The research, led by Harrison Watters of the Czech Academy of Sciences and Weber State University, together with colleagues at Universiti Putra Malaysia and Georgia Tech, applied two sophisticated dynamic functional MRI analyses to the resting brains of people with high versus low scores on the Smartphone Addiction Scale, Malay version (SAS-M). Unlike traditional static functional connectivity, which averages relationships between brain regions across an entire scan, these methods capture how network relationships fluctuate, recur, and reorganize from second to second. The distinction matters because attention is inherently temporal, and phase information, the timing offset between activations of different networks, may reveal more about cognitive state than average activity ever can.

The first technique, functional connectivity-based attractor networks (fcANN), treats the brain as a Hopfield network whose activity settles into stable basins, or attractors, defined in a pretrained energy landscape learned from a large independent dataset. Each moment of a participant’s 13-minute resting-state scan was assigned to one of four canonical attractor states organized along two interpretable axes: internal versus external context, and action versus perception. The second technique, complex principal component analysis (cPCA), identifies quasi-periodic patterns (QPPs), infraslow swells of network activity lasting roughly 20 seconds, in which the default mode network (DMN) and the dorsal attention network (DAN) repeatedly fall into antiphase cycles. The DMN is associated with mind wandering and internal thought, while the DAN mediates goal-directed external attention, and the frontoparietal network (FPN) supplies executive control over both.

The dataset comprised 45 participants, five of whom were excluded for excessive head motion or registration failure, leaving 18 individuals with High SAS-M scores and 22 with Low SAS-M scores. All were right-handed, neurologically healthy, and screened with a previously validated SAS-M threshold above 98, a cutoff for elevated risk of problematic smartphone use. Critically, the authors note, the SAS-M is a self-report screening instrument rather than a clinical diagnostic, so the groups are best described as high and low scorers rather than addicted versus non-addicted individuals.

When the researchers examined attractor-state occupancy, the two groups looked remarkably similar. Neither the fraction of time spent in each state, nor dwell times, entropy, or switching rates differed significantly, although the high-scoring group showed a non-significant tendency to spend less time in externally oriented states (22.5 percent versus 27.6 percent) and to linger briefly longer in internal contexts. In other words, people with high smartphone addiction scores did not obviously inhabit a different set of brain states; they navigated the same landscape, but something about the fine texture of the activity within those states appeared different.

That something emerged most clearly from the QPP analyses, particularly around moments when the brain transitioned between internal and external attractor states. Immediately before and after these transitions, high-scoring participants showed significantly greater separation between DMN and DAN activity, an effect that survived false discovery rate correction at lags of one image volume (three seconds) on either side of the transition. Cluster-based permutation tests confirmed extended DMN-DAN differences spanning several seconds around the shift, as well as a DMN-FPN difference stretching from one second before the transition to five seconds after it. The pattern amounts to a form of default mode dominance: during periods of cognitive-state reorganization, the internally oriented network overwhelms the attention and control systems that should be reallocating resources.

The authors interpret this through the lens of attentional control theory, which holds that efficient shifting between internal and external focus depends on the limited resources of the DAN and FPN. Constant switching between salient smartphone stimuli, notifications, feeds, and apps, could plausibly place sustained demands on exactly these executive systems. The increased DMN dominance observed in high scorers may therefore reflect reduced efficiency in reallocating attention during shifts in cognitive context, though the researchers are careful to stress that this interpretation is speculative and cannot be directly inferred from resting-state data alone.

Corroborating the categorical results, continuous analyses of individual SAS-M scores found positive associations between smartphone addiction scores and DMN-FPN separation around external-to-internal transitions, with several Spearman correlations surviving multiple-comparison correction. Together with prior work showing that DMN-FPN decoupling during tasks tracks cognitive demand, the findings hint that heavy smartphone users may carry a task-like strain into their resting brains, their network dynamics resembling those of people under elevated cognitive load.

The study’s authors are candid about its limits. The sample of 40 is modest, several moderate effects did not survive correction, and the subject-level QPP estimation relied on relatively short scans, which can destabilize spatiotemporal estimates. The QPP component selection procedure, though applied identically and blind to group membership, partly built DMN-DAN opposition into the measure being tested. Self-report questionnaires are an imperfect proxy for actual usage, and the cross-sectional design rules out any claim about causality: it remains possible that these dynamics predispose people to problematic use rather than resulting from it, or that they represent the brain’s adaptive plasticity rather than damage, much as videogamers show altered but improved attentional performance.

Still, the paper makes a broader case that extends well beyond smartphone research. Because objective measures of smartphone use are rarely collected in neuroimaging datasets, heavy digital engagement may be quietly injecting heterogeneity into brain-imaging studies of all kinds. The authors propose that large consortia such as the Human Connectome Project and UK Biobank begin logging screen time and social media behavior alongside age, sleep, and psychiatric history, and they suggest that future studies combine longitudinal designs, objective usage metrics, and task paradigms to determine whether the transition-linked dynamics observed here are stable traits or reversible adaptations. As the authors conclude, understanding how digital environments reshape intrinsic brain dynamics will matter increasingly for both basic neuroscience and mental health, and the present findings, while preliminary, offer one of the first dynamic glimpses of what habitual smartphone engagement may be doing to the brain’s switching machinery.

Subject of Research: Dynamic resting-state fMRI of attention network coordination in high smartphone addiction scores

Article Title: Altered attention network dynamics in resting-state fMRI of individuals with high smartphone addiction scores

Article References: Watters, H., Rashid, A. A., Suppiah, S., LaGrow, T. J., Hlinka, J., & Keilholz, S. (2026). Altered attention network dynamics in resting-state fMRI of individuals with high smartphone addiction scores. Frontiers in Neuroscience, 20, Article 1948718. https://doi.org/10.3389/fnins.2026.1948718

Image Credits: AI Generated

DOI: 10.3389/fnins.2026.1948718

Keywords: smartphone addiction, resting-state fMRI, default mode network, dorsal attention network, frontoparietal network, quasi-periodic patterns, attractor states, dynamic functional connectivity, attentional control, neuroimaging, SAS-M, brain dynamics