This study investigates how��cerebrospinal fluid (CSF)—the fluid that surrounds and cushions the brain and spinal cord—moves within the brain, and how this movement is influenced by the body’s automatic (autonomic) functions, such as heart rate and breathing. CSF flow is important because it helps remove waste and maintain brain health. While previous research has linked CSF movement to sleep and brain activity, the researchers wanted to isolate the role of the��autonomic nervous system��(the system that controls involuntary processes like heartbeat and respiration).

To do this, they used fMRI scans to observe fluid-related signals in the brain while changing levels of carbon dioxide (CO₂) in participants’ blood—a method that affects blood vessel tone, breathing, and heart function without directly altering brain activity. They found that changes in CSF movement could not be explained simply by physical or mechanical factors. Instead, variations in��heart-rate variability��(natural fluctuations in the time between heartbeats) played a key role in driving slow CSF flow, independent of breathing. Additionally, changes in CO₂ levels mainly affected how frequently heart rate and breathing patterns fluctuated, rather than how strong those fluctuations were.

Overall, the findings suggest that CSF movement is strongly influenced by autonomic regulation, and that both higher and lower-than-normal CO₂ levels can disrupt this process. This highlights a new way to study and potentially control brain fluid dynamics—by adjusting CO₂ levels—without relying on sleep or direct neural activity, offering potential insights into brain health and disease.

��

Fig 1: The predictions of CSF flow dynamics across capnias is based on three different physiological pathways: vascular tone, sympathetic tone, and neuronal activity. According to the vascular-tone theory, CSF fluctuations should be maximal at normocapnia. According to the neuronal-activity theory, CSF fluctuations should be maximized at hypocapnia. Lastly, according to the sympathetic-tone theory, CSF fluctuations should be maximized at hypercapnia. These theories will be tested using empirical data involving different capnias, at which all three variables will be altered.

]]> /valiant/2026/03/26/global-cortical-arousal-effects-in-fmri-reveal-brain-markers-of-state-and-trait-anxiety/ Thu, 26 Mar 2026 19:30:32 +0000 /valiant/?p=6334 Kimberly Kundert-Obando; Terra Lee; Caroline G. Martin; Kamalpreet Kaur; Juan Gomez Lagandara; Yamin Li; Jeffrey M. Harding; Shiyu Wang; Richard Song; Ruoqi Yang; Rithwik Guntaka; Sarah E. Goodale; Roza G. Bayrak; Lucina Q. Uddin; Martin Walter; Jeremy Hogeveen; Catie Chang (2026).��.��Cerebral Cortex, 36(2), bhag008.��

This study explores how brain activity measured with functional MRI (fMRI) can help better understand and personalize the diagnosis and treatment of anxiety. Anxiety is not just a psychological experience—it also involves physical responses in the body, such as changes in heart rate and alertness (called��arousal). These bodily and brain-wide states can influence fMRI signals across the entire brain, often referred to as “global” signals. Traditionally, these global signals have been treated as noise or interference, but the researchers investigated whether they might actually contain meaningful information about anxiety.

To do this, the team analyzed fMRI data to identify patterns related to both��autonomic physiological activity��(automatic body functions like heart rate) and��cortical arousal��(how alert or activated the brain is). They then examined how these patterns relate to two types of anxiety:��state anxiety��(temporary, situation-based anxiety) and��trait anxiety��(a person’s general tendency to feel anxious). The results showed clear links between these global brain signals and both forms of anxiety, with certain brain regions showing stronger associations. These patterns overlapped with well-known brain networks, including the��default mode network, which is involved in self-reflection and internal thoughts.

Overall, the findings suggest that these global fMRI signals carry useful information about how anxiety is represented in the brain. This insight could help improve how anxiety is measured and understood, potentially leading to more personalized approaches to diagnosis and treatment.

Fig 1. Spatial association between global components and anxiety. a and b) Areas in which the FAI was significantly associated with state and trait

anxiety. c and d) Areas in which the GS was significantly associated with state and trait anxiety (GS was analyzed using a negative contrast). Maps show

the t-statistics thresholded at P <0.05 corrected.

This study used high-resolution functional MRI (fMRI), a technique that measures brain or spinal cord activity by detecting changes in blood flow (called the BOLD signal), to better understand how pain from heat is processed in the spinal cord. Specifically, the researchers focused on the lumbar (lower back) region of the spinal cord in rats. They applied a painful heat stimulus (47.5°C) to one hind paw and recorded activity in spinal cord segments L3 to L5, while also collecting data during rest to examine how different regions communicate with each other.

The results showed that painful heat triggered increased activity in specific areas of the spinal cord’s gray matter, particularly in the��dorsal horn��(a region that processes sensory information like pain) and the��intermediate zone��(a region involved in integrating and relaying signals). These responses were strongest in segments L3 and L4, suggesting these areas play a key role in processing heat-related pain. Additionally, when the animals were at rest, the researchers found strong functional connectivity (synchronized activity) between similar regions on both sides of the spinal cord—specifically between dorsal horns and between ventral horns (the latter being more involved in motor control)—but not between different spinal segments. Based on these findings, the authors propose that a part of the L3 segment, known as the intermediate zone, may act as a central hub that helps regulate how pain signals are processed within the spinal cord.

Figure 1.:

High-resolution magnetization transfer contrast (MTC)-weighted anatomical images of the L3–L5 spinal cord from a representative rat. (A) Left: schematic illustration of the research interest of lumbar spinal cord. Right: A typical axial slice of MTC image of lumbar spinal cord with the gray–white matter boundary outlined by yellow lines. (B) Left: 5 axial MTC images acquired with slice 3 centered at the L3/L4 segment. Right: the corresponding axial slice positions overlaid on the coronal image in the middle line. (C) Schematics of 1 imaging session timeline and noxious heat stimulus presentation paradigm. D, dorsal; L, left; R, right; V, ventral.

Sleep causes major shifts in how the brain uses energy and how blood flow changes, but the detailed timing and patterns of these processes are still not fully understood. In this study, researchers combined��functional PET,��EEG, and��fMRI—three powerful brain-imaging tools collected at the same time—to track how metabolism and blood flow change as people transition from wakefulness into��non-rapid eye movement (NREM) sleep.

They found that as the brain moves into NREM sleep,��global glucose metabolism steadily decreases, and at the same time,��large hemodynamic (blood-flow–related) fluctuations��begin to appear. Both of these changes closely follow shifts in��EEG arousal levels, showing how tightly linked these processes are during the onset of sleep.

The study also identified two distinct brain network patterns unique to NREM sleep:

• Aslow (~0.02 Hz), oscillating sensorimotor network��that stays metabolically active and dynamic, meaning sensory and motor areas continue to respond even during sleep.
• Adefault-mode network (DMN)��that shows reduced hemodynamic and metabolic activity, reflecting the decreased self-awareness and internal thought typical of sleep.

These findings help explain why we lose conscious awareness during sleep but can still respond to certain sensory signals. They also highlight the complex and alternating balance of neuronal, blood-flow, and metabolic activity that shapes brain function during sleep.

Finally, this work shows how combining EEG, PET, and MRI can offer powerful new insights into the biological mechanisms behind cognition, arousal, and sleep in humans.

Fig. 1: Trimodal imaging of the electrophysiological, BOLD-fMRI, and fPET-FDG metabolic dynamics accompanying arousal-state transitions.

Study protocol for the design, implementation, and evaluation of the STRATIFY clinical decision support tool for emergency department disposition of patients with heart failure

a,��b��Top: Hypnogram of scored sleep staging and the spectrogram of an occipital EEG electrode; middle: fMRI-based hemodynamic oscillations of the visual network; bottom: fPET-based metabolic dynamics of the default-mode network. Networks were extracted using a public functional atlas. Functional PET signals were temporally detrended according to the arbitrarily chosen initial wakeful period (i.e., removal of the linear trend fitted to the data points within the shaded gray area) only in this plot to help visualize altered slopes at state transitions, with an increase/decrease of TAC slope indicating increased/decreased metabolism. Changes in electrophysiological recordings, fMRI intensities, and glucose metabolism (highlighted with green arrows) were identifiable across sleep-wake cycles (a, subject i) and within the NREM sleep (b, subject ii), mirroring arousal-state transitions (top, inferred from simultaneous EEG recordings). Note that our goal here is to highlight arousal-induced changes in the imaging signals, so we show fMRI and fPET signals from exemplar networks that exhibit strong sleep-wake differences for each modality independently (see group-level results in Fig.����below; to avoid a circular analysis, we re-ran the group-level analysis without including the two illustrative subjects shown here, the findings remained unaltered).

Many brain studies that use functional magnetic resonance imaging (fMRI) do not include measurements of basic body functions like breathing or heart rate, even though these physiological signals can strongly affect brain activity patterns. Natural changes in breathing and heart rate reflect important processes related to thinking, emotion, and overall health, and they can influence how fMRI signals are interpreted.

To address this gap, researchers developed��DeepPhysioRecon, a deep learning model based on a Long Short-Term Memory (LSTM) network. This model can estimate continuous changes in breathing amplitude and heart rate directly from fMRI scans of the whole brain—without the need for separate sensors. The team tested how well the model works across different datasets and experimental conditions and showed that including these reconstructed physiological signals improves how fMRI data are analyzed and interpreted.

This work emphasizes the importance of understanding the connections between the brain and the body. It also introduces a practical, open-source tool that can make fMRI a more effective biomarker for studying human health, cognition, and emotion.

Fig. 1.

DeepPhysioRecon��Pipeline. The pipeline for estimating respiration volume (RV) and heart rate (HR) signals from fMRI time-series dynamics is shown. Regions of interest are defined using four published atlases that had been constructed from different imaging modalities, comprising areas in cerebral cortex, white matter, subcortex, and the ascending arousal network. ROI time-series signals are extracted from the fMRI volumes, detrended, bandpass filtered and downsampled. The preprocessed signals are provided to a candidate network as input channels. A bidirectional LSTM network architecture is adapted for joint estimation. The output of linear layers are RV and HR signals.

In brain imaging research, small changes in functional MRI (fMRI) signals caused by breathing and heart rate have often been dismissed as “noise.” However, these fluctuations actually contain important information about how blood vessels in the brain and the body’s autonomic functions (like heart and breathing control) work.

In this study, we used these physiological signals to examine how brain function changes with age, using data from the large Lifespan Human Connectome Project Aging study. We found that as people get older, their fMRI signals show slower responses linked to breathing, faster responses linked to the heartbeat, and stronger connections between brain and heart signals. Importantly, these changes become especially noticeable after the age of 60, suggesting that declining vascular health and changes in autonomic function play a key role in aging.

We also tested whether these fMRI patterns might be influenced by age-related changes in brain structure, blood flow, or alertness during scans. Overall, our findings highlight major age effects in fMRI signals tied to heart and breathing activity. This work shows that resting-state fMRI can be used not only to study brain connectivity but also to reveal new markers of brain physiology that could help track vascular and autonomic changes with aging.

Fig. 1: Age effects on the spatiotemporal patterns of RV-coupled fMRI dynamics.

a��The cross correlation between the global cortical fMRI signal and RV (positive lag values suggest that fMRI signals lag RV) for different age groups, with shade denoting the standard errors across subjects.��b��Intra-cortical distributions of region-specific fMRI signal lag relative to RV (based on the Schaefer 300-parcel atlas). Regions exhibiting statistically significant between-group temporal lag differences were displayed at the bottom (“Aging II > I”, FDR < 0.05).��c��Tissue-type specific temporal lags relative to RV for each age group. Error bars indicate the standard errors of RV-fMRI temporal lags across subjects, and the shaded gray area highlights ROIs that exhibited statistically significant between-group differences (FDR < 0.05). ROI labels are shown at the bottom.

]]> /valiant/2025/08/25/changes-in-functional-connectivity-in-relapsing-remitting-multiple-sclerosis-spinal-cord-measured-via-region-based-and-data-driven-analyses/ Mon, 25 Aug 2025 20:30:33 +0000 /valiant/?p=5025 Witt, Atlee A., Combes, Anna J.E., Sengupta, Anirban, Zhang, Xinyu, Stubblefield, Seth, McKnight, Colin David, McGonigle, Trey William, McGrath, Megan, Stewart, Isabella, & Sweeney, Grace. (2025). “.” Imaging Neuroscience, 3, IMAG.a.51.

In multiple sclerosis (MS), a disease where the protective covering of nerve fibers is damaged, the symptoms people experience often do not match what standard MRI scans show. Functional MRI (fMRI) can help us understand how the brain and spinal cord’s networks adapt to this structural damage. While fMRI studies in the brain are common, studying the spinal cord is more difficult due to its small size and interference from normal body movements.

In this study, we used resting-state fMRI at 3T to examine the spinal cord of healthy people and those with relapsing-remitting MS. We looked at functional connectivity, which measures how different regions of the spinal cord communicate, and related these findings to clinical measures of disability.

We found that the strongest connectivity occurs between the ventral gray matter regions in both healthy participants and people with MS. Reduced connectivity was linked to poorer mobility. Using a data-driven analysis, we also observed a possible compensatory increase in connectivity in earlier stages of MS compared with later stages.

These results suggest that MS affects how the spinal cord functions and that the nervous system may try to compensate for early damage. Further research is needed, but our findings support the idea that functional changes in the spinal cord are an important part of MS.

Fig.1. Anatomic and functional data processing pipelines. For the anatomic image, vertebral levels were identified on the sagittal T2w image before co-registration of the T2w and multi-echo fast field echo (mFFE) image. For the functional image (fMRI), motion correction was followed by physiologic noise regression using AFNI-RETROICOR and band-pass filtering via a Chebyshev Type II filter. The resulting denoised fMRI and mFFE images were co-registered to one another, and then to the PAM50 template between spinal levels C3 and C5. The gray matter (GM) horns applied on top of the final functional image were extracted from the mFFE image in functional space. ROI correlations were identified between each horn, per slice.

Vigilance, or how alert and awake we are, constantly changes and affects our thinking and behavior. This state can be disrupted in many brain disorders. Certain areas deep in the brain, called neuromodulatory nuclei in the brainstem and basal forebrain, help regulate alertness and drive widespread brain activity and communication. However, it is not well understood how the brain’s large-scale networks change when we shift between being alert and drowsy.

In this study, we used simultaneous EEG (which measures brain electrical activity) and advanced fMRI scans to explore how these arousal centers connect with other parts of the brain depending on vigilance. We found that when people are drowsy, most of these nuclei show stronger global connections, especially to regions like the thalamus, precuneus, and sensory and motor areas. When people are more alert, the nuclei connect most strongly to networks involved in attention, internal thought, and hearing. These patterns remained consistent even after controlling for blood flow effects.

To confirm our findings, we analyzed two large brain imaging datasets and showed that these connectivity patterns are reproducible across different types of fMRI scans. Overall, this study provides new insights into how brain regions that regulate arousal influence large-scale brain activity depending on our level of alertness.

Fig 1 – Reproducible static connectivity profiles of neuromodulatory arousal centers. (a) Static functional connectivity (FC) t-maps of the locus coresuleus (LC), cuneiform/subcuneiform nucleus (CSC), and nucleus basalis of Meynert (NBM) in the VU 3T-ME, HCP 3T, and HCP 7T datasets for the mCSF/WM preprocessing pipeline. The FC t-maps were thresholded at 40% of the top t-values in the gray matter and at p < 0.05 (voxel-wise false discovery rate [FDR]-corrected over the entire gray matter volume). AFNI was used for visualization of the t-maps (@chauffeur_afni function; upper functional range set to the 98^th��percentile). (b) Spatial overlap of the thresholded static FC t-maps of the subcortical arousal regions with 16 canonical brain network templates from the FINDLAB and Melbourne atlases (Shirer et al., 2012;��Tian et al., 2020). A positive value for the spatial overlap corresponds to mostly positive correlations within the brain network template while a negative value corresponds to mostly negative correlations. (c) Spatial reproducibility (Dice similarity coefficient) of the thresholded static FC t-maps between the three fMRI datasets.

]]> /valiant/2025/08/25/the-physiological-component-of-the-bold-signal-impact-of-age-and-heart-rate-variability-biofeedback-training/ Mon, 25 Aug 2025 14:44:59 +0000 /valiant/?p=4966 Song, Richard, Min, Jungwon, Wang, Shiyu, Goodale, Sarah E., Rogge-Obando, Kimberly K., Yang, Ruoqi, Yoo, Hyunjoo, Nashiro, Kaoru, Chen, Jingyuan E., & Mather, Mara M. [2025]. “” Imaging Neuroscience, 3, IMAG.a.99.

Aging is linked to declines in the autonomic nervous system [which controls things like heart rate and breathing], reduced coordination between brain activity and blood flow, and weaker blood vessel responses. These changes may play a role in memory loss and neurodegenerative diseases. To better understand this, we studied how aging affects the way the brain integrates signals from the heart, lungs, and blood flow.

Using two independent brain imaging [resting-state fMRI] datasets with heart and breathing measurements from younger and older adults, we found that older adults showed reduced connections between heart rate, breathing patterns, carbon dioxide levels, and the brain’s oxygenation signal [BOLD signal]. These reductions were most noticeable in brain regions that help regulate automatic body functions, such as the orbitofrontal cortex, anterior cingulate cortex, insula, basal ganglia, and white matter. Younger adults showed stronger heart rate–brain signal coupling in white matter and faster brain responses to breathing and carbon dioxide changes in gray matter.

We also tested whether heart rate variability biofeedback [HRV-BF]—a non-invasive breathing-based training that improves natural heart rate rhythms—could affect these brain-body connections. In older adults, HRV-BF shifted heart rate–brain coupling patterns to look more like those of younger adults.

These results suggest that HRV-BF may help counteract age-related declines in brain and blood vessel function. Overall, this study shows how closely linked body rhythms are to brain health and highlights a potential strategy to support brain function and preserve cognitive health as we age.

Fig 1 – Schematic for the model to determine the physiological component of the BOLD signal. [A] After detrending and normalizing heart rate and respiratory variation, the signals are convolved with CRF and RRF basis functions. For every voxel, a general linear model is used to find beta weights for each of the cardiac and respiration regressors to minimize the error from the original BOLD signal. [B] Example of an original BOLD signal [normalized to percent signal change] and the corresponding physiological component.

Expert programmers tend to be better at coding, but it’s still unclear exactly why. Some researchers have used brain scans (like fMRI) to study how programmers think while doing specific coding tasks, such as understanding code. However, those studies haven’t found consistent brain differences based on experience. One possible reason is that focusing only on tasks may limit the brain areas that get activated during the scans.

In neuroscience, another approach is to study the brain while it’s at rest—that is, when a person is just lying still in the scanner. This “resting-state” brain activity reflects how the brain is naturally organized and can reveal long-term effects of experience. In this study, researchers analyzed resting brain scans from 150 people, including 96 programmers, to see how programming experience might shape brain networks.

They found that programmers showed stronger connections between brain regions related to language, math, and attention over time. In contrast, non-programmers showed more connections in areas linked to social and emotional thinking. The study also found that with more years of programming experience, there was less connection between two specific brain areas involved in reading visuals and speaking.

These findings suggest that the brain may reorganize itself with programming experience, particularly in ways that support logic, language, and focus.

Fig. 1:��

Group-level functional connectivity measures for (a) programmers and (b) non-programmers. Values in these matrices represent the functional connectivity (i.e., Pearson correlation) from every cortical brain region to every other cortical brain region. Correlation coefficients in these figures were Fisher’s z-transformed to normalize their distribution. These values now denote scaled correlation values following a normal distribution between -1 and 1.