Habituation of the stress response multiplex to repeated cold pressor exposure

Frontiers in Physiology · 2023 · 22 citations

• Medicine
• Cardiology
• Anesthesia

Humans show remarkable habituation to aversive events as reflected by changes of both subjective report and objective measures of stress. Although much experimental human research focuses on the effects of stress, relatively little is known about the cascade of physiological and neural responses that contribute to stress habituation. The cold pressor test (CPT) is a common method for inducing acute stress in human participants in the laboratory; however, there are gaps in our understanding of the global state changes resulting from this stress-induction technique and how these responses change over multiple exposures. Here, we measure the stress response to repeated CPT exposures using an extensive suite of physiologic measures and state-of-the-art analysis techniques. In two separate sessions on different days, participants underwent five 90 s CPT exposures of both feet and five warm water control exposures, while electrocardiography (ECG), impedance cardiography, continuous blood pressure, pupillometry, scalp electroencephalography (EEG), salivary cortisol and self-reported pain assessments were recorded. A diverse array of adaptive responses are reported that vary in their temporal dynamics within each exposure as well as habituation across repeated exposures. During cold-water exposure there was a cascade of changes across several cardiovascular measures (elevated heart rate (HR), cardiac output (CO) and Mean Arterial Pressure (MAP) and reduced left ventricular ejection time (LVET), stroke volume (SV) and high-frequency heart rate variability (HF)). Increased pupil dilation was observed, as was increased power in low-frequency bands (delta and theta) across frontal EEG electrode sites. Several cardiovascular measures also habituated over repeated cold-water exposures (HR, MAP, CO, SV, LVET) as did pupil dilation and alpha frequency activity across the scalp. Anticipation of cold water induced stress effects in the time-period immediately prior to exposure, indexed by increased pupil size and cortical disinhibition in the alpha and beta frequency bands across central scalp sites. These results provide comprehensive insight into the evolution of a diverse array of stress responses to an acute noxious stressor, and how these responses adaptively contribute to stress habituation.

StressNet: Detecting Stress in Thermal Videos

2021 · 22 citations

• Computer Science
• Artificial Intelligence
• Computer Science

Precise measurement of physiological signals is critical for the effective monitoring of human vital signs. Recent developments in computer vision have demonstrated that signals such as pulse rate and respiration rate can be extracted from digital video of humans, increasing the possibility of contact-less monitoring. This paper presents a novel approach to obtaining physiological signals and classifying stress states from thermal video. The proposed network–"StressNet"–features a hybrid emission representation model that models the direct emission and absorption of heat by the skin and underlying blood vessels. This results in an information-rich feature representation of the face, which is used by spatio-temporal network for reconstructing the ISTI ( Initial Systolic Time Interval : a measure of change in cardiac sympathetic activity that is considered to be a quantitative index of stress in humans). The reconstructed ISTI signal is fed into a stress-detection model to detect and classify the individual’s stress state (i.e. stress or no stress). A detailed evaluation demonstrates that Stress-Net achieves estimated the ISTI signal with 95% accuracy and detect stress with average precision of 0.842.

QSIPrep: an integrative platform for preprocessing and reconstructing diffusion MRI data

Nature Methods · 2021 · 315 citations

• Computer Science
• Computer Science
• Data Mining

Counterfactual Vision-and-Language Navigation via Adversarial Path Sampler

Lecture notes in computer science · 2020 · 76 citations

• Computer Science
• Artificial Intelligence
• Computer Science

Progesterone shapes medial temporal lobe volume across the human menstrual cycle

NeuroImage · 2020 · 93 citations

• Medicine
• Anatomy
• Biology

The rhythmic production of sex steroid hormones is a central feature of the mammalian endocrine system. In rodents and nonhuman primates, sex hormones are powerful regulators of hippocampal subfield morphology. However, it remains unknown whether intrinsic fluctuations in sex hormones alter hippocampal morphology in the human brain. In a series of dense-sampling studies, we used high-resolution imaging of the medial temporal lobe (MTL) to determine whether endogenous fluctuations (Study 1) and exogenous manipulation (Study 2) of sex hormones alter MTL volume over time. Across the menstrual cycle, intrinsic fluctuations in progesterone were associated with volumetric changes in CA2/3, entorhinal, perirhinal, and parahippocampal cortex. Chronic progesterone suppression abolished these cycle-dependent effects and led to pronounced volumetric changes in entorhinal cortex and CA2/3 relative to freely cycling conditions. No associations with estradiol were observed. These results establish progesterone's ability to rapidly and dynamically shape MTL morphology across the human menstrual cycle.

Functional reorganization of brain networks across the human menstrual cycle

NeuroImage · 2020 · 212 citations

• Neuroscience
• Psychology
• Biology

The brain is an endocrine organ, sensitive to the rhythmic changes in sex hormone production that occurs in most mammalian species. In rodents and nonhuman primates, estrogen and progesterone's impact on the brain is evident across a range of spatiotemporal scales. Yet, the influence of sex hormones on the functional architecture of the human brain is largely unknown. In this dense-sampling, deep phenotyping study, we examine the extent to which endogenous fluctuations in sex hormones alter intrinsic brain networks at rest in a woman who underwent brain imaging and venipuncture for 30 consecutive days. Standardized regression analyses illustrate estrogen and progesterone's widespread associations with functional connectivity. Time-lagged analyses examined the temporal directionality of these relationships and suggest that cortical network dynamics (particularly in the Default Mode and Dorsal Attention Networks, whose hubs are densely populated with estrogen receptors) are preceded-and perhaps driven-by hormonal fluctuations. A similar pattern of associations was observed in a follow-up study one year later. Together, these results reveal the rhythmic nature in which brain networks reorganize across the human menstrual cycle. Neuroimaging studies that densely sample the individual connectome have begun to transform our understanding of the brain's functional organization. As these results indicate, taking endocrine factors into account is critical for fully understanding the intrinsic dynamics of the human brain.

SSCR: Iterative Language-Based Image Editing via Self-Supervised Counterfactual Reasoning

2020 · 26 citations

• Computer Science
• Computer Science
• Artificial Intelligence

Iterative Language-Based Image Editing (IL-BIE) tasks follow iterative instructions to edit images step by step. Data scarcity is a significant issue for ILBIE as it is challenging to collect large-scale examples of images before and after instruction-based changes. However, humans still accomplish these editing tasks even when presented with an unfamiliar image-instruction pair. Such ability results from counterfactual thinking and the ability to think about alternatives to events that have happened already. In this paper, we introduce a Self-Supervised Counterfactual Reasoning (SSCR) framework that incorporates counterfactual thinking to overcome data scarcity. SSCR allows the model to consider out-ofdistribution instructions paired with previous images. With the help of cross-task consistency (CTC), we train these counterfactual instructions in a self-supervised scenario. Extensive results show that SSCR improves the correctness of ILBIE in terms of both object identity and position, establishing a new state of the art (SOTA) on two IBLIE datasets (i-CLEVR and CoDraw). Even with only 50% of the training data, SSCR achieves a comparable result to using complete data.

QSIPrep: An integrative platform for preprocessing and reconstructing diffusion MRI

bioRxiv (Cold Spring Harbor Laboratory) · 2020 · 25 citations

• Computer Science
• Computer Science
• Artificial Intelligence

ABSTRACT Diffusion-weighted magnetic resonance imaging (dMRI) has become the primary method for non-invasively studying the organization of white matter in the human brain. While many dMRI acquisition sequences have been developed, they all sample q-space in order to characterize water diffusion. Numerous software platforms have been developed for processing dMRI data, but most work on only a subset of sampling schemes or implement only parts of the processing workflow. Reproducible research and comparisons across dMRI methods are hindered by incompatible software, diverse file formats, and inconsistent naming conventions. Here we introduce QSIPrep, an integrative software platform for the processing of diffusion images that is compatible with nearly all dMRI sampling schemes. Drawing upon a diverse set of software suites to capitalize upon their complementary strengths, QSIPrep automatically applies best practices for dMRI preprocessing, including denoising, distortion correction, head motion correction, coregistration, and spatial normalization. Throughout, QSIPrep provides both visual and quantitative measures of data quality as well as “glass-box” methods reporting. Taken together, these features facilitate easy implementation of best practices for processing of diffusion images while simultaneously ensuring reproducibility.

Dynamic community detection reveals transient reorganization of functional brain networks across a female menstrual cycle

Network Neuroscience · 2020 · 64 citations

• Neuroscience
• Psychology
• Biology

Sex steroid hormones have been shown to alter regional brain activity, but the extent to which they modulate connectivity within and between large-scale functional brain networks over time has yet to be characterized. Here, we applied dynamic community detection techniques to data from a highly sampled female with 30 consecutive days of brain imaging and venipuncture measurements to characterize changes in resting-state community structure across the menstrual cycle. Four stable functional communities were identified, consisting of nodes from visual, default mode, frontal control, and somatomotor networks. Limbic, subcortical, and attention networks exhibited higher than expected levels of nodal flexibility, a hallmark of between-network integration and transient functional reorganization. The most striking reorganization occurred in a default mode subnetwork localized to regions of the prefrontal cortex, coincident with peaks in serum levels of estradiol, luteinizing hormone, and follicle stimulating hormone. Nodes from these regions exhibited strong intranetwork increases in functional connectivity, leading to a split in the stable default mode core community and the transient formation of a new functional community. Probing the spatiotemporal basis of human brain-hormone interactions with dynamic community detection suggests that hormonal changes during the menstrual cycle result in temporary, localized patterns of brain network reorganization.