Post-stroke sleep disturbance is common, with pooled prevalence estimates of roughly 50% to 53% in recent meta-analyses, while post-stroke cognitive impairment varies according to case definitions, assessment methods, population and time since stroke. Greater sleep fragmentation and lower sleep efficiency have been associated with poorer post-stroke cognitive performance. Executive dysfunction is especially consequential because problems with working memory, cognitive flexibility and conflict monitoring can slow functional gains, limit rehabilitation participation and hinder reintegration into community life. These observations nevertheless leave three possibilities open: disturbed sleep may contribute to cognitive impairment, it may emerge as a parallel consequence of brain injury, or both processes may be influenced by inflammation, circadian disruption, mood symptoms, medication and reduced daytime activity.

The clinical problem is also more complex than a single complaint about “poor sleep.” Insomnia, repeated awakening, obstructive sleep apnea, daytime hypersomnolence and circadian misalignment have different biological drivers. Obstructive sleep apnea, for example, involves recurrent arousals and intermittent hypoxia and has been associated with inflammation, oxidative stress and vascular dysfunction. Insomnia and fragmented sleep are more closely tied to hyperarousal and unstable sleep continuity. Likewise, executive dysfunction covers several cognitive domains that cannot be represented accurately by a single screening score. A targeted synthesis is therefore more informative than treating sleep disturbance and cognition as interchangeable symptoms, although the available literature often groups them this way and depends heavily on subjective questionnaires and global cognitive measures.

Stroke is increasingly understood not simply as focal tissue loss but as an event that damages communication across distributed systems. Focal lesions can coexist with diaschisis, disruption of long-range white-matter pathways and altered coordination among large-scale networks. The triple-network model focuses on interactions among the salience network, which is thought to help detect behaviorally relevant signals and switch between internally and externally directed states; the default mode network, which supports internally oriented cognition; and the frontoparietal network, which contributes to working memory, task-set maintenance and top-down control. Efficient executive behavior depends on the ability to disengage default-mode processing and recruit frontoparietal control when external demands rise. Stroke-related injury may therefore create a structural vulnerability that sleep disturbance could amplify at a functional level.

Post-stroke neuroimaging supports the presence of that vulnerable network architecture. One study of 118 patients with internal capsule stroke and 57 healthy controls found increased static coupling between the salience and default mode networks but reduced dynamic interactions between the executive control network and other systems. Patients with right-sided lesions also showed lower modularity and longer persistence of a state with strongly positive connections across all three networks, demonstrating that stronger connectivity can coexist with poorer differentiation. In another study, 17 patients with post-stroke cognitive impairment and 24 controls showed reduced static connectivity between the ventral default mode and salience networks. Patients spent longer in one recurring connectivity state, and longer dwell time correlated with lower Mini-Mental State Examination scores, although the association involved global rather than domain-specific cognition.

Directional analyses provide a further complication. Using spectral dynamic causal modelling, researchers comparing 41 patients with post-stroke cognitive impairment and 41 controls found increased effective influence from the posterior cingulate cortex to bilateral dorsolateral prefrontal regions and from the right insula to the right dorsolateral prefrontal cortex. Greater effective connectivity was associated with poorer cognitive scores. Lesion evidence also points to the salience network: in 144 patients with acute stroke, left insular lesions were associated with poorer set-switching even after accounting for the visual-spatial and motor demands of the task. Together, these studies show that stronger or more persistent communication is not automatically beneficial. They do not, however, reveal whether sleep disturbance causes, follows or amplifies the observed network abnormalities.

Sleep research from populations without stroke supplies biological plausibility but not direct proof. In 26 healthy adults scanned after normal sleep and approximately 24 hours without sleep, total sleep deprivation reduced connectivity within the default mode network and weakened its anticorrelation with task-positive regions during rest and a visual attention task. A separate study of 22 healthy participants found that 36 hours of sleep deprivation coincided with poorer working-memory performance, increased coupling between dorsal attention and default mode regions, and decreased coupling between frontoparietal and default mode regions. Insomnia findings point to reduced flexibility rather than necessarily altered average connectivity: 65 people with insomnia and 65 matched controls showed less temporal variation in connectivity between the anterior salience and left executive control networks. The authors note that experimental sleep loss is not equivalent to chronic post-stroke insomnia, apnea or fragmentation.

Stroke-specific evidence remains limited but includes a small resting-state imaging study of patients with post-stroke insomnia. Among 23 retained participants, imaging showed abnormalities in cortical regions beyond the infarcts, including elevated low-frequency activity in parts of the default mode network, but the measures did not test network switching or whether default-mode activity failed to suppress during executive tasks. In a separate actigraphy study of 35 older adults with chronic stroke and 35 matched adults without stroke, the stroke group had greater objectively measured fragmentation despite similar subjective questionnaire scores, and cognitive differences were larger where fragmentation was greater or sleep efficiency lower. A polysomnography study of 86 patients evaluated within 14 days of minor ischemic stroke found that obstructive sleep apnea was more prevalent among those with cognitive impairment, while shorter sleep time and lower minimum oxygen saturation remained associated with impairment. These observational findings cannot determine causal direction or identify the relevant network process.

To test the proposed pathway, the review recommends studies that combine objective sleep phenotyping with dynamic or effective connectivity, task-based functional MRI, domain-specific executive tests and longitudinal or interventional follow-up in the same participants. A practical initial population would be medically stable adults with imaging-confirmed ischemic stroke assessed within a defined recovery period, although that window is a design proposal rather than an established requirement. Insomnia, fragmentation, obstructive sleep apnea and their coexistence should be measured separately. Lesion location, structural disconnection, stroke stage, vascular burden, medication, mood, pain, fatigue, rehabilitation exposure and premorbid cognition may act as confounders or modifiers, depending on the causal model. Sample size should be determined through simulation because detecting an indirect effect through network function may require more participants than detecting a direct sleep-cognition association.

The framework makes specific predictions that can be contradicted. Greater fragmentation or insomnia severity would be expected to accompany longer dwell time in default-mode-dominant states, fewer transitions toward task-relevant control states and weaker salience-mediated switching. Insufficient suppression of internal activity and delayed frontoparietal recruitment could then impair conflict monitoring, working memory, processing speed and cognitive flexibility. Lesions or disconnection affecting fronto-insular, cingulate or frontoparietal pathways might strengthen these associations. A finding linking objectively measured sleep disruption to executive impairment but not to the prespecified network measure would challenge the proposed bridge. Likewise, an intervention that improves sleep and executive function without producing the predicted network change would weaken the mediation claim, although it would not make the treatment clinically useless. Because evidence is dominated by heterogeneous, largely observational studies and mechanistic inferences from non-stroke populations, the proposed pathway remains a roadmap for direct testing rather than an established basis for treatment selection.

Subject of Research: A mechanistic hypothesis linking post-stroke sleep phenotypes, large-scale brain-network dysfunction and executive impairment.

Article Title: Poststroke sleep phenotypes and executive dysfunction: toward a sleep-network-executive framework

Article References: Xu, J., Zhang, Y., Lin, D., Yan, X., Xie, N., Si, S., & Yang, S. (2026). Poststroke sleep phenotypes and executive dysfunction: toward a sleep-network-executive framework. Frontiers in Neuroscience, 20, Article 1978796. https://doi.org/10.3389/fnins.2026.1978796

Image Credits: AI Generated

DOI: 10.3389/fnins.2026.1978796

Keywords: post-stroke sleep disturbance, executive dysfunction, salience network, default mode network, frontoparietal network, functional connectivity, stroke rehabilitation, sleep fragmentation, insomnia, brain imaging, Poststroke, sleep

The study used resting-state magnetoencephalography, or MEG, to measure the timing of neuronal activity with greater temporal resolution than blood-oxygen-level-dependent functional magnetic resonance imaging. Participants’ MEG signals were projected onto 400 brain parcels defined by a publicly available atlas, and the researchers calculated parcel-to-parcel connectivity within three canonical networks: the default mode network, executive control network and salience network. This approach allowed the analysis to move beyond a single score for an entire network and test whether particular regional connections behaved differently during acute risk. The researchers described the work as a way to examine both relatively stable vulnerability and state-dependent changes associated with a recent crisis.

Participants came from the National Institute of Mental Health’s Neurobiology of Suicide protocol and were divided into four clinically defined groups. The high-risk group included eight people who had attempted suicide or experienced suicidal ideation with intent to act within the previous two weeks. Twenty-six participants formed a lower-risk group with a history of suicide attempts but no attempt or ideation during the past year. A clinical-control group of 22 had anxiety or mood disorders but no history of suicide attempts or ideation, while 21 minimal-risk participants had no personal or family history of psychiatric disorders or suicidal behavior. The final sample included 47 women and 30 men, with a mean age of 38.41 years.

Groups did not differ significantly in age, sex or ethnicity, but they did differ in mood and anxiety symptoms, medication history and suicidal-ideation scores. The high-risk group had substantially higher current ideation scores than every comparison group and also had greater depression and anxiety severity. A multivariate analytical method called DIABLO—Data Integration Analysis for Biomarker discovery using Latent variable approaches for Omics studies—integrated 2,628 default mode connectivity features, 1,225 executive control features and 1,176 salience features. A discrimination design, selected because the network measures were highly correlated, significantly separated the groups at p = 0.018, whereas an alternative sparse partial least-squares design did not at p = 0.319.

Follow-up analyses indicated that the high-risk group generally had reduced within-network connectivity across all three systems. Reduced default mode connectivity involved the dorsolateral prefrontal cortex in pairs with the retrosplenial or medial prefrontal cortex and with a combined precuneus and posterior cingulate region. Lower executive control connectivity appeared among frontal and parietal areas, including the precuneus, while weaker salience connections joined medial or lateral frontal regions with sensory, insular and parietal areas. One exception was stronger connectivity between the lateral prefrontal cortex and parietal operculum than in the clinical-control group. These were group-level associations, and the analysis identified influential features rather than demonstrating that any single connection could independently predict an individual’s risk.

The relationship between recent ideation and connectivity was more specific than the overall group differences. Among participants in the high-risk group, stronger default mode connectivity between the precuneus or posterior cingulate cortex and the dorsolateral prefrontal cortex, and between the dorsolateral and medial prefrontal cortices, was associated with more severe current suicidal thoughts. The researchers reported interaction effects for these relationships, along with a negative interaction for another posterior default mode connection. In the salience network, stronger connections between the medial frontal and medial parietal cortices and between the parietal operculum and lateral prefrontal cortex were likewise associated with greater ideation severity in the recent-crisis group.

By contrast, executive control connectivity between the precuneus and posterior medial prefrontal cortex showed an inverse association with recent ideation in the high-risk group relative to lower-risk and minimal-risk participants. The investigators used Bayesian linear mixed models to accommodate non-normal data and the limited sample. Models were run with four Markov chain Monte Carlo chains and 5,000 iterations, with additional iterations used when convergence was inadequate. They reported convergence with R-hat below 1.1 and generally acceptable effective sample sizes. These models describe associations with ideation severity; they did not prospectively predict whether a participant would attempt suicide or validate the signatures in an independent cohort.

The results suggest that a recent suicidal crisis is associated with a reorganization of coordinated activity spanning cognitive control, internal thought and detection of salient stimuli. The default mode and salience relationships were generally positive with ideation severity, while the executive control relationship was negative, demonstrating that regions grouped within the same broad network can contribute in different directions. The authors suggested that stronger default mode coupling may reflect intensified self-referential and emotional processing, weaker precuneus–posterior medial prefrontal coupling may relate to impaired cognitive integration, and stronger salience coupling may reflect active emotion regulation or amplified processing of emotionally relevant information. These interpretations were drawn from prior literature rather than tested directly in the present experiment, which did not manipulate death-related cues or measure the cognitive processes proposed as explanations.

Sensitivity analyses complicated the most straightforward interpretation. When depression severity, anxiety severity and medication history were included as covariates, several executive control and salience associations were attenuated, whereas most selected default mode features remained comparatively robust. Because the high-risk group had more severe mood and anxiety symptoms and a more extensive medication history than the comparison groups, the neural patterns cannot yet be cleanly attributed to suicidal crisis alone. Participants were also medication-free or receiving clinically indicated prescriptions, and the sample included both major depressive disorder and bipolar disorder, introducing additional variability. The timing between clinical assessments and MEG collection varied modestly and was longer for some high-risk participants because of scheduling constraints.

Major limitations were the small high-risk sample, the absence of an external validation dataset and possible sensitivity to the chosen brain-parcellation scheme. The group categories also captured different temporal states—recent crisis, historical risk, clinical control and minimal risk—rather than tracking the same individuals through changing levels of risk. The results therefore support the hypothesis that acute suicidal states have electrophysiological signatures that differ from historical vulnerability, but they do not show that those signatures are unique to suicide, are stable over time or can be used in routine screening. Larger longitudinal studies, external replication and samples with closely matched symptom severity and medication histories will be needed to determine whether the network connections retain their value as objective biomarkers of acute risk.

Subject of Research: Magnetoencephalography assessment of brain-network connectivity in recent suicidal crisis and historical suicide risk

Article Title: Brain network-driven predictive neural markers for suicide risk

Article References: Lee, Y., Gilbert, J. R., Chin Fatt, C. R., Stout, J. D., Waldman, L. R., Lamontagne, S. J., Zarate, C. A., Jr., & Ballard, E. D. (2026). Brain network-driven predictive neural markers for suicide risk. Frontiers in Psychiatry, 17, Article 1950068. https://doi.org/10.3389/fpsyt.2026.1950068

Image Credits: AI Generated

DOI: 10.3389/fpsyt.2026.1950068

Keywords: suicide risk, magnetoencephalography, suicidal ideation, brain connectivity, default mode network, executive control network, salience network, DIABLO, electrophysiology, predictive biomarkers, Brain, network-driven

The research, led by Zhiqiang Wu, Jiacheng Lu, Hao You, and Siyan Hou with corresponding authors Junxia Zhang and Qin Ye, draws on connectomics, the field devoted to mapping the structural and functional topology of the human brain as a unified network. Rather than viewing the brain as a collection of isolated regions with fixed jobs, connectomics treats it as a set of interacting networks. The study focused on five of the most important of these: the salience network, which helps the brain decide which stimuli deserve attention; the central executive network, which supports planning and decision-making; the default mode network, active during introspection and memory retrieval; and the dorsal and ventral attention networks, which orient attention to the external world. All five have critical hubs in or near the temporal lobes, which is precisely where temporal gliomas grow.

To build these personalized network maps, the team used imaging data from each patient, applying techniques associated with the Human Connectome Project and diffusion tractography imaging, which traces the orientation of water molecules in brain tissue to infer the paths of white matter fibres. The approach allowed the researchers to reconstruct not only grey-matter functional structures but also the major fibre bundles that carry signals between them, including the inferior fronto-occipital fasciculus, the inferior longitudinal fasciculus, and the optic radiations. These tracts are the wiring of the brain’s long-distance communication, and damaging them can sever the connections that cognitive networks depend on even when the cortical areas themselves appear intact.

In the retrospective portion of the study, the researchers analysed 53 patients with temporal gliomas whose personalized functional networks had been reconstructed from imaging. The findings were striking. In 52 of the 53 patients, or 98.11 percent, tumour infiltration extended into brain networks and fibre tracts, a slightly higher rate than the 94.34 percent of patients whose tumours invaded discrete functional structures. In other words, nearly every tumour was not simply a local mass pressing on a language area or a memory hub; it was a lesion woven into the connective fabric of the brain. This network-level invasion, largely invisible to conventional surgical planning that focuses on landmarks and eloquent cortex, offers a plausible explanation for why cognitive decline persists after technically successful resections.

The second phase of the study moved from observation to intervention. The researchers compared 29 patients with temporal gliomas operated on with connectomics-optimized surgical programs, the experimental group, against patients treated with traditional surgical strategies, the control group. Before surgery, the two groups showed no significant differences in the characteristics of tumour invasion, meaning the comparison was not skewed by one group having more aggressive or more widespread disease. After the operations, the differences became clear. In the 16 postoperative patients in the experimental group, the protection rates for brain networks and for functional structures were noticeably higher than in the 13 patients who underwent traditional surgeries, with statistical significance reported at P = 0.000 for network protection and P = 0.029 for functional structure protection.

The left temporal lobe deserves special mention because it houses much of the language machinery of the brain in most people, making surgery there particularly delicate. When the analysis was restricted to the 18 patients with left temporal gliomas, the pattern held. The eight patients in the connectomics-guided experimental group had significantly greater protection of both brain networks and functional structures than the ten patients in the control group, with P values of 0.017 and 0.029 respectively. Both surgical outcomes and cognitive scores, assessed with tools such as the Montreal Cognitive Assessment, were significantly better in the connectomics group than in the traditional treatment group, with P values below 0.05 across these comparisons.

Technically, the connectomics-guided approach changes what the surgeon sees and plans around. Traditional neurosurgical planning emphasizes avoiding eloquent cortex, the regions where electrical stimulation or functional MRI shows critical functions such as movement, speech, or vision. The connectomic approach adds a second layer: it visualizes the network edges, the white matter pathways and their connections to distributed cognitive systems, and asks the surgeon to preserve the topology of those systems while still achieving maximal tumour resection. In practice, this means an incision trajectory and resection boundary designed not only around what a region does but around what it connects to. A tumour that infiltrates the inferior fronto-occipital fasciculus, for example, threatens the link between occipital visual processing and frontal language and executive areas, and cutting along the tumour margin without accounting for that tract could disconnect a network whose nodes are all perfectly preserved.

The clinical implication, the authors argue, is that preserving traditional functional structures is necessary but no longer sufficient. During temporal glioma surgery, they conclude, it is essential to also retain the brain networks that carry advanced cognitive functions. Their preliminary evidence indicates that connectomics-based visualization of functional networks improves neurosurgical comprehension, optimizes surgical programs, and holds the potential to minimize surgical trauma while enabling superior levels of tumour resection with preserved functional outcomes. For a disease where the balance between oncological control and quality of life is the central clinical tension, that framing reframes the goal of surgery: the target is no longer just the tumour volume but the integrity of the patient’s cognitive connectome.

Several caveats temper the enthusiasm. The comparative analysis involved relatively small numbers of patients, 29 in the overall comparison and 18 in the left temporal subgroup, and the study design was retrospective in its first phase, which limits the strength of causal conclusions. The published version was also shared early as a citable accepted manuscript subject to further edits. Larger, prospective, and ideally randomized comparisons will be needed before connectomics-guided planning becomes a standard of care. The study was conducted under the Declaration of Helsinki with ethics approval from the First Affiliated Hospital with Nanjing Medical University and informed consent from all participants, and the authors declared no competing interests, with funding from a Clinical Capacity Enhancement Project grant at Jiangsu Province Hospital.

Even with those limitations, the study lands at a moment when the tools of network neuroscience are maturing rapidly. Machine learning methods, which the authors list among their keywords, are increasingly able to segment networks, predict cognitive deficits from connectivity patterns, and integrate multimodal imaging into surgical navigation. The idea that a glioma should be understood as an attack on a network rather than an occupation of a region is philosophically simple but surgically transformative, and this work offers an early, quantified demonstration that acting on that idea changes outcomes. If larger trials confirm these results, the connectome may become as routine a preoperative map for neurosurgeons as the anatomical MRI is today, and the devastating trade-off between removing a tumour and losing one’s cognitive self may become considerably less stark.

Subject of Research: Connectomics-guided surgical planning to preserve cognitive brain networks in temporal glioma patients

Article Title: Connectomics-based functional networks in temporal gliomas

Article References: Wu, Z., Lu, J., You, H., Hou, S., Zhang, Z., Ye, M., Wang, X., Yuan, C., Zhang, J., & Ye, Q. (2026). Connectomics-based functional networks in temporal gliomas. BMC Medical Imaging. https://doi.org/10.1186/s12880-026-02715-3

Image Credits: AI Generated

DOI: 10.1186/s12880-026-02715-3

Keywords: connectomics, temporal glioma, brain networks, neurosurgery, cognitive function, default mode network, salience network, central executive network, diffusion tractography, white matter tracts, BMC Medical Imaging, brain mapping

The research, led by Dr. Noam Goldway under the supervision of Prof. Talma Hendler and Dr. Haggai Sharon of Tel Aviv University’s Institute for Psychedelic Research at the Sagol School of Neuroscience, was published in the journal Neuropsychopharmacology. For the first time in humans, the team demonstrated that ketamine activates two distinct mechanisms in the brain: one responsible for pain relief and another responsible for the dissociative state the drug is famous for. The distinction is not merely a matter of subjective experience — it is visible in the brain’s activity patterns.

The question the researchers set out to answer is one of the most consequential in modern psychedelic medicine. In recent years, mind-altering substances have migrated from the fringes of culture to the center of serious clinical investigation. MDMA is being studied as a treatment for post-traumatic stress disorder; psilocybin, the active compound in magic mushrooms, is being tested primarily against depression; and ketamine has attracted considerable interest for its effectiveness in treating pain, depression and, more recently, PTSD. Yet a central unresolved debate shadows the entire field: is the alteration of consciousness produced by these drugs a necessary condition for their therapeutic benefit, or merely a side effect that happens to accompany it?

To address that question, the team turned to ketamine, a drug that relieves pain while simultaneously inducing dissociation — a temporary sense of detachment from one’s self, body or surroundings. For years, the working assumption had been that these two phenomena were causally linked: the more detached a patient felt, the less intense or threatening their pain would seem. If that were true, then the psychedelic experience itself would be an essential ingredient of the drug’s analgesic action, with major implications for how ketamine-based therapies should be designed.

The study enrolled 37 healthy volunteers, both women and men, each of whom participated in two separate sessions. In one session they received a ketamine infusion; in the other, a placebo. While under the influence of each infusion, participants underwent functional magnetic resonance imaging (fMRI) and were exposed to carefully controlled pain stimuli. Throughout the experiment, they reported both the intensity of their pain and the depth of their dissociative experience, allowing the researchers to track the two phenomena independently and in real time.

The behavioral results were strikingly clear. As expected, ketamine reduced pain and increased dissociation. But when the researchers analyzed the relationship between the two effects, no connection emerged. Participants who experienced strong dissociation did not necessarily enjoy greater pain relief, and those with the most substantial analgesia were not necessarily the most detached. As Dr. Goldway explained, the sense of detachment is simply not the mechanism through which ketamine reduces pain. The two effects, long assumed to be two faces of the same process, appear to run in parallel without one driving the other.

The brain imaging data reinforced the behavioral findings at the neural level. Pain relief was accompanied by reduced activity in brain regions classically involved in processing pain, including the insula and the anterior cingulate cortex — structures that map the intensity and emotional salience of noxious stimuli. Dissociation, by contrast, was related to changes in a different network altogether: the default mode network, a set of interconnected regions implicated in, among other things, the construction of the sense of self. In other words, the study did not merely document two uncorrelated subjective experiences; it revealed two different brain mechanisms, both triggered by the same drug but operating on distinct neural circuitry.

Prof. Hendler, who heads TAU’s Institute for Psychedelic Research, framed the findings within the broader scientific controversy. “In recent years, there has been a lively scientific and clinical debate over whether the subjective experience produced by psychoactive drugs is an integral part of their therapeutic effect,” she said. “Our study shows that, at least in the case of ketamine and pain relief, the answer is apparently no. We were able to show that the drug’s effect on pain is separate from its effect on consciousness — both behaviorally and in terms of brain activity.” She was careful, however, to draw a boundary around the conclusion: “It is important to note that this cannot be generalized to other psychedelic drugs, such as magic mushrooms. In this case, the burden of proof still rests with us, the scientists.”

Dr. Sharon, Deputy Head and Medical Director of the Institute and a specialist in pain medicine, emphasized what the results reveal about the brain itself. The study, he noted, provides further evidence of the complexity of the brain’s representation of the pain experience and of the many different ways the brain can regulate and alter it. Pain, in this view, is not a single signal to be switched off but a multidimensional construct assembled across multiple networks — and different drugs, or different mechanisms within the same drug, can intervene at different points in that assembly process.

The clinical implications could be far-reaching. If researchers can learn how to activate ketamine’s pain-relief mechanism without simultaneously inducing dissociation, it may become possible to develop future treatments that preserve the drug’s analgesic effectiveness while reducing the sense of detachment and other subjective side effects. Such decoupled therapies could broaden access to ketamine’s benefits, particularly for patients who cannot tolerate its psychological effects, and could reshape how clinicians think about the role of altered consciousness in psychedelic-assisted medicine more broadly. For now, the study stands as a landmark demonstration that, where ketamine and pain are concerned, relief and detachment are separable — and the road to better analgesics may not need to pass through an altered mind at all.

Subject of Research: Dissociation of ketamine's analgesic and consciousness-altering brain mechanisms in humans

Article Title: Ketamine relieves pain without “disconnecting” consciousness

Article References: Ketamine relieves pain without “disconnecting” consciousness. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: ketamine, pain relief, dissociation, consciousness, fMRI, psychedelic research, insula, anterior cingulate cortex, default mode network, Neuropsychopharmacology, Tel Aviv University, analgesia

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

The first cluster of studies zeroes in on the close relationship between emotional distress and problematic digital media use. Zhou and Feng demonstrated that loneliness partially mediated the association between social anxiety and smartphone addiction in Chinese college students, hinting that socially anxious young adults may reach for their phones, in part, to cope with interpersonal disconnection. Zhu and colleagues, studying a similar population, found that negative emotions and problematic phone use went hand in hand with poorer life satisfaction and mental health. Tian and colleagues extended the picture with adolescent athletes, showing that resilience was associated with lower mobile phone addiction, an effect explained by reduced anxiety and depressive symptoms. Taken together, these findings converge on a clinically important message: problematic smartphone use is not simply a matter of excessive exposure to technology, but is often embedded in broader difficulties with affective regulation, distress tolerance, and adaptive coping.

A second group of articles emphasizes that addiction-related behaviors take shape within relational and environmental contexts that can either buffer or amplify risk. In a two-wave longitudinal study with children, Wei and Xu found that a more positive school climate predicted lower internet gaming addiction one year later, with deviant peer affiliation acting as a mediator and sensation seeking as a moderator. The results underscore the importance of school environments as potential risk or protective factors during development, a point with obvious implications for prevention programs aimed at children before gaming habits harden into compulsive patterns.

Family dynamics emerged as another powerful contextual force. Xu and colleagues reported that excessive parental psychological control was associated with greater psychological distress in Chinese university students with smartphone addiction. Intriguingly, it was inhibitory emotion regulation, rather than cognitive reappraisal, that helped explain this relationship, suggesting that maladaptive family environments may raise addiction risk not only directly but also by eroding the very self-regulatory capacities that would normally protect against compulsive behavior. Li and colleagues broadened the scope further by examining cosmetic surgery addiction in Chinese adults, finding that adverse childhood experiences, defined as a range of traumatic events before age 18, were linked to greater symptom severity, with insecure attachment and self-alienation serving as mediating factors. Although this form of addiction differs from digital or substance-related addictions, it points to the same long-term role of early adversity, disrupted attachment, and impaired self-related processes in driving compulsive, reward-seeking behavior.

The neurobiological heart of the collection comes from three complementary studies probing how reward circuitry and executive networks miscommunicate. Zhang and colleagues used resting-state functional MRI and dynamic causal modeling to compare brain connectivity in adults with tobacco use disorder against non-smokers. They found altered effective connectivity between the default mode network, an internally oriented circuit active during self-referential thought, and the central executive network, which governs working memory and cognitive control. Specifically, connectivity from the right dorsolateral prefrontal cortex and the medial prefrontal cortex was increased, and this pattern predicted heightened motivation for smoking. If confirmed in larger samples, the authors argue, these findings would support the view that addiction involves altered interactions between internally oriented and executive-control circuits, and they raise the possibility that network-level markers could eventually aid early assessment or risk stratification in clinical settings.

Sensation seeking, a trait long associated with adolescent risk-taking, received its own neural spotlight. Qianlan and colleagues examined sensation seeking, reward sensitivity, and risk adjustment in young adults facing uncertain conditions, measuring behavior alongside electroencephalographic responses. High sensation seekers adjusted more poorly to varying levels of risk and showed weaker neural responses to reward. The findings link reward-related personality traits to reduced flexibility in decision-making under uncertainty, a combination that could help explain why some young people persist in risky choices even as evidence of mounting losses accumulates. The editors note that these promising results deserve expansion with advanced neuroimaging and cognitive modeling techniques that can dissect the precise computational failures involved.

Genetics added a third layer to the mechanistic story. Emond and colleagues genotyped two dopaminergic polymorphisms, ANKK1 rs1800497, better known as Taq1A, and COMT rs4680, along with one nicotinic polymorphism, CHRNA4 rs1044396, in American children aged nine to twelve. All three variants had previously been linked to internet and video gaming addiction. The results showed that the Taq1A polymorphism, which is associated with reduced dopamine sensitivity, was related to higher problematic media use scores in these pre-adolescent children. Although replication in larger samples is needed, the study illustrates how genetic differences in reward sensitivity may quietly shape addiction vulnerability well before the transition to adolescence, potentially setting the stage for later compulsive behaviors.

Together, the mechanistic studies strongly support the core premise of the Research Topic: addiction risk in youth is best understood through the dynamic interplay of genetic predisposition, reward sensitivity, emotion regulation systems, and executive functions. Heightened reward seeking, reduced adaptability to risk, altered large-scale brain connectivity, and possible dopaminergic genetic vulnerability all describe pathways through which impulsive or compulsive behaviors may outpace still-maturing control systems. Crucially, the editors stress that these vulnerabilities are not purely biological. They are shaped by mental health status, family relationships, school context, and adverse early experiences, meaning that any account of youth addiction limited to brain or genes alone would be incomplete.

The translational value of the collection is reinforced by studies on assessment and intervention. Diaz-Guerra and colleagues validated the Executive Functions Scale for University Students, known as UEF-1, in a large sample of Cuban university students. The scale focuses on academic skills and self-regulation in Spanish-speaking populations, and the results supported its seven-factor structure, covering domains such as self-regulation, behavioral monitoring, decision-making, and emotion regulation. This contribution matters psychometrically and conceptually, because culturally appropriate tools are essential for identifying executive-function profiles relevant to addiction risk in diverse populations. Meanwhile, Haenjohn and colleagues piloted the Integrative Cognitive Neuropsychological Program for Inhibitory Control, or ICNIC, in Thai adults with substance use problems. The program combined psychoeducation, behavioral control skills, and computerized tasks aimed at improving inhibition and reducing impulsivity. Participants reported greater self-efficacy in behavioral control, although accuracy rates on visual and auditory Substance Stop Signal Tasks measuring motor inhibition to drug cues did not improve. Even with this mixed pattern, the editors suggest that gains in perceived control may emerge before measurable changes in specific inhibitory processes, meaning multifaceted approaches to cognitive control hold promise for treatment.

Finally, two contributions situate addiction within a broader developmental framework. Using a very large national sample of South Korean adolescents, Jeong and colleagues showed that earlier alcohol use and smoking predicted a later increase in addiction risk, and that smartphone overdependence interacted with substance-related behaviors. Behavioral addictions and substance-related risk behaviors, in other words, may reinforce each other over development rather than unfold in isolation. Complementing this empirical work, Lozano Wun and Luciana reviewed developmental models of addiction and argued for a dual-systems perspective in which an imbalance between reward-related systems and executive control during specific phases of adolescence, interacting with individual differences, heightens vulnerability to reward-seeking behaviors. Both developmental timing and individual differences matter: addiction risk is not static across adolescence, and future prevention efforts may need to be tailored to specific developmental windows. The editors conclude that the collection points toward operational targets for prevention and intervention, including strengthening inhibitory control and emotion regulation, promoting resilience, addressing early adverse experiences, and improving family and school environments, while future longitudinal, multimodal, and cross-cultural research will be essential to clarify causal mechanisms and translate these findings into developmentally sensitive interventions.

Subject of Research: Developmental and neurobiological mechanisms of addiction vulnerability in adolescents and young adults

Article Title: Editorial: Executive functions, reward systems, and addiction in adolescents and young adults

Article References: Passarotti, A. M., & Lee, M. (2026). Editorial: Executive functions, reward systems, and addiction in adolescents and young adults. Frontiers in Adolescent Medicine, 4, Article 1835532. https://doi.org/10.3389/fradm.2026.1835532

Image Credits: AI Generated

DOI: 10.3389/fradm.2026.1835532

Keywords: addiction, adolescence, executive functions, reward systems, smartphone addiction, tobacco use disorder, sensation seeking, default mode network, dopamine genetics, emotion regulation, developmental psychology, public health

The framework builds on a biopsychosocial perspective but pushes it further than the familiar three-pillar model. Rather than treating biology, mind, and society as separate influences whose contributions can simply be added together, Neroni proposes that creativity arises from dynamic, interdependent relationships that shift across individuals, tasks, developmental stages, and historical moments. The unit of explanation, in this view, is not the isolated determinant but the evolving configuration of determinants. This distinction matters because the same ingredient can play radically different roles depending on who is using it, what stage of creative work they are in, and what technological or social environment surrounds them. The perspective draws on earlier systemic models, such as the influential account locating creativity in the relationship among person, domain, and field, and on sociocultural approaches that dissolved the boundary between mind and material world. What is new is the extension of this relational logic across all levels of analysis, from neural networks to algorithms.

The neuroscience section of the argument illustrates the problem vividly. Over the past two decades, creativity research has moved away from hunting for a discrete creative brain area toward network-based models. Attention has focused on interactions between the default mode network, which supports internally directed thought, episodic memory, and spontaneous associative processing, and the executive control network, which contributes to working memory, attentional regulation, and the evaluation of emerging ideas. A third system, the salience network, appears to coordinate shifts between internally and externally oriented processing. Studies of divergent thinking, in which people generate multiple unusual uses for everyday objects, have repeatedly found functional coupling between these networks, supporting the idea that creative thought depends on cooperation between idea generation and cognitive control.

Yet recent critiques caution against replacing one reductionism with another. As the framework notes, studies differ substantially in the tasks they use, how they score creativity, and the neural patterns they report, and individual differences in how people approach creative tasks are often overlooked. Evidence that the default mode and executive networks cooperate during divergent thinking does not establish a general neural system for creativity. A broader concern holds that creativity should not be treated as a single cognitive faculty with a dedicated neural substrate, because creative activities recruit very different combinations of cognitive, emotional, perceptual, motor, and domain-specific processes. Findings from lesion studies reinforce this picture: damage to anatomically distinct brain regions can alter fluency, flexibility, or originality, yet distinct sites may connect to common functional circuits, suggesting distributed systems rather than a creative command center. Genetic research tells a similar story. Associations between creative performance and dopaminergic gene variants exist, but creativity is a complex behavioral phenotype that cannot map directly onto individual genes; genetic influences operate indirectly through temperament, reward sensitivity, and neural plasticity, and their expression depends on developmental and environmental conditions.

Development itself dismantles the notion of a biologically fixed creative capacity. Lifespan research distinguishes creative potential, everyday creative behavior, domain-specific performance, and socially recognized achievement, each following different trajectories. Childhood offers neural plasticity and exploratory behavior that favor experimentation, but children lack accumulated domain knowledge, and whether their explorations yield outcomes judged creative depends on task, educational environment, materials, and evaluative criteria. Later adulthood may bring declines in processing speed alongside continued growth in expertise and semantic knowledge, changing the resource mix rather than simply raising or lowering creativity. Research on sex and gender drives the point home: recent meta-analyses find only minimal average gender differences in creative ability, and differences in variability cannot explain the much larger disparities in eminent creative achievement, which instead reflect educational opportunities, institutional barriers, professional networks, and systems of cultural recognition. The significance of any biological characteristic, the framework argues, depends on the developmental, social, and cultural conditions in which it operates.

Psychological research reveals the same conditionality. Executive functions such as working memory, inhibition, and cognitive flexibility can help maintain goals, suppress dominant responses, and shift perspectives, yet stronger executive control is not always beneficial. Control helps when a task demands evaluation, refinement, or adherence to constraints, while less constrained processing may better facilitate access to unusual associations during idea generation. Cognitive flexibility can restructure an ineffective representation, but constant shifting becomes counterproductive when creative work requires sustained elaboration. Metacognition offers a way to understand this coordination: creative activity requires not only generating ideas but assessing progress, selecting strategies, and deciding whether to continue exploring or change direction. Emotion follows the same pattern. Positive affect has been linked to broad attention and associative flexibility, while some negative states support persistence and analytical processing, but recent experimental work shows that cognitive reappraisal facilitated creativity particularly among individuals lower in openness to experience, demonstrating how an emotion-regulation strategy interacts with personality rather than acting universally.

Social and cultural environments do not merely facilitate or inhibit individual creativity; they help shape the possibilities people encounter, the forms of novelty they recognize, and the outcomes that acquire value. Ideas about originality, usefulness, authorship, and innovation vary across societies and historical periods, and recent reviews caution against treating Western and Eastern cultures as homogeneous categories. Multicultural experience has been associated with greater cognitive flexibility, but exposure alone is insufficient; its effects depend on openness, language competence, domain knowledge, and the quality of intercultural engagement. In education, the long-standing debate over whether schooling stifles creativity through standardization or provides the knowledge needed for creative development remains unresolved, and the framework argues the opposition between structure and freedom is too simple. Structure can limit exploration when rigid, but it also supplies the knowledge, strategies, and feedback needed to develop an idea. Autonomy encourages exploration but may be less useful when learners lack the metacognitive skills to evaluate and refine what they generate.

Organizational research adds further layers. Psychological safety, autonomy, and diversity can support creativity, but none guarantees it; actor-context relationships can be synergistic, compensatory, inhibitory, or characterized by diminishing returns. Conditions that increase individual idea generation do not necessarily improve collective innovation, because teams must coordinate expertise, manage disagreement, and select among alternatives. Digital environments complicate the picture further. Recommendation systems and platform architectures shape what information people encounter and which ideas become visible, sometimes expanding access to diverse material while reinforcing particular patterns of attention and popularity. Generative artificial intelligence makes technology’s role even more direct, participating in ideation, recombination, evaluation, and production rather than merely providing access to information.

The evidence on human-AI collaboration illustrates the framework’s central claim with striking clarity. One influential study found that access to generative AI improved evaluations of individual creative stories, particularly among less creative writers, but simultaneously increased similarity across outputs and reduced collective diversity. Another line of research compared large language models used as ghostwriters, taking the primary role in content generation, versus sounding boards providing feedback on human-generated material. Sounding boards improved outcomes for non-experts, while ghostwriting provided no comparable advantage and was detrimental to expert users, possibly by anchoring subsequent human contributions. A separate experiment found that participants produced less creative poetry when given an AI-generated poem to edit than when writing independently, but this disadvantage disappeared when the system supported iterative co-creation with human and AI alternately contributing, and the co-creative configuration preserved higher creative self-efficacy. A meta-analysis of human-AI collaboration found substantial variation in outcomes depending on the relative strengths of humans and machines and how their contributions were combined. The same technology, in other words, can benefit individual performance while constraining diversity at the collective level.

From this configurational account, the framework derives four mechanisms—constraint, affordance, regulation, and feedback or selection—that describe how relationships across levels develop over time. Constraints can limit exploration but also reduce an unmanageable search space and focus attention. Affordances are relational possibilities for action that emerge between a person and their environment, such as an AI system enabling rapid generation of alternatives whose usefulness depends on the user’s expertise and evaluative skills. Regulation encompasses metacognitive monitoring, cognitive control, motivation, and emotion at the individual level, and feedback, norms, and collaboration at the institutional level. Feedback and selection make creative activity recursive, as products are accepted or rejected and systems amplify some outputs while overlooking others. The framework yields testable propositions: the effect of any creativity-related factor should vary with conditions, factors supporting one phase of creative activity should not necessarily support another, the same intervention can have opposite effects at different levels, and the impact of opportunities or constraints should change across development. Rather than asking which factors universally increase creativity, researchers should now ask for whom, under which conditions, at which stage and level, and over what timescale particular factors support or constrain creative activity—a question that demands ecological, longitudinal, multilevel, and interdisciplinary research designs capable of following creative processes as they unfold in the real world.

Subject of Research: A biopsychosocial and sociotechnical framework explaining creativity as a configurational phenomenon across biological, psychological, cultural, and technological levels

Article Title: Creativity beyond the isolated mind: toward a biopsychosocial and sociotechnical perspective

Article References: Neroni, M. A. (2026). Creativity beyond the isolated mind: toward a biopsychosocial and sociotechnical perspective. Frontiers in Cognition, 5, Article 1893765. https://doi.org/10.3389/fcogn.2026.1893765

Image Credits: AI Generated

DOI: 10.3389/fcogn.2026.1893765

Keywords: creativity, neuroscience, biopsychosocial, generative AI, cognitive flexibility, default mode network, executive control, sociocultural, developmental psychology, organizational creativity, human-AI collaboration, metacognition

The research team, led by investigators affiliated with hospitals and medical colleges in Yancheng and Nantong, China, took a fundamentally different approach to the puzzle. Rather than asking where in the brain gray matter changes occur, they asked what functional network those changed regions belong to. The underlying idea, drawn from a rapidly growing movement in neuroscience, is that brain disorders are best understood not as problems of isolated spots but as disturbances of interconnected networks. When different anatomical locations produce similar clinical pictures, it is often because they are wired into the same functional circuit. The team used a technique called functional connectivity network mapping, or FCNM, to test whether the spatially scattered gray matter findings in IGE might converge on a shared network architecture.

To build their dataset, the researchers systematically searched PubMed, Embase, and Web of Science following PRISMA guidelines, ultimately identifying 18 whole-brain VBM studies of IGE published up to May 2026. These studies collectively reported 111 peak coordinates of gray matter alteration from 1,492 participants, including 694 patients and 798 healthy controls. The epilepsy subtypes represented ranged from juvenile myoclonic epilepsy—the most frequently studied—to childhood absence epilepsy, juvenile absence epilepsy, and epilepsy with generalized tonic-clonic seizures alone. Patient ages spanned from early childhood to middle adulthood, and disease durations ranged from a few months to two decades. Crucially, the team pooled coordinates reflecting both increased and decreased gray matter in their primary analysis, treating the direction of change as secondary to the question of network membership.

The methodological heart of the study lies in how those coordinates were transformed into a network map. Each of the 18 patient-control contrasts yielded its own set of gray matter coordinates, which were converted into 4-millimeter-radius spherical seeds. Using resting-state functional MRI data from 1,093 healthy adults in the Human Connectome Project S1200 release, the researchers calculated whole-brain functional connectivity maps for each seed by correlating the seed’s mean time series with that of every other brain voxel. Pearson correlations were converted to Fisher’s z values, individual maps were combined through one-sample t-tests, and the resulting group-level maps were thresholded using voxel-wise false discovery rate correction at p < 0.05. Only positive connectivity was considered, reflecting ongoing debate about the biological interpretation of negative connectivity measures. The binarized maps from all 18 contrasts were then overlaid, and only voxels showing positive connectivity with at least 60 percent of the contrast-specific seeds were retained in the final network—a criterion previously validated in FCNM studies.

The result was striking. Despite the anatomical scatter of the input coordinates, the analysis revealed a coherent, distributed normative connectivity pattern anchored in four principal regions: the bilateral middle temporal gyri, the bilateral thalami, the left superior frontal gyrus, and the right putamen. When the team measured how much of this network overlapped with eight canonical brain networks—seven cortical networks defined by the Yeo parcellation plus a subcortical network delineated from the Human Brainnetome Atlas—the subcortical network dominated, accounting for 42.77 percent of the overlap. The default mode network, a cortical system involved in self-referential thinking and internally directed cognition, came in a distant second at 11.06 percent. Overlap with the visual, somatomotor, dorsal attention, ventral attention, limbic, and frontoparietal networks each fell below 10 percent.

Replication is where many neuroimaging findings falter, so the team put their network to the test. They repeated the entire FCNM procedure using an independent, cross-scanner validation cohort: the Southwest University Adult Lifespan Dataset, comprising 329 healthy individuals scanned on a different 3 Tesla Siemens system. The validation analysis produced a broadly similar spatial pattern, with the subcortical network again showing the greatest overlap at 43.98 percent. The default mode network correspondence, however, was noticeably weaker in the validation dataset than in the discovery analysis—a discrepancy the authors attribute partly to the wider age range of the SALD sample and to differences in preprocessing pipelines, including the use of ICA-FIX denoising in the Human Connectome Project data but not in SALD.

Sensitivity analyses reinforced the robustness of the subcortical signal while exposing the fragility of the cortical component. When the researchers varied the seed radius from 4 millimeters to 1 and 7 millimeters, the resulting maps preserved their dominant subcortical and default mode overlap. When they separated gray matter increases from decreases, a potentially intriguing dissociation emerged: decreased gray matter foci showed their greatest overlap with the subcortical network at 65.84 percent, while increased gray matter foci aligned more with the default mode network at 37.23 percent. The authors caution that these direction-specific analyses rest on fewer contrasts and should be treated as hypothesis-generating rather than definitive. A leave-one-contrast-out analysis, in which each of the 18 contrasts was excluded in turn and the FCNM procedure repeated, found that 14 of the 18 resulting maps—77.7 percent—reproduced the major network pattern.

The convergence on the thalamus and putamen carries substantial biological weight. The thalamus has long been implicated in generalized epilepsies: structural MRI studies have documented thalamic volume and morphology abnormalities in childhood absence epilepsy, juvenile myoclonic epilepsy, and generalized tonic-clonic seizures, and functional imaging has captured thalamic activation during typical absence seizures coinciding with generalized spike-and-wave discharges. Neurochemical work has revealed altered GABA concentrations in the frontal lobe and thalamus of patients with juvenile myoclonic epilepsy. The putamen, meanwhile, has emerged as a subcortical hub in generalized epilepsy pathogenesis, with multimodal imaging revealing microstructural integrity deficits and diminished dopamine D2/D3 receptor binding that may weaken the basal ganglia’s inhibitory grip on pathological cortical discharges. Prior intracranial EEG research has further shown that interictal thalamocortical network configuration tracks susceptibility to consciousness-impairing seizures, and thalamic deep brain stimulation and vagus nerve stimulation are thought to act partly by restoring normative thalamocortical rhythms.

The default mode network component of the findings is more tentative. The middle temporal gyrus and superior frontal gyrus nodes identified in the study are known to participate in cognitive and emotional processing, and previous work has documented both structural and functional abnormalities in these regions in juvenile myoclonic epilepsy. Broader epilepsy literature has consistently reported altered functional connectivity within the default mode network across multiple syndromes, particularly at the posterior cingulate cortex and precuneus, and dynamic analyses suggest these subtle network anomalies may be more sensitively captured by temporal variability measures than by static connectivity. Yet because the DMN overlap was not comparably reproduced in the validation dataset, the authors interpret it as a secondary, exploratory observation rather than a definitive network-level finding.

The researchers are careful to frame their conclusions within important limits. Because the functional connectivity was derived entirely from healthy adults, the identified network represents the normative connectivity profile of reported gray matter alteration sites, not direct evidence of abnormal connectivity in patients. No coordinate-based permutation or spatially constrained null-model analysis was performed, so the study cannot establish that the observed overlap exceeds chance expectation or that it is disease-specific—particularly given the thalamus’s inherently broad connectivity. The developmental mismatch between the adult normative connectome and pediatric patient samples, the heterogeneity of scanners and analytic methods across the included studies, and the absence of negative findings from the original literature all add uncertainty. Still, the authors argue that their findings provide preliminary, descriptive evidence that the scattered gray matter alterations of IGE are embedded within a reproducible subcortical normative connectivity pattern, reframing IGE as a distributed cortico-subcortical network disorder and offering a framework that may guide future network-based biomarkers and candidate targets for neuromodulation.

Subject of Research: Normative functional network localization of gray matter alterations in idiopathic generalized epilepsy

Article Title: Normative functional network localization of gray matter alterations in idiopathic generalized epilepsy

Article References: Wang, X., Zhang, Z.-P., Zheng, B.-C., Liu, L., Wang, G.-D., Yang, H.-C., & Ma, L.-L. (2026). Normative functional network localization of gray matter alterations in idiopathic generalized epilepsy. Frontiers in Neurology, 17, Article 1957963. https://doi.org/10.3389/fneur.2026.1957963

Image Credits: AI Generated

DOI: 10.3389/fneur.2026.1957963

Keywords: idiopathic generalized epilepsy, gray matter, voxel-based morphometry, functional connectivity network mapping, thalamus, putamen, default mode network, subcortical network, resting-state fMRI, Human Connectome Project, juvenile myoclonic epilepsy, brain networks

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

The research team compared resting-state functional magnetic resonance imaging data from 94 patients with recurrent major depressive disorder who experienced suicidal ideation, 53 patients with recurrent depression who did not, and 84 healthy controls. Resting-state fMRI, which records spontaneous brain activity while participants simply lie still in the scanner, allows researchers to compute functional connectivity, a statistical measure of how strongly the activity of different brain regions rises and falls together over time. Regions that co-activate are assumed to form functionally coupled networks, and disruptions in this coupling have repeatedly been implicated in psychiatric illness.

By organizing the brain into its canonical intrinsic networks, including the default mode network, the visual network, the sensorimotor network, the dorsal attention network, the frontoparietal network, the ventral attention network and the subcortical network, the investigators set out to ask two complementary questions. First, which connections between networks are abnormally weak or strong in each patient group? Second, does the global architecture of the brain network, as captured by graph theory, differ between patients with and without suicidal thoughts? Graph theory treats the brain as a set of nodes, corresponding to regions, connected by edges, corresponding to functional links, and then computes properties such as how efficiently information can travel across the network.

The answer to the first question was strikingly clear. When compared with healthy controls, the patients with suicidal ideation showed more widespread dysconnectivity than the patients without suicidal ideation. Specifically, the suicidal ideation group exhibited reduced connectivity between the default mode network and the visual network, between the default mode network and the sensorimotor network, and between the sensorimotor network and the dorsal attention network. The default mode network, which is most active during self-referential thought and rumination, is a central player in depression research, and its weakened dialogue with perceptual and attentional systems may reflect how ruminative loops intrude on ordinary sensory and attentional processing.

Importantly, the two patient groups were not simply more or less severe versions of the same picture. Patients with and without suicidal ideation shared some impairments, notably reduced connectivity between the visual network and the sensorimotor network and within the sensorimotor network itself, suggesting that these changes form part of the common neural substrate of recurrent depression. But the suicidal ideation group carried an additional burden. Relative to depressed patients without suicidal thoughts, they showed reduced connectivity between the visual network and the dorsal attention network, and between the default mode network and the sensorimotor network. These two connections, moreover, were negatively correlated with the severity of suicidal ideation, meaning that the weaker the functional link, the more severe the suicidal thoughts tended to be.

That correlation is the kind of finding that turns a descriptive study into a clinically meaningful one. If the strength of specific inter-network connections tracks the intensity of suicidal thinking, then those connections could serve as candidate imaging biomarkers, objective measurements that complement what patients disclose. The study also examined the second question, the topological organization of the network. Patients with suicidal ideation showed nodal topological alterations in the occipital lobe relative to both depressed controls without suicidal ideation and healthy participants. However, this topological difference did not reach statistical significance, a sobering reminder that connectivity measures and network-architecture measures may capture different aspects of brain dysfunction with different statistical power in a given sample.

The most ambitious part of the study was its attempt to translate group-level differences into individual-level diagnosis. Using a support vector machine, a standard supervised machine learning algorithm, the researchers tested whether the features that differed significantly between groups could classify individual patients. The result was encouraging: a classifier that combined functional connectivity measures with topological metrics outperformed classifiers built on either type of feature alone, achieving an accuracy of 0.74 to 0.78 in identifying patients with suicidal ideation. In a field where neuroimaging-based prediction of suicidal behavior has struggled to reach usable levels of accuracy, a combined-feature approach approaching 78 percent is a notable proof of concept.

The logic behind combining the two feature types is worth unpacking. Functional connectivity features capture local abnormalities in specific circuits, such as the weakened link between the visual and dorsal attention networks that correlated with suicidal severity. Topological features, by contrast, describe more global properties of how nodes are embedded in the network as a whole, such as whether a region serves as an efficient relay point for information flow. Because these two perspectives are mathematically distinct, a classifier that sees both can in principle detect patterns that neither could reveal alone, and the observed performance gain supports that intuition, even if it will need to be validated in independent cohorts before any clinical deployment.

The study’s scale and design strengthen its credibility. It drew on data from the REST-meta-MDD consortium, a large multi-site Chinese resting-state fMRI collaboration, with data collected at multiple hospitals including The Affiliated Guangji Hospital of Soochow University, the First Affiliated Hospital of China Medical University, the First Affiliated Hospital of Xi’an Jiaotong University, the Faculty of Psychology of Southwest University, Second Xiangya Hospital of Central South University and Affiliated ZhongDa Hospital of Southeast University, each with local ethics approval and written informed consent from participants. Multi-site designs are essential for brain-imaging biomarker research because they guard against findings that are artifacts of a single scanner or a single recruitment strategy, although they also introduce site-related variability that analysis pipelines must control carefully.

The broader implications are considerable. Suicide remains a leading cause of death worldwide, and most people who die by suicide have contact with health services beforehand, yet prediction at the individual level remains notoriously difficult. Studies like this one do not claim that a scan can predict who will act on suicidal thoughts; suicidal ideation itself is the target here, and ideation is one step in a complex cascade. Still, identifying a neural signature associated with ideation in recurrent depression, one that correlates with severity and supports above-chance individual classification, adds a biologically grounded instrument to a risk-assessment toolkit that has long relied on behavioral and clinical information alone. The authors suggest that the combination of connectivity and topological metrics may hold potential as imaging markers to aid clinical diagnosis, and future work will determine whether such markers can be refined, replicated and eventually woven into routine psychiatric practice.

Subject of Research: Functional brain network connectivity and topology in recurrent major depressive disorder with and without suicidal ideation

Article Title: Impaired connectivity and topology of functional networks in recurrent major depressive disorder with and without suicidal ideation

Article References: Li, H., Wang, X., Wang, Y., Li, X., Zhang, F., Hang, H., Li, J., Huang, J., Zhou, J., Xiao, L., Feng, L., Zhou, Y., Yan, C., Dong, K., Ji, X., & Yu, A. (2026). Impaired connectivity and topology of functional networks in recurrent major depressive disorder with and without suicidal ideation. BMC Psychiatry. https://doi.org/10.1186/s12888-026-08638-1

Image Credits: AI Generated

DOI: 10.1186/s12888-026-08638-1

Keywords: suicidal ideation, major depressive disorder, resting-state fMRI, functional connectivity, brain networks, graph theory, default mode network, sensorimotor network, dorsal attention network, support vector machine, neuroimaging biomarkers, BMC Psychiatry