taxonomy, of these mappings. This enterprise is non-trivial, as relevant dimensions that might contribute to such an ontology are not yet agreed upon. We propose that any candidate dimensions should be evaluated on their ability to explain observed differences in functional neuroimaging activation patterns. In this study, we use a large sample of task-based functional magnetic resonance imaging (task-fMRI) results and a data-driven strategy to identify these dimensions. First, using a data-driven dimension reduction approach and multivariate distance matrix regression (MDMR), we quantify the variance among activation maps that is explained by existing ontological dimensions. We find that task paradigm categories explain the most variance among task-activation maps than other dimensions, including latent cognitive categories. Surprisingly, study ID, or the study from which each activation map was reported, explained close to 50% of the variance in activation patterns. Using a clustering approach that allows for overlapping clusters, we derived data-driven latent activation states, associated with re-occurring configurations of the canonical fronto-parietal/salience, sensory-motor, and default mode network activation patterns. Importantly, with only four data-driven latent dimensions, one can explain greater variance among activation maps than all conventional ontological dimensions combined. These latent dimensions may inform a data-driven cognitive ontology, and suggest that current descriptions of cognitive processes and the tasks used to elicit them do not accurately reflect activation patterns commonly observed in the human brain.

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↗