Most recent paper

Neuroimage. 2026 Mar 25:121888. doi: 10.1016/j.neuroimage.2026.121888. Online ahead of print.

ABSTRACT

The Default Mode Network (DMN) is a large-scale intrinsic brain network critically involved in internally oriented cognition, including autobiographical memory. Core DMN regions such as the hippocampus and medial prefrontal cortex are central to memory retrieval, schema construction and self-referential processing. Individuals with Highly Superior Autobiographical Memory (HSAM) provide a unique model to investigate the neural mechanisms underlying exceptional memory ability. However, the intrinsic functional connectivity and temporal dynamics of the DMN in HSAM remain largely unexplored. To provide new insight into the baseline network mechanisms that supports HSAM irrespective of memory retrieval, in this study we examined both static and dynamic features of DMN functional architecture in 12 HSAM individuals and 31 matched controls during resting-state fMRI. Using a multilevel analytical framework encompassing link-level, node-level, and whole-network level measures, we characterized connectivity strength, temporal variability, and co-activation dynamics within the DMN. HSAM individuals showed enhanced and more temporally stable functional connectivity among memory-related, schema-related, and self-referential DMN regions, including the hippocampus, temporal pole, and ventromedial prefrontal cortex. These findings suggest that HSAM is associated with a more integrated and stable DMN organization, potentially supporting continuous memory replay and the consolidation of autobiographical experiences. This enhanced DMN coherence may represent a neural signature of HSAM.

PMID:41895550 | DOI:10.1016/j.neuroimage.2026.121888

Neuroimage. 2026 Mar 25:121883. doi: 10.1016/j.neuroimage.2026.121883. Online ahead of print.

ABSTRACT

Dexterous hand motor behavior emerges from coordinated interactions within a distributed brain network. While task-related neural dynamics have been investigated, recent fMRI studies showed that also spontaneous - i.e. non-task related - brain connectivity can predict task-specific performance. Still, it remains unclear whether spontaneous functional connectivity reflects also the encoding of general aspects of hand motor control. Here, we applied connectome-based predictive modelling (CPM) to resting-state functional connectivity (rs-FC) from the Human Connectome Project (HCP) to predict performance in hand motor tasks. We identified a "core" hand motor network whose intrinsic connectivity predicted not only task-specific measures (dexterity and strength) but generalised its prediction across different effectors and tasks. This "core" model also generalized its predictions to an independent dataset (external validation), including different behavioral measures and rs-fMRI data. In addition, transcranial magnetic stimulation (TMS) over inferior parietal cortex selectively impacted the core model's predictive power in a time-dependent manner, consistent with the known neurophysiological effects of the stimulation protocol. Together, these findings demonstrate that spontaneous brain activity encodes behaviorally relevant information about hand motor control, spanning both low-level features and higher-order representations. By linking spontaneous brain activity to behavioural motor outcomes, our findings pave the way for better understanding how spontaneous connectivity alterations might underlie motor dysfunction in neurological disorders.

PMID:41895547 | DOI:10.1016/j.neuroimage.2026.121883

Tomography. 2026 Mar 2;12(3):30. doi: 10.3390/tomography12030030.

ABSTRACT

BACKGROUND: In temporal lobe epilepsy (TLE), locally reduced glucose metabolism (i.e., hypometabolism) is indicative of the epileptogenic onset zone (EZ). Here, we investigate the potential value of resting-state fMRI (rs-fMRI) for localizing the EZ with fluorodeoxyglucose positron emission tomography (FDG-PET) as ground truth.

METHODS: Twelve PET-positive patients (34.1 ± 13.1 y; 5 females) with unilateral drug-resistant TLE were included. FDG-PET and rs-fMRI were acquired simultaneously at a hybrid 3T PET-MR scanner. Hypometabolic regions were identified on the FDG-PET images by a nuclear medicine expert. The FDG-PET images were compared with a clinical FDG-PET control dataset with normal glucose uptake distribution. The output z-score maps were thresholded at z < -2 to produce a binary mask of the significantly hypometabolic regions. The hypometabolism masks were mirrored onto the contralateral hemisphere for the asymmetry comparison. Regional homogeneity (ReHo), amplitude of low-frequency fluctuations (ALFF), and fractional ALFF (fALFF) were calculated from the rs-fMRI in conventional (0.01-0.1 Hz) and slow-3 (0.073-0.198 Hz) frequency bands. Asymmetry indices (AIs) were calculated using the ipsilateral and contralateral hypometabolic masks in the PET-positive subjects and assessed via the one-sample Wilcoxon test and Spearman correlation coefficients.

RESULTS: The AIs of conventional fALFF were significantly lower in the hypometabolic zone (p < 0.05). A significant negative correlation was found between the AIs of FDG-PET and fALFF in the slow-3 band (r = -0.62; p < 0.05).

CONCLUSIONS: Conventional and slow-3 band fALFF showed a potential to mimic the FDG-PET findings in terms of EZ localization. Further research with extended cohorts and histopathological validation is required to determine the clinical value.

PMID:41893825 | DOI:10.3390/tomography12030030

Brain Sci. 2026 Mar 19;16(3):327. doi: 10.3390/brainsci16030327.

ABSTRACT

Background/Objectives: Young adulthood is a critical developmental period during which lifestyle behaviors may shape intrinsic brain network dynamics that support cognition. This pilot longitudinal intervention study examined whether variability in physical activity and sedentary behavior during an 8-week exercise and/or cognitive intervention protocol was associated with changes in intrinsic brain dynamics and cognitive and mood outcomes in undergraduate young adults. Methods: Participants (n = 32) completed resting-state functional magnetic resonance imaging (rs-fMRI) at baseline (T1) and post-intervention (T2). Dynamic functional network connectivity (dFNC) was estimated from 53 intrinsic connectivity networks derived using spatially constrained independent component analysis (ICA). Ten recurring dynamic connectivity states were identified and individualized using constrained dynamic double functional independent primitives (c-ddFIPs). State occupancy and dynamic convergence and divergence metrics were computed to characterize network flexibility. Results: Greater moderate-to-vigorous physical activity was modestly but consistently associated with increased occupancy of integrative higher-order states, particularly States 6 and 7, and reduced occupancy of more segregated configurations. More physically active individuals also demonstrated greater divergence between integrative and low-engagement states, whereas greater sedentary time corresponded to increased similarity among segregated configurations. Working memory performance showed parallel associations with more integrative and better-differentiated dynamic patterns. Conclusions: These findings suggest that dynamic functional network reconfiguration may represent a neurobiological mechanism linking lifestyle behaviors and cognitive health in young adulthood. Furthermore, they highlight the translational promise of engagement-driven, low-burden programs for college-aged young adults, showing that even modest variability in habitual physical activity corresponds to greater engagement and differentiation of integrative connectivity states linked to executive and broader cognitive functions.

PMID:41892670 | DOI:10.3390/brainsci16030327

Brain Sci. 2026 Mar 14;16(3):312. doi: 10.3390/brainsci16030312.

ABSTRACT

BACKGROUND: Previous research has found that mindfulness-based techniques are beneficial for reducing stress in heavy-drinking individuals. However, the underlying neurobiology of these stress-reducing effects are unclear. Moreover, much of the research examining neurobiological correlates of mindfulness has used static functional connectivity, suggesting that brain activity goes unchanged for the entire length of an MRI scan.

METHODS: In the current study, we used a state-based dynamic functional connectivity model to examine brain states during either a 10 min mindfulness session or resting control that followed an individually tailored stress imagery task. Using a hidden semi-Markov model (HSMM), six brain states and the associated dynamics of state traversal were estimated for a population of moderate-to-heavy drinkers (N = 32). We modeled the 36 Schaefer atlas regions spanning the salience and default mode networks, and the HSMM characterized each state by its distinct multivariate pattern of activity and covariance structure. Group differences in dwell times, transition behavior, and overall state dynamics were evaluated using permutation tests and mixed-effects models.

RESULTS: Participants that experienced the mindfulness session had more transitions and longer time spent in states in which the salience network was more active. Participants assigned to the control group had more transitions and increased time spent in states in which nodes of the default mode network were more active. Moreover, for control participants, increased occupancy time to SN-dominant states was associated with lower perceived stress.

CONCLUSIONS: Using HSMM provided a unique insight into network connectivity during mindful states; we believe it offers a novel approach to testing and optimizing mindful-based therapies.

PMID:41892655 | DOI:10.3390/brainsci16030312

J Pain Res. 2026 Feb 4;19:573011. doi: 10.2147/JPR.S573011. eCollection 2026.

ABSTRACT

OBJECTIVE: This study integrates resting-state functional magnetic resonance imaging (rs-fMRI) studies to systematically identify core regions of altered regional brain activity and abnormal functional connectivity within cerebral networks in patients with primary trigeminal neuralgia (PTN).

METHODS: We systematically searched PubMed, Web of Science, and EMBASE for rs-fMRI studies (between January 2010 and June 2025) comparing PTN patients with healthy controls (HCs). Through a standardized data extraction protocol, coordinates of divergent brain functional metrics and network connectivity parameters were collected. Ultimately, we included 16 studies, of which 14 were related to functional specific indices (375 patients) and 5 to functional connectivity (204 patients). Separate modality-specific Activation Likelihood Estimation (ALE) meta-analyses were conducted for studies employing Amplitude of Low-Frequency Fluctuation (ALFF/fALFF), Regional Homogeneity (ReHo), and Degree Centrality (DC), to reflect their unique neurophysiological meanings.

RESULTS: ALE analysis revealed index-specific alteration patterns with limited spatial overlap. The ALFF/fALFF analysis identified consistent hypoactivity in the left medial prefrontal cortex. In contrast, ReHo and DC analyses converged to show hyperactivity in the posterior cerebellar lobe and temporal pole. Notably, Seed-based functional connectivity analyses yielded no convergent findings, and limbic system activation was highly heterogeneous.

CONCLUSION: Our meta-analysis demonstrates that PTN involves complex, multidimensional brain remodeling, not a dysfunction of a single "pain center." The distinct spatial patterns identified by different rs-fMRI metrics underscore their complementary value in uncovering PTN pathophysiology. These findings advance the understanding of PTN's central mechanisms, support the development of targeted interventions, and highlight the need to differentiate neurophysiological metrics and clinical subtypes in future research.

PMID:41890577 | PMC:PMC13016099 | DOI:10.2147/JPR.S573011

Front Psychiatry. 2026 Mar 11;17:1745388. doi: 10.3389/fpsyt.2026.1745388. eCollection 2026.

ABSTRACT

BACKGROUND: Intermittent theta burst stimulation (iTBS) to the dorsolateral prefrontal cortex (DLPFC) for major depression has been FDA-approved in the United States since 2018. Accelerated iTBS (aiTBS) protocols of multiple treatments per day have shown promising response and remission rates for major depression, especially when combined with connectivity-guided targeting. Brain networks associated with emotion regulation demonstrate significant changes in connectivity after effective iTBS. However, these findings have been confined to treatment of the left DLPFC, despite literature suggesting equivalent outcomes with right side stimulation. To date there has not been a direct comparison of clinical outcomes and connectivity changes between left and right DLPFC aiTBS for depression.

METHODS: Forty-four patients aged 50-79 with chronic major depressive disorder underwent open-label accelerated fMRI-guided aiTBS (45 sessions, 9 days) to the DLPFC (18 left, 26 right). Depression, anxiety, and anhedonia symptoms were assessed, and resting-state fMRI was obtained at baseline (Visit 1), after 15 sessions (Visit 2), and end of treatment (Visit 3). Patients who were not demonstrating at least 10% improvement in depression at Visit 2 were switched to contralateral stimulation for the remaining 30 sessions.

RESULTS: For the entire cohort (N = 44), mean depression (IDS-C30) scores decreased significantly over the course of treatment. Mean change in IDS-C30, GAD-7, TEPS, SHAPS, and BISBAS between participants with right-sided stimulation (N = 26) and those with left-sided stimulation (N = 18) were not statistically different. Functional connectivity analysis demonstrated significant decreases in connectivity from Visit 1 to Visit 3 between default mode network and limbic networks in patients receiving right DLPFC iTBS, whereas patients receiving left DLPFC iTBS demonstrated limited changes in connectivity.

CONCLUSION: Accelerated iTBS to the right DLPFC appears to have equivalent efficacy as aiTBS to the left DLPFC in terms of magnitude of reduction of depressive, anxious, and anhedonic symptoms in a late-life population. However, connectivity changes associated with treatment were asymmetric, and may reflect hemispheric lateralization of functional network responses to iTBS. Further work is needed to confirm the comparative efficacy and network dynamics of left versus right hemisphere aiTBS for depression.

PMID:41890433 | PMC:PMC13014255 | DOI:10.3389/fpsyt.2026.1745388

Front Hum Neurosci. 2026 Mar 11;20:1757604. doi: 10.3389/fnhum.2026.1757604. eCollection 2026.

ABSTRACT

BACKGROUND: Medical staff frequently experience sleep deprivation, impacting both their health and patient care quality. Understanding brain network changes under sleep deprivation can guide preventive strategies. This study aims to determine how total sleep deprivation (TSD) alters brain network topology in medical professionals.

METHODS: Using graph-theory analysis of resting-state fMRI data from 36 medical staff, we assessed global and local brain network properties following TSD and normal sleep (rested wakefulness, RW), examining topological changes and their correlation with cognitive performance.

RESULTS: Small-world properties were present in both conditions, but the TSD condition showed higher clustering coefficients (p = 0.044). Key nodal changes included increased degree centrality in the right superior medial frontal gyrus (p = 0.0006) and decreased nodal efficiency in the left fusiform gyrus (p = 0.0004). Using the right superior medial frontal gyrus as ROI, enhanced functional connectivity (zFC) was observed in multiple bilateral frontal/temporal regions (peak t > 4.5). These topological changes correlated with cognitive deficits: reduced Digit Symbol Test (DST) scores (p < 0.001), prolonged Number Connection Test-A (NCT-A) and Line Tracing Test (LTT) completion times (p < 0.05), while increased clustering coefficients (Cp) positively correlated with NCT-A/SDT performance changes (r = 0.341-0.411, p < 0.05). And older staff exhibited greater vulnerability in global network efficiency and path length (r = -0.352, r = 0.390, p < 0.05).

CONCLUSION: By identifying key brain network nodes affected by TSD, this study provides insights into neural adaptations under TSD, offering an evidence-based framework for developing both therapeutic interventions and preventive strategies to mitigate cognitive and health impacts in high-risk populations.

PMID:41890345 | PMC:PMC13013376 | DOI:10.3389/fnhum.2026.1757604

Imaging Neurosci (Camb). 2026 Mar 24;4:IMAG.a.1179. doi: 10.1162/IMAG.a.1179. eCollection 2026.

ABSTRACT

Determining the functional organization of the auditory cortex (AC) has been difficult with conventional task-based approaches due to the broad responsiveness of auditory subregions to various acoustic properties. Moreover, most studies have investigated functional organization of AC with static methods, although the brain has shown to be organized into dynamic networks. Here, we investigated dynamically varying coactivation patterns of the local networks in the auditory cortex (AC) determined from 7T fMRI data. Dynamic AC patterns successfully captured interindividual variability, as indicated by significantly higher variability between than within individuals for the AC pattern occurrence rates and spatial topographies. The coactivation patterns shared similarities between resting-state and auditory-task data, as indicated by the group-level similarity of 0.84 and individual-level similarity of 0.71 in the spatial topographies. Furthermore, the occurrence rates of AC patterns identified in the task data, using pattern templates derived from resting-state data, correlated with specific task contrast regressors. Our results suggest that the AC function can be characterized by a set of dynamically varying coactivation patterns. These patterns are consistently observed during resting state and auditory stimulation, and they become synchronized with auditory inputs. These findings enhance our understanding of the relationship between spontaneous and stimulus-driven activity in the AC and support the development of more time-efficient paradigms for studying its functional organization.

PMID:41890246 | PMC:PMC13015430 | DOI:10.1162/IMAG.a.1179

Hum Brain Mapp. 2026 Apr 1;47(5):e70509. doi: 10.1002/hbm.70509.

ABSTRACT

While resting-state brain dysfunctions have been extensively investigated in Alzheimer's disease (AD), the dynamic alterations of functional systems remain poorly understood. We employed co-activation pattern (CAP) analysis to characterize the functional-state alterations in 243 participants using resting-state fMRI data and applied graph theory analysis to estimate corresponding topological properties. The CAP analysis identified five distinct brain states across groups: State 1 (limbic network dominated), State 2 (dorsal attention network (DAN) and central executive network dominated), State 3 (default mode network and central executive network dominated), State 4 (somatomotor network and ventral attention network dominated), and State 5 (DAN, sensorimotor, and visual networks dominated). Compared to cognitively unimpaired individuals, State 3 demonstrated significantly reduced persistence and resilience in both mild cognitive impairment (MCI) and AD groups. Additionally, both clinical groups (MCI and AD) exhibited decreased transitions from State 2 to State 5 and reduced self-transitions within State 3. Graph theory analysis revealed that compared to cognitively unimpaired individuals, MCI and AD individuals had increased node degree centrality and node efficiency, alongside decreased node local efficiency in regions within the default mode network (DAN) and visual network, which corresponded well with CAP analysis results. Our findings provide a multiscale framework linking dynamic state instability to static network reorganization, advancing understanding of the dynamic functional alterations underlying cognitive decline in AD spectrum disorders.

PMID:41889068 | DOI:10.1002/hbm.70509

Cogn Affect Behav Neurosci. 2026 Mar 26. doi: 10.3758/s13415-026-01422-4. Online ahead of print.

ABSTRACT

Adaptive decision-making under risk requires individuals to flexibly adjust their risk-taking behavior after losses. Such post-loss risk adjustment is shaped by self-control (SC) and achievement motivation (AM), yet whether and how these two traits interact to influence this adjustment remains unknown, as well as its underlying neural correlates. To address this question, we recruited two independent samples (discovery: N1 = 425; replication: N2 = 182) to ask them undergo the resting-state fMRI scanning, finish the psychological measurements, and Balloon Analogue Risk Task (BART) used to measure post-loss risk adjustment (Kloss). The behavior results showed a significant interaction effect between SC and AM: higher SC predicted greater post-loss risk reduction (lower Kloss), but only among individuals with low AM, indicating that achievement motivation moderates how self-control shapes adaptive reactions to loss. At the neural level, converging voxel-based morphometry and rsFC results revealed an interaction effect between SC and AM in the left putamen along with functional connectivity (FC) between the left putamen and the anterior cingulate cortex (ACC). Moreover, a moderated mediation analysis demonstrated that the left putamen-ACC FC mediated the relationship between SC and Kloss exclusively in low-AM individuals. These effects replicated in the independent sample, underscoring the robustness of the behavior results and the identified function pathway. Collectively, these findings highlight a putamen-ACC circuit through which self-control and achievement motivation jointly influence adaptive post-loss risk adjustment. The results provide novel evidence that SC and AM shape risk-taking behavior through through mechanisms associated with monitoring and processing of conflict signals.

PMID:41888330 | DOI:10.3758/s13415-026-01422-4

Brain Topogr. 2026 Mar 27;39(3):38. doi: 10.1007/s10548-026-01187-6.

ABSTRACT

Mild cognitive impairment (MCI) is regarded a potential early stage of Alzheimer's disease (AD) and associated with a significantly increased risk of progression to AD. This study aims to evaluate whole-brain static functional connectivity (SFC) disruptions with the default mode network (DMN) seed points in patients with MCI by resting-state functional magnetic resonance imaging (rs-fMRI), and to explore whether these disruptions could serve as potential markers for MCI progression to AD. Retrospective rs-fMRI data with MCI (n = 36) and corresponding matched healthy controls (HCs) (n = 26) were collected for comparison. Independent component analysis (ICA) was used to extract DMN regions, and SFC was calculated for four seed points within the DMN. Two-sample t-tests were performed to compare group differences in SFC strength between the MCI and HC groups, and Pearson correlation analyses were conducted. Compared to HCs, the MCI group showed both increased and decreased SFC between four subregions and multiple brain regions, decreased SFC was more widely distributed than increased SFC. Abnormal connectivity was more prominent in the first two key nodes compared to the latter two. Affected regions primarily located in the precuneus, frontal gyri, temporal gyri, postcentral gyrus, caudate nucleus, lingual gyrus, and fusiform gyrus. The SFC value between the right angular gyrus and the right insula was significantly negatively correlated with MoCA scores (r = - 0.385, p < 0.05, FDR-corrected). It reveals a decline in the functional integration capacity within the DMN, as well as complex reorganization and abnormal connectivity patterns between the DMN and other brain networks. The altered interactions between DMN subregions and abnormal brain areas are significantly associated with episodic memory disturbance in MCI.

PMID:41888327 | DOI:10.1007/s10548-026-01187-6

Sci Rep. 2026 Mar 26;16(1):9978. doi: 10.1038/s41598-026-39424-4.

NO ABSTRACT

PMID:41888182 | DOI:10.1038/s41598-026-39424-4

Brain Imaging Behav. 2026 Mar 26;20(2):61. doi: 10.1007/s11682-026-01124-y.

ABSTRACT

Entropy-based analysis is increasingly used in task-based functional magnetic resonance imaging (fMRI) to quantify neural signal complexity and information dynamics, but variation in entropy definitions, parameter choices, and analytic scope can limit cross-study comparability. To systematically review how entropy measures are implemented, parameterized, and interpreted in task-based fMRI studies in healthy human subjects, focusing on methodological practice. Web of Science was searched using the keywords “fMRI” and “entropy” for the period 2000–2023, restricted to journal articles, proceedings papers, review articles, meeting abstracts, and book chapters. Included studies used task-based fMRI, applied entropy-based quantitative measures, involved healthy human participants, and reported original empirical findings or methodological applications. Non-human, clinical, and resting-state studies were excluded. Records were screened by verifying whether “fMRI” and “entropy” appeared in the title, keywords, Keywords Plus, or abstract. Extracted items included entropy type, analytic scope (regional/voxel-wise, network-level, connectivity-based), parameter and reporting details, task types, and preprocessing context where available. Data were synthesized using structured narrative methods because meta-analysis was not appropriate given differences in entropy definitions, parameterization, task types, and outcome metrics. Risk of bias was assessed with an adapted Joanna Briggs Institute (JBI) checklist (Joanna Briggs Institute, 2017). Database searches yielded 1,313 records. 274 were screened and 234 full texts assessed. 92 studies met inclusion criteria. Exclusions at full-text were primarily resting-state studies (n = 81), clinical populations (n = 42), and non-human studies (n = 19). Across the 92 included studies, Shannon entropy predominated (78.3%), followed by sample entropy (9.78%), transfer entropy (4.35%), multiscale entropy (3.26%), approximate entropy (3.26%), and multiple-entropy approaches (1.09%). Entropy measures were found to be matched with distinct methodological roles. Approximate and sample entropy were commonly used for regional or voxel-wise signal regularity, multiscale entropy for multi–time scale complexity (often at the network level), transfer entropy for directed connectivity, and Shannon entropy for broad applications including machine-learning feature and validation use. Evidence synthesis was constrained by inconsistency in entropy formulations, parameter reporting, preprocessing decisions, and outcome metrics. Formal heterogeneity testing, subgroup analyses, and sensitivity analyses were not conducted, and results were summarized descriptively. Task-based fMRI entropy research is methodologically diverse but consistently demonstrates the feasibility of using entropy to characterize task-related brain complexity across different analytic levels. The prevalent use of Shannon entropy and inconsistent parameter/reporting practices underscore the need for clearer, standardized reporting and reproducible implementation guidance to improve comparability across studies.

PMID:41886027 | PMC:PMC13021814 | DOI:10.1007/s11682-026-01124-y

Mov Disord. 2026 Mar 26. doi: 10.1002/mds.70268. Online ahead of print.

ABSTRACT

BACKGROUND: Parkinson's disease (PD) is a progressive neurodegenerative disorder clinically defined by three cardinal motor symptoms: bradykinesia, rigidity, and tremor. Although the natural history of bradykinesia and rigidity is well described, the evolution of tremor as the disease progresses remains controversial.

OBJECTIVES: The goal was to clinically characterize long-term trajectories of rest, postural, and action tremor in PD and examine their neural network correlates using longitudinal resting-state functional magnetic resonance imaging (rs-fMRI).

METHODS: In this retrospective longitudinal cohort study, we analyzed 93 tremor-positive PD patients (mean disease duration: 3.6 years), each with up to six clinical assessments over 4.2 years. Linear mixed-effects models assessed temporal change in Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS) and Fahn-Tolosa-Marín Tremor Rating Scale (FTM-TRS) scores. Rs-fMRI data from 30 tremor-affected patients (mean interval, 3.35 years) were analyzed for seed-to-whole-brain connectivity and connectivity between predefined brain regions.

RESULTS: Bradykinesia (β = +0.09, P < 0.001) and rigidity (β = +0.06, P < 0.001) worsened, whereas total FTM-TRS scores declined by 0.49 points/year (P = 0.037), driven by reductions in postural (P < 0.001) and action tremor (P = 0.033); resting tremor remained stable. Rs-fMRI revealed longitudinal changes in cerebellar- and thalamic-seed-to-whole-brain connectivity.

CONCLUSIONS: Tremor in PD evolves along distinct clinical courses, often stabilizing or improving as other motor features worsen. These findings are consistent with partially adaptive reorganization within tremor-related networks and underscore the importance of identifying patient subtypes with divergent trajectories to inform prognosis and optimize therapy. © 2026 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

PMID:41885037 | DOI:10.1002/mds.70268

Autism Res. 2026 Mar 26:e70208. doi: 10.1002/aur.70208. Online ahead of print.

ABSTRACT

Autism spectrum disorder (ASD) is a lifelong neurodevelopmental disorder. Preliminary evidence suggests an increased risk for early-onset cognitive and neurological decline in ASD. While brain development in children, adolescents, and young adults with ASD diverges from neurotypical (NT) peers, it remains unclear in older adults with ASD. Understanding age-related changes of brain function in ASD is crucial to establish best practices for cognitive and health screenings and develop interventions that might reduce the risk of accelerated decline. Decreases in blood-oxygenation-level-dependent (BOLD) signal variability (BSV) in typical aging have been shown across multiple studies and are associated with poorer cognitive performance. We hypothesized that adults with ASD would show reduced BSV compared to the NT group, with steeper negative age associations in the ASD than NT group. The study assessed BSV during resting state fMRI in adults (40-70 years), 28 with ASD and 39 age-matched NT. General linear models tested diagnostic group, age, and group-by-age interactions, controlling for motion. Significant group-by-age interactions were observed for the right insular, left temporal occipital fusiform, right frontal orbital, and right inferior lateral occipital cortex, with BSV showing strong negative associations with age in the ASD but not NT adults. These findings suggest that BSV decreases may occur earlier in adults with ASD compared to their NT peers. This would be consistent with accelerated aging; however, additional longitudinal analyses are necessary to determine if the results presented truly reflect accelerated aging or arise from lifelong persistent differences in brain function.

PMID:41885009 | DOI:10.1002/aur.70208

Sci Data. 2026 Mar 25. doi: 10.1038/s41597-026-07081-x. Online ahead of print.

ABSTRACT

The Quantitative Neuroimaging Laboratory Dataset provides magnetic resonance imaging (MRI) modalities and two resting-state and twelve task-based functional MRI (fMRI) tapping into four cognitive domains (episodic memory, fluid reasoning, processing speed, and crystallized memory). It also includes three positron emission tomography (PET) scans ([18 F]Fluorodeoxyglucose (FDG), Florbetaben, and MK-6240), plus neuropsychological assessments, and vital signs. Currently, 356 participants consented (97 young: 20 ~ 40 years; and 259 elderly: 60 ~ 80 years), while 259 completed at least one scan. We uploaded 4688 MRI/fMRI and 719 PET scans (232 Florbetaben, 251 FDG, and 236 MK-6240). 189 participants completed all scan modalities. All imaging underwent an in-house, pre-processing pipeline developed for each modality. This dataset aims to characterize the spatial and temporal properties of the brain's hemodynamic response in the opposite direction (i.e., brain deactivation), its task dependency, and its interaction with the brain's large-scale functional connectivity networks. Ultimately, this will enable the translation of neuroimaging findings into personalized medicine approaches that better characterize and predict individual pathologies in neuropsychiatric diseases.

PMID:41882039 | DOI:10.1038/s41597-026-07081-x

Addict Biol. 2026 Apr;31(4):e70145. doi: 10.1111/adb.70145.

ABSTRACT

Withdrawal or the adverse response to abstinence is a significant marker of addiction; however, the neural features of internet gaming disorder (IGD), especially the effects of sex under abstinence, have rarely been examined. This study aimed to examine brain reactions in IGD patients after short-term abstinence and the differences between the sexes. Thirty males and 30 females with IGDs and 30 males and 30 females recreational game users (RGUs) were recruited. Resting-state fMRI data were collected after 1.5 h without gaming. In the IGD and RGU groups, we found atypical brain areas with concurrent degree centrality (DC) and regional homogeneity (ReHo) changes. We then performed functional connectivity (FC) analysis and two-factor ANOVA on these regions to compare IGD and RGU and test for sex differences. Compared with RGUs, IGD subjects presented abnormal cerebral areas with concurrent DC and ReHo abnormalities. After short-term abstinence, IGD and RGU patients presented abnormal prefrontal lobe and insula FC values. Subsequent sex difference analyses focused on the superior frontal gyrus (SFG), middle frontal gyrus (MFG), inferior frontal gyrus (IFG) and insula. ANOVA followed by FDR-corrected post hoc comparisons revealed that IGD males exhibited significantly greater prefrontal and insula FC than females after short-term abstinence. Specifically, males showed markedly enhanced FC in multiple prefrontal regions and the insula, with effect sizes (Cohen's d) ranging from medium to large, confirming both the efficacy and reliability of the observed differences. Compared with RGUs, IGD patients presented FC changes in executive control and reward processing brain regions. With respect to sex differences, short-term abstinence may have altered cognitive control functions more in males than in females and increased internet gaming severity in males. These findings suggest that males are more susceptible to IGD.

PMID:41881684 | DOI:10.1111/adb.70145

J Affect Disord. 2026 Mar 23:121670. doi: 10.1016/j.jad.2026.121670. Online ahead of print.

ABSTRACT

BACKGROUND: Adolescents with major depressive disorder (MDD) and bipolar disorder (BD) share substantial clinical overlap and elevated suicide risk, yet the neurobiological distinctions between these disorders and their associations with suicidality remain incompletely understood. This study investigated functional connectome differences between adolescent MDD and BD and examined associations with suicide attempts (SA).

METHODS: Resting-state fMRI data were acquired from 125 adolescents aged 12-19 years (48 MDD, 36 BD, 41 healthy controls). We used graph-theoretical analysis to investigate group differences in functional brain networks, and machine learning models were applied to functional network data to distinguish between MDD and BD.

RESULTS: Compared with MDD and healthy controls, adolescents with BD exhibited a global shift toward network randomization, characterized by a lower clustering coefficient and widespread reductions in nodal centrality across hubs of the DMN, SN, and CEN. In contrast, MDD was characterized by preserved global topology but focal nodal alterations. Within the MDD group, greater suicidal-ideation severity was associated with lower nodal efficiency in the insula and supramarginal gyrus. A support vector machine classifier distinguished MDD from BD with 88.24% (p < 0.001) accuracy, with features from the insula and cingulate gyrus being highly informative.

CONCLUSIONS: Adolescent MDD and BD showed distinct patterns of functional network disruption, with BD showing global network disorganization and MDD showing more localized disruptions. Alterations involving the insula and supramarginal gyrus may be relevant to suicidality in adolescent MDD, and network-based features may aid in distinguishing MDD from BD.

PMID:41881121 | DOI:10.1016/j.jad.2026.121670

J Affect Disord. 2026 Mar 23:121665. doi: 10.1016/j.jad.2026.121665. Online ahead of print.

ABSTRACT

BACKGROUND: Emerging evidence has highlighted the cerebellum's role in emotion and cognition through cerebello-cerebral interactions. However, the manner in which the cerebellum integrates with the cerebral triple core networks, the default mode network (DMN), central executive network (CEN), and salience network (SN), in Major Depressive Disorder (MDD) remains unclear.

METHODS: Resting-state functional magnetic resonance imaging (rs-fMRI) data were acquired from 119 patients with MDD and 106 healthy control (HC) subjects. Voxel-wise functional connectivity (FC) between the cerebral cortex and cerebellum was subsequently constructed. To evaluate cerebello-cerebral functional integration based on voxel-wise FC, functional gradient analysis and independent component analysis (ICA) were performed.

RESULTS: The cerebral triple core network components were found to map onto cerebellar motor and cognitive functional modules. In patients with MDD, reduced mapping of the cerebral DMN and SN components to the cerebellum was observed. Additionally, patients exhibited compression of the cerebello-cerebral functional gradient within both motor and cognitive modules. The triple core networks showed increased contributions to cerebellar cognitive modules, whereas the DMN demonstrated decreased contributions to cerebellar motor modules in MDD. These cerebello-cerebral interaction patterns were significantly correlated with clinical assessment measures, including scores on the Trail Making Test (TMT) and the Emotion Regulation Questionnaire (ERQ).

CONCLUSIONS: These findings indicate disrupted functional integration between the cerebral triple core networks and the cerebellum in MDD. The results further support the cerebellum's involvement in disease pathogenesis and suggest potential neurobiological markers for diagnosis and intervention.

PMID:41881114 | DOI:10.1016/j.jad.2026.121665