The research, led by Elijah Agoalikum of Sichuan Provincial People’s Hospital and the University of Electronic Science and Technology of China, together with colleagues including senior author Michael Maes, used resting-state functional magnetic resonance imaging to compare people with schizophrenia, people with ADHD, and healthy controls. Rather than computing one static connectivity map per participant, the team employed a technique known as dynamic functional network connectivity, or dFNC. The method slices continuous scanning data into overlapping temporal windows and estimates how strongly a set of intrinsic brain networks are synchronized with one another within each window. A clustering algorithm then sorts these moment-to-moment connectivity patterns into a small number of recurring configurations, or states, that the brain revisits again and again across the scan.

In this study, the sliding-window analysis paired with k-means clustering revealed four distinct, recurring connectivity states. Each state represents a characteristic pattern of coordination among major intrinsic networks, including the default-mode network, which is active during introspection and self-referential thought; the fronto-parietal network, which supports cognitive control and working memory; the sensorimotor, visual, and auditory networks, which handle perception and action; the subcortical network, encompassing deep structures such as the basal ganglia and thalamus; and the cerebellar network, long associated with motor coordination but increasingly implicated in cognition and emotion. Once the four states were defined, the researchers quantified how each group used them, measuring the mean dwell time spent in each state, the fraction of total scan time occupied by each state, and the frequency of transitions between states.

The results divided the two disorders along a clear axis of network dynamics. Participants with schizophrenia spent significantly more time, both in average dwell duration and in overall fraction of time, in states characterized by reduced integration between networks and greater segregation, compared with both the ADHD group and healthy controls. In other words, the schizophrenic brain appeared to get stuck in configurations in which major systems operate in relative isolation from one another. This finding resonates with a long-standing hypothesis in schizophrenia research that the illness involves a weakening of long-range connectivity, particularly between the prefrontal cortex and distributed association networks, which may underlie difficulties in coordinating thought, emotion, and behavior.

ADHD presented a different picture. Rather than showing the pronounced entrenchment in segregated states seen in schizophrenia, participants with ADHD spent more time in states reflecting moderate connectivity patterns. The authors interpret this as evidence of altered but more flexible network dynamics relative to schizophrenia. The ADHD brain, in this framing, is not locked into any single maladaptive configuration; instead, its pattern of state occupancy is shifted toward intermediate regimes, suggesting a subtler disruption in how the brain balances stability and flexibility. This distinction is clinically meaningful, because cognitive models of ADHD have long emphasized variability and instability of performance over time, and the dFNC findings provide a neural correlate of that temporal unevenness.

Beyond state occupancy, the study uncovered disorder-specific differences in the strength of connectivity within the states themselves. Schizophrenia showed increased connectivity between cerebellar, cognitive control, and sensory networks compared with ADHD. This may seem paradoxical alongside the finding of reduced integration, but it illustrates an important principle of network neuroscience: connectivity alterations are not uniformly stronger or weaker across the brain. Instead, particular pairs of networks can become hyperconnected while others drift apart, producing a reorganized architecture rather than a simple global deficit. The cerebellum, once viewed as a purely motor structure, has emerged in recent psychiatric imaging as a hub with reciprocal connections to prefrontal and association cortices, and its altered coupling with control and sensory systems in schizophrenia may reflect the wide-ranging cognitive and perceptual disturbances characteristic of the illness.

Perhaps the most striking results are those that link temporal dynamics to lived experience. The researchers correlated the dFNC metrics with clinical symptom scores and found that in schizophrenia, greater mean dwell time and fraction of time in one particular state, designated state 2, were associated with physical anhedonia, the diminished capacity to experience pleasure from tactile, sensory, and bodily sources. Meanwhile, hyperactivity symptoms in the schizophrenia group were associated with the fraction of time spent in state 1. These correlations suggest that the amount of time the brain spends in specific connectivity configurations is not an abstract mathematical curiosity but maps onto concrete symptom dimensions. A patient who lingers longer in a segregated network state may, in effect, be carrying the neural signature of a diminished capacity for sensory pleasure.

This symptom-level mapping carries implications for how psychiatric illness is conceptualized and measured. The authors situate their findings within frameworks, such as the Research Domain Criteria initiative, that view psychopathology as arising from graded disruptions in interacting neural systems rather than from discrete diagnostic categories. In that view, schizophrenia and ADHD are not sealed boxes but overlapping regions in a multidimensional space of brain dynamics, and the same temporal metric, such as dwell time in a segregated state, may index different symptom dimensions in different populations. The study supports this transdiagnostic perspective by showing both shared instability in network dynamics across the two disorders and clearly disorder-specific patterns of state occupancy and connectivity.

The technical pipeline behind these conclusions is worth appreciating. After standard preprocessing of the resting-state fMRI data, the team used group independent component analysis to decompose each participant’s brain activity into spatially independent component networks, separating sources such as the default-mode, fronto-parietal, subcortical, sensorimotor, visual, auditory, and cerebellar networks while suppressing noise from head motion, respiration, and cardiac pulsation. Motion was carefully controlled, with framewise displacement used to account for the fact that participant movement, which itself can differ between clinical groups, can artificially inflate connectivity estimates. Sliding windows were then applied to the component time courses, k-means clustering grouped the windowed connectivity matrices into the four states, and statistical comparisons of dwell time, fraction of time, and transition probabilities between groups were corrected for multiple comparisons using false discovery rate procedures, a standard safeguard against spurious findings when many tests are performed simultaneously.

The authors propose that these dynamic connectivity features could serve as potential biomarkers for symptom dimensions in schizophrenia and ADHD. A biomarker of this kind would not diagnose a disorder in isolation, but it could help clinicians and researchers track illness mechanisms, stratify patients by underlying neural dynamics rather than surface symptoms, and monitor whether treatments are shifting the brain toward healthier patterns of state occupancy. The study also carries a broader message about the moving brain. Static averages, the findings imply, can obscure the very features that distinguish one condition from another: not where the brain goes, but how long it stays and how readily it leaves. As dynamic functional network connectivity matures and larger datasets accumulate, the fleeting choreography of neural networks may become one of psychiatry’s most informative windows into mental illness, revealing that what separates conditions such as schizophrenia and ADHD is not only which networks fail, but how the brain’s moment-to-moment dance among them goes awry.

Subject of Research: Dynamic functional network connectivity alterations in schizophrenia and ADHD measured with resting-state fMRI

Article Title: Network state transitions and connectivity alterations in attention deficit/hyperactivity disorder and schizophrenia: a dynamic fMRI study

Article References: Agoalikum, E., Klugah-Brown, B., Wu, H., Pacheco-Luna, L., Carvalho, A. F., Stoyanov, D., & Maes, M. (2026). Network state transitions and connectivity alterations in attention deficit/hyperactivity disorder and schizophrenia: a dynamic fMRI study. BMC Psychiatry. https://doi.org/10.1186/s12888-026-08577-x

Image Credits: AI Generated

DOI: 10.1186/s12888-026-08577-x

Keywords: dynamic functional network connectivity, schizophrenia, ADHD, resting-state fMRI, brain networks, default-mode network, cerebellum, anhedonia, hyperactivity, state transitions, psychiatry, biomarkers