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bioRxiv [Preprint]. 2026 Feb 19:2026.02.18.706630. doi: 10.64898/2026.02.18.706630.

ABSTRACT

The early diagnosis of Alzheimer's disease (AD), a cause of progressive cognitive decline, remains challenging. Recent information-theoretic advances allow brain dynamics to be quantified in terms of how regions share and combine information. Integrated Information Decomposition (ΦID) separates redundant (the same content present in multiple regions) from synergistic information (new content that emerges only when regions are considered together). Such information-dynamic measures may provide biomarkers relevant to AD risk and progression. Here we applied integrated information decomposition (ΦID) to resting-state fMRI from the Alzheimer's Disease Neuroimaging Initiative (ADNI), to test whether ΦID measures are diagnostically sensitive and track cognition along the AD spectrum. For each region, we computed total synergy and redundancy and compared values across cognitively normal (CN), mild cognitive impairment (MCI), and AD groups. Compared to CN, AD patients showed a striking synergy reduction across the entire brain, in concert with widespread redundancy increases, particularly in the executive and default mode networks. Transitions from CN to AD included an intermediate MCI decrease in redundancy, possibly reflecting early disease compensation strategies. This AD informational shift from complex higher level information processing to more robust inefficient forms likely reflects a cognitive shift to simpler, less integrative cognitive processes. Indeed, when re-analysing the data according to a standard cognitive clinical test (the Montreal Cognitive Assessment), we found a synergy-redundancy shift in high versus low performers broadly very similar to the CN to AD shift. AD shows a clear information-processing signature: reduced global synergy and increased redundancy, especially in the executive control network. These striking results provide powerful insights into the widespread information processing reconfiguration that occurs in AD, with clear changes already emerging at the earlier MCI stage. Further, these results provide a novel route to support early diagnosis and stratification.

PMID:41757079 | PMC:PMC12934565 | DOI:10.64898/2026.02.18.706630

Front Psychiatry. 2026 Feb 11;17:1765885. doi: 10.3389/fpsyt.2026.1765885. eCollection 2026.

ABSTRACT

BACKGROUND: Chronic insomnia disorder (CID) and depression exhibit high comorbidity, yet the underlying neurobiological mechanisms remain poorly understood. Neuroimaging meta-analyses suggest the subgenual anterior cingulate cortex (sgACC) is a key node, but the characteristics of its network connectivity in CID patients with depressive symptoms (CID-D) are unclear.

METHODS: This study enrolled 197 participants: 66 CID patients without depression (CID-nD), 67 CID-D patients, and 64 good sleep controls (GSC). Using resting-state functional magnetic resonance imaging (fMRI), we compared sgACC-based functional connectivity (FC) across groups. We also examined correlations between altered FC and clinical symptoms, and investigated whether altered sgACC FC mediated the relationship between insomnia severity and depressive symptoms.

RESULTS: Significant group differences in sgACC FC were found in the left inferior temporal gyrus (ITG), inferior frontal gyrus (IFGtri), right supplementary motor area (SMA), postcentral gyrus (POCG), and medial superior frontal gyrus (SFGmed). Specifically, compared to CID-nD, CID-D patients showed increased FC with ITG.L and IFGtri.L, and decreased FC with SMA.R and POCG.R. FC between sgACC and ITG.L or IFGtri.L was positively correlated with depressive symptoms, while sgACC-POCG.R FC was negatively correlated. Mediation analysis revealed that sgACC-ITG.L FC partially mediated the link between insomnia and depressive symptoms.

CONCLUSION: Our findings identify specific alterations in sgACC functional network in CID patients with comorbid depression. The mediating role of sgACC-ITG.L connectivity highlights a potential neural pathway through which insomnia contributes to depressive symptoms, identifying a putative target for neuromodulation therapies.

PMID:41756572 | PMC:PMC12932571 | DOI:10.3389/fpsyt.2026.1765885

Entropy (Basel). 2026 Feb 9;28(2):191. doi: 10.3390/e28020191.

ABSTRACT

Major depressive disorder (MDD) represents a heterogeneous condition lacking reliable neurobiological biomarkers and a mechanistic understanding. Time-resolved characterization of brain dynamics reveals that mental health is associated with a characteristic dynamical regime, exhibiting spontaneous switching between a repertoire of ghost attractor states forming resting-state networks. Analysing resting-state fMRI data from 848 patients with MDD and 794 healthy controls across 17 sites in China (REST-meta-MDD) using Leading Eigenvector Dynamics Analysis (LEiDA), we found patients with MDD exhibited significantly reduced default mode network (DMN) occupancy (p < 0.001; Hedges' g = -0.51) and increased occipito-parieto-temporal state occupancy (p < 0.001; Hedges' g = 0.42), suggesting compensatory dynamical rebalancing. These findings extend prior observations of DMN disruption in MDD, aligning with the emerging dynamical systems framework for mental health to advance the mechanistic understanding of MDD pathophysiology.

PMID:41751694 | PMC:PMC12939193 | DOI:10.3390/e28020191

Biomedicines. 2026 Jan 31;14(2):333. doi: 10.3390/biomedicines14020333.

ABSTRACT

Functional magnetic resonance imaging (fMRI) is a valuable tool for presurgical brain mapping, traditionally implemented with task-based paradigms (tb-fMRI) that measure blood oxygenation level-dependent (BOLD) signal changes during controlled motor or cognitive tasks. Tb-fMRI is a well-established tool for non-invasive localization of cortical eloquent areas, yet its dependence on patient cooperation and intact cognition limits use in individuals with aphasia, cognitive impairment, or in pediatric and other vulnerable populations. Resting-state fMRI (rs-fMRI) provides a task-free alternative by leveraging spontaneous low-frequency BOLD fluctuations to delineate intrinsic functional networks, including motor and language systems that show good spatial concordance with tb-fMRI and with direct cortical stimulation. This narrative review outlines the methodological foundations of tb-fMRI and rs-fMRI, comparing acquisition protocols, preprocessing and denoising pipelines, analytic approaches, and validation strategies relevant to presurgical planning. Particular emphasis is given to the technical and physiological foundations of BOLD imaging, statistical modeling, and the influence of motion, noise, and standardization on data reliability. Emerging evidence indicates that rs-fMRI can reliably expand mapping to patients with limited task compliance and may serve as a robust complementary modality in complex clinical contexts, though its methodological heterogeneity and absence of unified practice guidelines currently constrain widespread adoption. Future advances in harmonized preprocessing, multicenter validation, and integration with connectomics and machine learning frameworks are likely to be critical for translating rs-fMRI into routine, reliable presurgical workflows.

PMID:41751232 | DOI:10.3390/biomedicines14020333

Brain Sci. 2026 Feb 10;16(2):208. doi: 10.3390/brainsci16020208.

ABSTRACT

Background: Resting-state functional MRI (rs-fMRI) studies typically rely on linear Pearson correlation to characterize brain connectivity, potentially overlooking the distributional characteristics of functional networks. This study introduces a kernel-transformed functional connectivity (FC) entropy framework to quantify network dedifferentiation in bipolar disorder (BD). Methods: We utilized a Gaussian kernel function to execute a nonlinear similarity transformation (referred to as reweighting) on standard linear correlation matrices. This approach acts as a functional filter to amplify the contrast between strong and weak connections. Multiscale entropy (global, modular, and nodal) was subsequently calculated to characterize the uniformity of connectivity weight distributions. Results: Compared to Normal Controls (NCs), patients with BD exhibited significantly higher entropy at the global level and within the Default Mode, Salience, and Somatosensory-Motor networks, indicating widespread network dedifferentiation (distributional flattening). These alterations were robust across different kernel widths and remained significant after rigorously controlling for head motion (Mean FD). Furthermore, manic symptom severity (YMRS) was negatively correlated with global entropy, suggesting a pathological "locking-in" or rigidity of specific neural circuits during manic states. Conclusions: The kernel-transformed FC entropy serves as a distribution-sensitive complement to conventional linear metrics. Our findings highlight network dedifferentiation as a key pathophysiological feature of BD and suggest this framework as a promising candidate metric for characterizing network dysregulation.

PMID:41750208 | DOI:10.3390/brainsci16020208

Brain Sci. 2026 Jan 31;16(2):181. doi: 10.3390/brainsci16020181.

ABSTRACT

Background/Objectives: Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition characterized by differences in social communications and restricted, repetitive patterns of behaviors and interests, affecting approximately 1% of children globally. While functional magnetic resonance imaging (fMRI) has provided insights into altered brain connectivity patterns in ASD, classification based on neuroimaging remains a challenging due to the heterogeneity of the disorder and variability in imaging data across sites. This study employs a network-based approach using large-scale, multi-site rs-fMRI dataset from the Autism Brain Imaging Data Exchange (ABIDE I and II) to classify ASD and healthy controls using machine learning. Methods: A semi-blind Independent Component Analysis method, specifically the spatial constraint reference ICA, is applied to identify functional brain networks, and the ComBat harmonization technique is used to address site-specific variability across 11 independent datasets, ensuring consistency in feature representation. Support Vector Machines (SVMs) are employed for classification, focusing on three key networks: the Default Mode Network (DMN), Sensorimotor Network (SMN), and Visual Sensory Network (VSN). Results: The results demonstrate high classification accuracy, with the VSN achieving the highest performance (83.23% accuracy, 87.90% AUC), followed by the DMN (81.43% accuracy, 84.53% AUC) and the SMN (80.52% accuracy, 84.96% AUC), positioned with their recognized roles in social cognition and sensory-motor processing, respectively. Conclusions: The integration of ICA-based feature extraction with ComBat harmonization significantly improved classification accuracy compared to previous studies. These findings point out the potential of network-based approaches in ASD classification and point out the importance of integrating multi-site neuroimaging data for identifying reproduceable network-level features.

PMID:41750182 | DOI:10.3390/brainsci16020181

Hum Brain Mapp. 2026 Mar;47(4):e70485. doi: 10.1002/hbm.70485.

ABSTRACT

The brain's rich club is a network of particularly densely interconnected regions, metabolically costly to maintain but central to the balance between functional segregation and integration. We assessed whether the rich club can accordingly be described as a control center of the brain, and present a systematic analysis of its involvement in maintenance of and traversal between various cognitively relevant functional states. Brain states were defined based on fMRI task-evoked and resting-state patterns of activity as provided by the Human Connectome Project (HCP). Using tools from network control theory (NCT), we computed the necessary effort needed for control of dynamics when the rich club, versus a size-matched set of low-degree peripheral regions, was prohibited from exerting control over dynamics. Control energy needed to traverse functional states was significantly higher, and stability of states significantly lower, when the set of peripheral regions was prohibited from control. Findings were stable across various rich-club and null model definitions and across different parameter settings. A region's contribution to optimal control processes was instead associated with its affiliation with certain intrinsic connectivity networks and its position on the visual-sensorimotor, but not sensory-transmodal cortical gradient. We accordingly report that the rich club was systematically less involved in control of dynamics than the size-matched set of peripheral regions. These results do not negate an integratory role of the rich club, but question its proposed role as a driver of control. Indeed, if it would inhabit such a role, we would have expected opposite results. Our findings fit with a position describing the rich club as a passive "data-highway" which, by means of its high connectivity, can be easily controlled by peripheral regions and thus facilitate relevant communication channels between them. We call for methodological expansions of the control theoretical toolbox allowing for elaborations on the temporal dynamics of control processes.

PMID:41749476 | DOI:10.1002/hbm.70485

Sci Rep. 2026 Feb 26. doi: 10.1038/s41598-026-40813-y. Online ahead of print.

ABSTRACT

Brain functional development from birth to adolescence follows the cortical gradient from primary sensorimotor to higher-order association regions. Precuneus (PCun) is crucial in spatial cognition, visual-motor integration, and social cognition. However, functional connectivity changes of PCun subregions in this dynamic developmental period are not known. Multimodal cross-sectional diffusion MRI and resting-state fMRI of subjects from birth to early adolescence were acquired to obtain structural and functional connectivity. PCun in neonates, 1-year-olds, 2-year-olds, and early adolescent subjects were consistently parcellated into four subregions based on structural connectivity of PCun. Significant developmental changes were found in functional connectivity between the parcellated PCun subregions and default mode network (DMN), and between the parcellated PCun subregions and cerebellum network. To understand altered development of PCun in brain disorders, connectivity-based parcellation was performed in the subjects with autism spectrum disorder (ASD). Similar parcellation pattern of PCun was found, but the relative volume of the dorsal-posterior subregion significantly decreased in the subjects with ASD compared to typically developmental subjects. These findings revealed functional developmental patterns of PCun subregions in their connected networks in typical developing brains and revealed PCun subregion alteration in ASD, shedding light onto functional changes of PCun architecture during development.

PMID:41748807 | DOI:10.1038/s41598-026-40813-y

NPJ Parkinsons Dis. 2026 Feb 26. doi: 10.1038/s41531-026-01294-y. Online ahead of print.

ABSTRACT

Impulse control disorders (ICDs) affect up to 45% of Parkinson's disease (PD) patients, yet their neural mechanisms remain unclear. Using multimodal PET and resting-state fMRI in 23 PD patients (11 PDICD + , 12 PD-ICD-) and 14 healthy controls, we identified specific brain pathways underlying ICDs. PDICD+ patients showed steeper delay discounting and altered functional connectivity, including enhanced posterior parietal coupling within executive networks and disrupted salience-executive interactions. Critically, aberrant right supplementary motor area-amygdala connectivity was linked to ICD severity and decisional impulsivity. Path analysis revealed that increased SMA 5-HT₂ₐ receptor availability was associated with enhanced SMA-amygdala coupling, which in turn was positively associated with ICD symptoms. By linking serotonergic dysfunction to disrupted motor-limbic networks and impulsive behavior, this study identifies targetable pathways for managing a common non-motor complication of PD.

PMID:41748653 | DOI:10.1038/s41531-026-01294-y

Brain Res Bull. 2026 Feb 24:111787. doi: 10.1016/j.brainresbull.2026.111787. Online ahead of print.

ABSTRACT

BACKGROUND: Post-stroke cognitive impairment (PSCI) is associated with abnormal dynamic functional connectivity, yet the temporal dynamic of brain activity and their underlying molecular mechanisms remain unclear.

METHODS: Participants were classified into two groups based on neuropsychological assessments: PSCI group (N=67) and post-stroke with no cognitive impairment (NPSCI) group (N=65), alongside 47 healthy controls (HCs). Dynamic brain states were analyzed using a Hidden Markov Model (HMM) with the Brainnetome Atlas, yielding metrics like fractional occupancy (FO), mean dwell time (MDT), switching rate (SR) and transition probabilities (TP) based on resting-state functional magnetic resonance imaging (rs-fMRI). Finally, we further assessed the spatial correlations between the mean activation of HMM state and neurotransmitter receptors/transporters distribution, cognitive relative term, and gene expression profiles.

RESULTS: Five HMM states were identified. Compared with HCs and NPSCI group, patients with PSCI group exhibited different dynamics, including FO, MDT, SR, and TP. Additionally, we found that the mean activation maps of HMM state were associated with the neurotransmitter receptors/transporters distribution and cognitive relative term. Furthermore, our results demonstrated a spatial correlation between the mean activation maps of state 5 and gene expression patterns. Finally, enrichment analysis indicated that PLS-positive genes were enriched in pathways related to DNA/RNA metabolism, signal transduction and regulation, and immune-disease associations, whereas, PLS-negative genes were mainly enriched in lipid metabolism and insulin response, virus-cytokine interactions, and influenza response pathways.

CONCLUSIONS: This study provides new insights into characterizing dynamic neural activity in PSCI. The brain network dynamics defined by HMM analysis may deepen our understanding of the neurobiological underpinnings of PSCI, indicating a linkage between neural configuration and gene expression in PSCI.

PMID:41747873 | DOI:10.1016/j.brainresbull.2026.111787

Prog Neuropsychopharmacol Biol Psychiatry. 2026 Feb 24:111653. doi: 10.1016/j.pnpbp.2026.111653. Online ahead of print.

ABSTRACT

BACKGROUND: Food addiction (FA) affects a significant proportion of the general population and could contribute to excess weight and its related complications. This phenomenon has been well described in terms of behavior, but little is known about its neurological determinants. The primary aim of this systematic review is to identify the neuroimaging characteristics associated with FA, using the Yale Food Addiction Scale (YFAS) as a validated assessment tool.

METHODS: A systematic search was conducted in PubMed and ScienceDirect databases from 2009 to July 2024 in accordance with the PRISMA 2020 guidelines. Studies were included if they investigated associations between the YFAS and neuroimaging outcomes. A descriptive analysis was conducted due to the methodological heterogeneity between the included articles.

RESULTS: Of the 528 records identified, 25 studies were included in the review, representing 3081 participants in total. Functional magnetic resonance imaging (fMRI, n = 18) and structural MRI (n = 9), were the most commonly used imaging techniques. Studies reported associations between YFAS scores and altered resting-state functional connectivity or brain responses to cognitive tasks, especially in caudate, putamen, amygdala, insula, nucleus accumbens, orbitofrontal cortex, thalamus and precuneus. Yet, numerous neuroimaging findings related to FA presented discrepancies across studies.

DISCUSSION: There is some evidence of altered activation and functional connectivity in brain areas involved in reward and cognitive control among individuals with FA. However, neuroimaging outcomes related to FA remain highly inconsistent across studies, partly due to heterogenous methodologies. Methodological recommendations are provided to improve consistency of future neuroimaging research in the context of FA.

PMID:41747855 | DOI:10.1016/j.pnpbp.2026.111653

Front Comput Neurosci. 2026 Feb 10;20:1731868. doi: 10.3389/fncom.2026.1731868. eCollection 2026.

ABSTRACT

Understanding the neural mechanisms underlying the transitions between different states of consciousness is a fundamental challenge in neuroscience. Thus, we investigate the underlying drivers of changes during the resting-state dynamics of the human brain, as captured by functional magnetic resonance imaging (fMRI) across varying levels of consciousness (awake, light sedation, deep sedation, and recovery). We deploy a model-based approach relying on linear time-invariant (LTI) dynamical systems under unknown inputs (UI). Our findings reveal distinct changes in the spectral profile of brain dynamics-particularly regarding the stability and frequency of the system's oscillatory modes during transitions between consciousness states. These models further enable us to identify external drivers influencing large-scale brain activity during naturalistic auditory stimulation. Our findings suggest that these identified inputs delineate how stimulus-induced co-activity propagation differs across consciousness states. Notably, our approach showcases the effectiveness of LTI models under UI in capturing large-scale brain dynamic changes and drivers in complex paradigms, such as naturalistic stimulation, which are not conducive to conventional general linear model analysis. Importantly, our findings shed light on how brain-wide dynamics and drivers evolve as the brain transitions toward conscious states, holding promise for developing more accurate biomarkers of consciousness recovery in disorders of consciousness.

PMID:41743844 | PMC:PMC12929524 | DOI:10.3389/fncom.2026.1731868

Front Pediatr. 2026 Feb 10;14:1711271. doi: 10.3389/fped.2026.1711271. eCollection 2026.

ABSTRACT

BACKGROUND: Motor coordination impairment among children with autism spectrum disorder (ASD) has recently gained increasing attention. However, the relationship between functional connectivity (FC) alterations and motor coordination impairment among ASD remains inconclusive.

METHODS: We evaluated motor coordination function using the Developmental Coordination Disorder Questionnaire (DCDQ) and acquired resting-state functional magnetic resonance imaging (rs-fMRI) scans from 23 autistic individuals and 25 typically developing (TD) controls (6-10 years old). Within- and between-network FC was estimated using group independent component analysis (ICA) and group comparison was addressed using two-sample t-tests. Relationships between abnormal FC and motor coordination among ASD were investigated with multiple linear regression, with age, gender, and intelligence quotient (IQ) considered as covariates.

RESULTS: In the ASD group, 1) FC within the right cerebellar crus II was negatively correlated to the score of general coordination (β = -.566, p = 0.035) and control during movement (β = -0.529, p = 0.026); 2) FC between the cerebellar network and frontal-temporal-parietal network was negatively correlated to the score of general coordination (β = -2.610, p = 0.006); 3) Increased FC between the cerebellar network and insular network was associated with a higher score of fine motor/handwriting (β = -0.529, p = 0.026).

CONCLUSIONS: We confirmed the role of the insular network in interoception and motor processing among ASD, which was related to impaired information integrating, relaying, and visual feedback during movement. A significant relationship between the cerebellar network and frontal-temporal-parietal network in motor coordination indicated that a deficit in the planning of movements may contribute to atypical motor skills. The study gained an understanding of neuroimaging traits among ASD children and may provide evidence for the design of the motor-related intervention.

PMID:41743223 | PMC:PMC12930269 | DOI:10.3389/fped.2026.1711271

Neuropsychopharmacology. 2026 Feb 25. doi: 10.1038/s41386-026-02375-5. Online ahead of print.

ABSTRACT

Predicting selective serotonin reuptake inhibitor (SSRI) response in obsessive-compulsive disorder (OCD) remains a clinical challenge. Converging evidence implicated that the sensorimotor circuit is linked to OCD-related sensory phenomena and repetitive motor rituals, and it is densely innervated by serotonergic projections, making it a plausible substrate of SSRI effects. We therefore hypothesized that baseline functional connectivity (FC) of this circuit could serve as a candidate neural marker of SSRI treatment response. In this exploratory single-site resting-state fMRI study, 54 drug-naïve patients with OCD and 39 matched healthy controls (HCs) underwent scanning. Patients received sertraline for 12 weeks and were classified as responders (rOCD, n = 33) or non-responders (nOCD, n = 21) based on Yale-Brown Obsessive Compulsive Scale score reductions. Seed-based FC analysis of the sensorimotor circuit was conducted across the three groups. We observed that OCD patients exhibited abnormal FC primarily within the sensorimotor circuit and in its connections with the cerebellum. The rOCD group showed generally higher FC within the sensorimotor circuit than HCs, whereas the nOCD group showed lower FC values. Cerebellar regions with altered connectivity included areas involved in sensorimotor processing and higher-level functions. In prediction analyses, the connectivity between right thalamus and cerebellar Crus I region achieved an AUC of 0.854 for distinguishing responders from non-responders under leave-one-out cross-validation. Moreover, FC-based models showed better predictive performance than clinical models. These findings suggest that baseline sensorimotor-network FC may serve as a candidate biomarker of sertraline response in OCD, pending validation in large, independent samples.

PMID:41741690 | DOI:10.1038/s41386-026-02375-5

Int J Eat Disord. 2026 Feb 25. doi: 10.1002/eat.70066. Online ahead of print.

ABSTRACT

OBJECTIVE: Numerous publications utilize data from the Adolescent Brain and Cognitive Development (ABCD) Study. This review aimed to evaluate how data from the ABCD cohort contributes to understanding the pathophysiology of incipient eating disorders.

METHOD: Searches were completed using PubMed and the ABCD Study research publications database. All available neuroimaging articles assessing prevalence and predictors of disordered eating/eating disorders were included.

RESULTS: Thirty-eight articles met inclusion criteria, 10 of which presented neuroimaging results, all analyzing baseline brain data. The majority (n = 9) assessed brain structure and function in children with binge eating (BE)/binge eating disorder (BED). Results were inconsistent across imaging modalities. Structural MRI studies included widespread increases in gray matter density and reductions in cortical thickness associated with eating pathology. Task-based fMRI studies reported conflicting findings, with frontostriatal activation during reward processing in children with BE/BED reduced, increased, or not different compared to controls. Resting-state fMRI analyses consistently identified reduced functional connectivity in key frontal circuits, although patterns differed when samples were stratified by sex or BMI. Non-imaging studies showed positive associations between eating disorders/disordered eating and several sociodemographic, cognitive, behavioral, and biological correlates.

DISCUSSION: Alterations in brain structure and function associated with binge eating are identified in neuroimaging analyses of baseline scans from the ABCD cohort, with inconsistent results. One potential pattern suggests alterations in reward system function, although the direction and exact location of such alterations are unclear. Consistency in methodological approaches is necessary to allow patterns in neural alterations to be more clearly identified. There is significant and ongoing potential for the ABCD Study dataset to quantify developmental aspects of binge eating. Recommendations for future analyses as the sample progresses through puberty and eating disorder prevalence increases are also presented.

PMID:41741359 | DOI:10.1002/eat.70066

AJNR Am J Neuroradiol. 2026 Feb 25:ajnr.A9264. doi: 10.3174/ajnr.A9264. Online ahead of print.

ABSTRACT

This case report uses magnetoencephalography (MEG), electroencephalography (EEG), diffusion kurtosis imaging (DKI), pseudo-continuous arterial spin labelling (pCASL), and resting-state functional MRI (rs-fMRI) to compare female, high-school soccer dizygotic twins who differed in recent concussion history. One twin, "Twin A", sustained her first clinically diagnosed concussion 72 hours before baseline imaging. "Twin B" was not concussed and served as a control for Twin A. Participants completed clinical, neuropsychological, and neurophysiological assessments at baseline (T1), 1 month (T2), and 3 months (T3) timepoints. Imaging and electrophysiology were acquired using a harmonized protocol across modalities. MEG was collected with a MEGIN Triux Neo whole-head system, and 64-channel EEG was acquired simultaneously. MRI was conducted on a 3T Siemens Prisma scanner following the Adolescent Brain Cognitive Development (ABCD) protocol. DKI was processed using FSL to generate fractional anisotropy and mean diffusivity maps. pCASL was analyzed using BASIL to estimate cerebral blood flow. rs-fMRI preprocessing and denoising were performed in CONN, and voxel-wise power spectral density (0.01-0.1 Hz) was computed to quantify low-frequency oscillatory activity. Twin A demonstrated acute symptoms, left frontal hypoperfusion, reduced rs-fMRI power, and increased low-frequency electrophysiological activity at T1, with gradual recovery across modalities. Twin B exhibited stable findings across all assessments. Our findings highlight the potential of multimodal brain imaging to localize sports-related concussion and to help inform return-to-play decisions.

PMID:41741217 | DOI:10.3174/ajnr.A9264

Neuroimage. 2026 Feb 23:121829. doi: 10.1016/j.neuroimage.2026.121829. Online ahead of print.

ABSTRACT

Problematic use of social media (PUSM) is a major public health concern estimated to affect 35% of adolescents. However, data-driven research to identify neural networks predictive of PUSM in adolescents remains limited. The aim of this study was to utilize connectome-based predictive modelling (CPM), a machine-learning approach that employs whole-brain functional connectivity data, to predict PUSM severity and identify underlying neural networks in adolescents. We included 2294 participants from the Adolescent Brain Cognitive Development study (Mage = 10.03, 50.6% female) who had resting-state functional magnetic resonance imaging (fMRI) data at baseline and PUSM scores at the four-year follow-up. CPM with 10-fold cross-validation was applied to resting-state fMRI data and PUSM scores. CPM successfully predicted PUSM scores and identified connectivity within and between multiple large-scale neural networks predictive of PUSM severity, which could be categorized into two key systems: (i) a cognitive control and self-regulation system consisting of the default mode, frontoparietal, and medial frontal networks, and (ii) a perceptual-motor integration system consisting of the visual area 1 and sensorimotor networks. The large-scale networks identified in the present study provide mechanistic insight into PUSM vulnerability and represent potential targets for personalized interventions. Future research should aim to replicate and extend the current results to refine prevention and treatment approaches.

PMID:41740634 | DOI:10.1016/j.neuroimage.2026.121829

Neuroimage Clin. 2026 Feb 18;49:103973. doi: 10.1016/j.nicl.2026.103973. Online ahead of print.

ABSTRACT

BACKGROUND: Patients implanted with modern deep brain stimulation (DBS) hardware can now undergo functional magnetic resonance imaging (fMRI), leading to its increased used to study DBS' mechanisms and predict optimal therapy settings. To accurately interpret fMRI data and realize its clinical potential for DBS, a better understanding of reliability is needed.

METHODS: Sixteen patients with Parkinson's disease (PD) and DBS targeting the subthalamic nucleus or pallidum underwent 3T test-retest resting-state fMRI with and without concurrent stimulation. Effects of stimulation and device-metal artifacts on reliability of fMRI brain connectivity and moment-to-moment brain variability were explored, plus factors influencing between-subject variations in reliability such as motion.

RESULTS: The brain variability fMRI metric yielded higher intra-class correlation coefficients than the connectivity metric (range across whole brain, motor, limbic, and cognitive networks: 0.36-0.85 and 0.68-0.99, respectively). Average network connectivity appeared less reproducible when DBS was ON versus OFF during fMRI, and fMRI metric reliability for brain areas affected by metal artifacts was significantly higher (brain variability) or lower (connectivity) than unaffected areas (puncorrected < 0.05). Motion and DBS target best explained between-subject variations.

CONCLUSION: DBS hardware and active stimulation may alter fMRI reliability. To develop clinically useful fMRI biomarkers for DBS and aid assessments of reproducibility across studies, the reliability of single study results need reporting.

PMID:41740214 | DOI:10.1016/j.nicl.2026.103973

Neuropsychiatr Dis Treat. 2026 Feb 18;22:566720. doi: 10.2147/NDT.S566720. eCollection 2026.

ABSTRACT

Multiple system atrophy (MSA) is a neurodegenerative disease characterized by α-synuclein pathology and pronounced clinical heterogeneity, making early diagnosis difficult. Functional magnetic resonance imaging (fMRI) has emerged as a promising tool to enhance diagnostic precision. By identifying disease- and symptom-specific network connectivity abnormalities, fMRI may reflect pathological changes in corresponding brain regions, thereby providing mechanistic insights. Recent work demonstrates that resting-state fMRI (rs-fMRI) can capture subtype-specific patterns, predominant basal ganglia-cortical disruption observed in the parkinsonian subtype of MSA (MSA-P) and cerebellar-cortical disconnection in the cerebellar subtype (MSA-C), reflecting their respective underlying pathologies of striatonigral degeneration and olivopontocerebellar atrophy. Rs-fMRI can also distinguish MSA from related parkinsonian syndromes, including Parkinson's disease (PD) and progressive supranuclear palsy (PSP), based on characteristic disruptions in cerebellar-cortical network connectivity. These patterns align with pathological features, providing important insights into disease progression. Task-based fMRI (t-fMRI), though less studied, further highlights impairments in motor network integration. Beyond diagnosis, fMRI has shown potential in evaluating treatment effects, with neuromodulatory interventions such as transcranial magnetic stimulation associated with measurable network changes. However, existing studies remain constrained by small sample sizes, single-center designs, and methodological variability. Future directions include large, multicenter trials, standardized imaging protocols, and integration with multimodal and computational approaches to establish robust fMRI-based biomarkers. Collectively, these advances position fMRI as a promising biomarker-oriented tool in MSA, supporting subtype classification, enhancing differential diagnosis from PD and PSP, elucidating symptom-specific network dysfunction, and enabling objective evaluation of therapeutic interventions in clinical and translational settings.

PMID:41738058 | PMC:PMC12927845 | DOI:10.2147/NDT.S566720

Imaging Neurosci (Camb). 2026 Feb 20;4:IMAG.a.1143. doi: 10.1162/IMAG.a.1143. eCollection 2026.

ABSTRACT

Numerous approaches have been used to denoise spinal cord functional magnetic resonance imaging (fMRI) data. Principal component analysis (PCA)-based techniques, which derive regressors from a noise region of interest (ROI), have been used in both brain (e.g., CompCor) and spinal cord fMRI. However, spinal cord fMRI denoising methods have yet to be systematically evaluated. Here, we formalize and evaluate a PCA-based technique for deriving nuisance regressors for spinal cord fMRI analysis (SpinalCompCor). In this method, regressors are derived with PCA from a noise ROI, an area defined outside of the spinal cord and cerebrospinal fluid. A parallel analysis is used to systematically determine how many components to retain as regressors for modeling; this designated a median of 9 regressors across four fMRI datasets: motor task (n = 26), breathing task (n = 27), and resting state (n = 15 and n = 10). First-level fMRI modeling demonstrated that principal component regressors did fit noise (e.g., physiological noise from blood vessels), though the effectiveness may be dependent upon the acquisition parameters. However, group-level activation maps did not show a clear benefit from including SpinalCompCor regressors. The potential for collinearity of principal component regressors with the task may be a concern, and this should be considered in future implementations for which task-correlated noise is anticipated. In general, denoising with SpinalCompCor regressors in place of physiological recording-derived regressors is only recommended when the latter are unavailable, as SpinalCompCor may not consistently reproduce recording-based denoising across datasets or acquisitions.

PMID:41738011 | PMC:PMC12926774 | DOI:10.1162/IMAG.a.1143