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.

The brain undergoes major structural and functional changes from childhood through adolescence. Research suggests that neurodevelopment happens in a hierarchical way, meaning different brain regions and networks mature at different rates. However, less is known about how the brain’s intrinsic spatiotemporal propagations—patterns showing how activity spreads across the brain over time—develop during this period. This study examined how these activity patterns change from childhood to early adulthood.

Using a recently developed method that measures time-lagged dynamic propagations, the researchers analyzed how brain activity travels along three major axes of brain organization: the sensory–association (S-A) axis, which connects basic sensory regions to higher-order thinking areas; the task-positive to default network (TP-D) axis, which reflects shifts between attention-focused networks and the default mode network active during rest and internal thought; and the somatomotor–visual (SM-V) axis, which links movement and visual processing regions. The results showed that these propagation patterns gradually become more adult-like over development. As children mature, they spend more time in S-A and TP-D propagation states, while the occurrence of SM-V propagation states decreases.

Importantly, top-down propagations along the S-A axis—meaning activity flowing from higher-order cognitive regions to sensory regions—increased with age and were better predictors of cognitive performance than bottom-up propagations, which flow from sensory areas upward. These findings were replicated in two independent datasets, the Human Connectome Project Development cohort and the Nathan Kline Institute Rockland Sample, supporting the robustness and generalizability of the results. Overall, the study provides new insight into how large-scale functional brain dynamics develop during youth and how these changes support cognitive abilities.

Fig. 1: Spatiotemporal propagation patterns and their neurodevelopmental change from children to early adulthood.

A The first three propagation patterns derived from the reference cohort (HCP-A), represent group-level reference propagation patterns. Each row displays a full propagation cycle for the recurring spatiotemporal patterns: sensorimotor to association (S-A), task-positive to default mode networks (TP-D), and somatomotor to visual networks (SM-V). The patterns are depicted through their temporal phase cycle, ranging from 0 to 2π.��B Explained variance ratios of the first six propagation patterns from CPCA. The light blue line represents the youth cohort (HCP-D) and the dark line represents the reference adult cohort (HCP-A).��C Between-cohort similarity matrix showing the pairwise Pearson’s correlation of the propagation patterns across youth (HCP-D) and adult (HCP-A) propagation patterns. We also confirmed cross-cohort similarity using HCP Young Adult cohort (N = 892, age 21-35, Figure.��)].��D Reliability of propagation patterns, assessed by the discriminability for HCP-D and HCP-A cohorts.��E Age-related similarity of propagation patterns to adult reference. Dots represent the spatial correlations of the propagation pattern between individuals in the youth cohort and the group-level adult reference. The regression line illustrates the developmental trend across age. Age effect was assessed using a Spearman correlation, with p values adjusted for multiple comparisons using the false-discovery-rate (FDR) correction. Significant age-related increases were observed for the S-A (pFDR <0.001), TP-D (pFDR <0.001) and SM-V (pFDR = 0.002) propagation patterns. Statistical significance is denoted by asterisks (*: pFDR <0.05).��F Age prediction using the first three dynamic patterns. A combination of the first three dominant propagation patterns in the PLSR model predicts age with a Spearman’s correlation ρ of 0.80 and a mean absolute error (MAE) of 1.87 years.

This review looks at recent research showing that blood oxygenation level–dependent (BOLD) signals in white matter (WM) contain meaningful information about brain activity. These signals are influenced by the structure of white matter, its blood supply, and its metabolism, and they are closely connected to functional MRI (fMRI) signals in gray matter (GM). BOLD signals in WM can be detected both during tasks and at rest, where their natural fluctuations reveal coordinated activity between white and gray matter. Even so, many fMRI studies have traditionally ignored WM signals or treated them as noise.

New evidence shows that WM BOLD signals reflect how different brain regions communicate. Studies have found that the strength and behavior of these signals depend on features such as myelination, neurite density, mitochondrial content, and blood vessels within white matter tracts. Different types of fibers, such as association and projection fibers, show different BOLD patterns, and some heavily myelinated fibers may show little or no detectable signal. Research has also clarified how WM BOLD signals relate to GM networks, including during resting-state activity. Together, these findings suggest that WM BOLD signals provide valuable insight into brain function and should be included in fMRI analyses to better understand how the brain is organized and operates.

Fig. 1.��Population maps of HRF features show qualitative differences between GM and WM. Shown are the MNI T1, WM and GM masks for anatomical reference. Population-averaged features of the HRF are shown for FWHM, Height, PSC, time to Peak, Time to Dip, and Dip Height.

Risk-taking behavior is a feature of many neuropsychiatric disorders, yet effective treatments are limited because we still don’t fully understand what is happening in the brain when people make risky choices. Scientists know that certain “reward circuitry” regions—such as the amygdala, orbitofrontal cortex, insula, and anterior cingulate—are involved, but the specific electrical activity that predicts risk-taking in these areas has not been well studied in humans. Identifying local field potential (LFP) frequency patterns linked to risk-taking could help guide future therapies.

In this study, eleven patients with hard-to-treat epilepsy, who already had stereotactic EEG electrodes implanted for medical reasons, took part in an experiment measuring brain activity in these reward-related regions. Each person played a simple gambling game in which they guessed whether a hidden playing card would be lower or higher than a visible one, choosing to bet either $5 or $20. While they made these decisions, researchers recorded their local field potentials—electrical signals generated by groups of neurons. The team used statistical models to look for specific changes in oscillatory power (brainwave activity across different frequencies) related to reward prediction error, which is the difference between expected and actual outcomes. They also calculated a “risk-taking value” for each trial based on the card number and the size of the bet, and identified which oscillatory patterns were linked to riskier choices.

The results showed clear time-frequency patterns associated with reward prediction error signals in both the amygdala and the orbitofrontal cortex, with several strong clusters of activity in each region. Risky choices themselves were predicted by increased oscillatory power in the theta-to-beta frequency range in the orbitofrontal cortex during card presentation, and by increased high-beta power in the insula. Further analysis pinpointed these signals to the lateral orbitofrontal cortex and the posterior insula. Activity in one insula cluster linked to risky decisions was also connected to a theta-alpha reward prediction error signal in the orbitofrontal cortex. Additionally, an amygdala reward prediction error signal was associated with how often participants chose the higher bet, and a lateral orbitofrontal cortex signal predicted high bets specifically in risky situations.

Overall, the study identifies distinct electrical activity patterns in key reward-related brain regions that predict when a person is about to make a risky decision. These oscillatory signatures could eventually serve as biomarkers—measurable indicators that help guide new treatments, including closed-loop neuromodulation, for disorders in which risk-taking becomes harmful.

Figure 1

Gambling task and behavioural data. (A) Behavioural task events and timing between events in seconds (mean ± standard deviation). (B) Mean response time (time from bet cue presentation to patient response) for each patient card number. Error bars represent standard error. (C) Mean per cent of trials on which subjects bet high ($20) for each patient card number. Asterisk indicates P < 0.05. Error bars represent standard error.

In humans and other primates, the adolescent years are a time when thinking and problem-solving abilities improve, and the brain continues to grow and reorganize. However, scientists still don’t fully understand how these structural brain changes influence the actual neural activity that supports cognitive performance. In this study, researchers followed monkeys throughout adolescence and measured their behavior, their neurons’ activity, and their brain structure over time to better understand this process.

The team focused on the prefrontal cortex, a brain region important for working memory—the ability to hold and use information for short periods. They found that changes in prefrontal neural activity closely matched the animals’ improvements in working memory skills. More complex patterns of neural activity evolved gradually through the teenage years, but even simple features—like the average firing rate of neurons and how much that activity varied—helped predict how well the animals performed.

The researchers also examined how changes in the brain’s wiring related to these improvements. They discovered that the development of long-distance white matter tracts—pathways that connect the frontal lobe to other brain regions—strongly predicted both the progression of neural activity and gains in cognitive performance. Surprisingly, changes in brain volume and cortical thickness, which are known to shift during human adolescence, did not predict these neural or behavioral changes in monkeys.

Overall, the study shows that the maturation of white matter connections plays a key role in shaping how neural activity develops during adolescence, helping to support the rise in cognitive abilities during this critical period.

Fig. 1: Saccade precision and latency improve during adolescence.

a, Sequence of events in the ODR task. The monkey is required to maintain fixation while a cue stimulus is presented and after a delay period, when the fixation point turns off, saccade to the remembered location of the cue.��b, Sequence of events in the ODR with distractor task. After the delay period, a distractor stimulus appears, which needs to be ignored. The monkey is still required to saccade to the remembered location of the cue.��c, Possible locations of the stimulus presentation on the screen.��d, Schematic illustration of variability of two groups of saccades. The gray dots represent the endpoints of individual saccades for two stimulus locations. DI, defined as the area within one s.d. from the average landing position of each target is shown.��e, DI in the ODR task, during the neural recording sessions. Each dot is one session; data from different monkeys are shown in different colors. The blue line shows the GAMM-fitted trajectory. The gray shaded regions denote the 95% confidence intervals (CIs). The dashed vertical line denotes a mid-adolescence age of 0. The horizontal dashed bar denotes significant developmental effect intervals. The horizontal solid bar denotes intervals with significant monotonic developmental effect.��f, As in e, for the RT of saccade in the ODR task.��g, As in e, for the DI in the ODR with distractor task.��h, As in f, for the RT in the ODR with distractor task.��i, Schematic diagram of the three cohorts of monkeys (groups A–C) used to evaluate behavioral improvement. The image of the monkeys was created with .��j, DI in the ODR task of groups A and B at the TP1 and TP2 time points. The violin plot shows the distribution of DI values for both groups at two distinct time points, with the width of the plot indicating the density of the data points. Statistical comparisons were performed with a two-sided, two-sample t-test (no adjustment for multiple comparisons). TP1: P = 0.21; TP2: P = 2.83 ×�ĉ10⁻⁴.��k, DI in the ODR task of groups A and C at the first time point. Two-sided, two-sample t-test (no adjustment for multiple comparisons): P = 9.35 ×�ĉ10⁻⁵. ***P &����; 0.0001.

Sex differences in brain volume are well known across ages, but it is less clear how these differences relate to early cognitive abilities, especially in children exposed to alcohol before birth (prenatal alcohol exposure, PAE), which is a leading cause of developmental delays in North America. In this study, we examined how executive function—skills such as planning, attention, and self-control, measured by the BRIEF/BRIEF-P Global Executive Composite (GEC) and the Statue subtest of the NEPSY-II—relates to the volume of 36 brain regions in 169 children aged 2–8 years (37 with PAE; 534 total MRI scans). We found significant interactions between sex, alcohol exposure, and executive function in many regions: 22 regions for the GEC and 6 regions for the Statue. Unexposed boys generally showed negative associations between brain volume and executive function, while boys with PAE showed the opposite pattern. Unexposed girls showed strong positive associations, whereas girls with PAE showed weaker positive associations. In regions without these three-way interactions, we still observed sex differences: boys tended to show negative volume-executive function relationships and girls positive relationships, regardless of alcohol exposure. Overall, the findings suggest that boys with PAE and unexposed girls show more mature patterns of brain volume related to executive function compared to girls with PAE and unexposed boys. This study emphasizes the importance of considering sex when studying brain structure and cognitive development, especially in children affected by prenatal alcohol exposure.

Fig. 1.��Box plot showing the distribution of scores for the Statue and the GEC. We found no significant differences in Statue scores between exposure groups. For the GEC, there was a significant difference (indicated by **) between the unexposed and PAE groups. Within exposure groups there were no sex differences in performance on executive function measures.

Math skills depend on a mix of math-specific abilities and more general thinking skills, known as executive functions (EF), which help with things like memory, focus, and self-control. Brain imaging studies have shown that a part of the brain called the intraparietal sulcus is consistently active during arithmetic tasks. However, activity in the frontal brain regions, which are linked to executive functions, has been less consistent across studies. These differences may be due to individual differences or the specific demands of math tasks.

This study looked at brain activity in adolescents while they worked on math tasks involving ratios and compared it with their measured math skills and executive functions. The researchers found that the brain responded differently depending on whether the numbers were shown as symbols (digits) or as nonsymbolic quantities (dot arrays).

For symbolic number tasks, greater activity in the left parietal and frontal brain regions was linked to higher calculation scores. For nonsymbolic tasks, greater activity in both sides of the parietal lobes was linked to better math fluency. Executive functions were also important: inhibitory control was linked to brain activity during nonsymbolic tasks, while working memory was linked to activity during symbolic tasks across several brain regions.

Overall, the findings suggest that the way numbers are presented (symbols versus quantities) changes how the brain processes them, and these patterns also depend on individual differences in math ability and executive functions.

Fig. 1.��In-scanner behavioral performance (p).

Understanding how the human brain changes throughout life—from infancy to old age—is essential for learning about childhood development, aging, and brain disorders. In this study, we aimed to provide detailed information about the brain’s white matter pathways by examining their tiny structures (microstructure), larger organization (macrostructure), and the shape of the brain’s outer layer (cortex) connected to these pathways. We analyzed four large, high-quality datasets that included 2,789 brain scans from people aged 0 to 100 years, using advanced imaging techniques. We found that different features of white matter pathways develop and decline at different times and rates, depending on the brain area and pathway type. We also discovered connections between various features that could help explain biological changes at different life stages. Additionally, the patterns of change with age were unique for each feature, and the way white matter changes during development is strongly linked to how it changes during aging. Overall, this study provides important baseline data about white matter pathways in the human brain, which can help future research on normal brain development as well as brain diseases.

Fig 1

Microstructural, macrostructural, and cortical features associated with each of 63 white matter bundles.

Near-infrared spectroscopy (NIRS) is a popular tool for monitoring brain activity by measuring how oxygen flows through the brain. Some advanced techniques, like the cerebral oxygenation index and Fast Fourier Transform (FFT)-based methods, are used to understand how the brain adjusts to changes in blood pressure (a process called cerebral autoregulation). However, these methods work best when the data being measured is stable over time and don’t respond quickly to sudden changes.

In this study, researchers explored a different method called wavelet transfer function analysis. Unlike the older methods, wavelet analysis can handle quickly changing data and still provide reliable results. The researchers improved an existing wavelet software tool to better measure brain responses, specifically focusing on how well the brain maintains steady blood flow under changing conditions.

They tested this improved tool using both simulated data and real data from five healthy men who experienced large changes in blood pressure and oxygen levels through a procedure that altered pressure in their lower bodies. The team compared results from their wavelet method to those from the traditional FFT method.

They found strong agreement between the two methods, especially for detecting changes in lower frequency brain activity, which is important for understanding how the brain regulates its blood flow. In some frequency ranges, the wavelet method even performed better than FFT, especially when the brain’s response changed quickly.

In conclusion, the wavelet method the researchers developed showed good accuracy and could be a powerful way to study how the brain regulates blood flow, especially in real-world situations where brain activity and blood pressure change over time.

Figure 1. Experimental setup to study response of cerebral blood flow, brain tissue oxygenation, cardiovascular parameters to lower body positive pressure (Protocol A) or negative pressure and hypoxia (Protocol B). ECG electrocardiogram, SpO₂ peripheral oxygen saturation, TCD transcranial Doppler, MCAV middle cerebral artery velocity, NIRS near infrared spectroscopy, cTOI tissue oxygenation index by NIRS, BP blood pressure, SV stroke volume, CO cardiac output, LBP lower body pressure, LBNP lower body negative pressure, LBPP lower body positive pressure.