Most recent paper

Eur J Nucl Med Mol Imaging. 2026 Jul 4. doi: 10.1007/s00259-026-08023-w. Online ahead of print.

ABSTRACT

AIM: To explore potential neuroimaging features derived from [18F]FDG PET/MRI imaging that may help characterize differences between adolescent with bipolar disorder (BD) and those with major depressive disorder (MDD).

MATERIALS AND METHODS: Patients were grouped based on the Hamilton Depression Scale (HAMD) and Hypomania Check List (HCL), and the severity of suicidal ideation and behavior was assessed. 14 BD and 14 MDD adolescent patients were enrolled. Simultaneous PET and MRI data (including 3D T1-weighted imaging, Diffusion Tensor Imaging (DTI), and resting-state functional MRI (fMRI)) were acquired. Functional connectivity (FC) and structural connectivity (SC) were derived from fMRI and DTI respectively. A multiple linear regression model was employed to predict FC from SC. SC-FC coupling was defined as Pearson correlation coefficient between predicted FC and original FC. Using cerebellar gray matter uptake as reference region, standardized uptake value ratio (SUVr) of each brain region was calculated as metabolic parameter.

RESULTS: Compared with the MDD group, the BD group showed significantly decreased SC-FC coupling and glucose metabolism in the thalamus. ‌In the BD group, SC-FC coupling in the paracentral lobule showed a significant positive correlation with metabolism (r = 0.70). In the MDD group, SC-FC coupling in cingulate gyrus showed a significant negative correlation with metabolism (r=-0.73). ‌In the BD group, SC-FC coupling in superior parietal lobule, postcentral gyrus, and precentral gyrus correlated significantly with HAMD, HCL, and suicide scores, respectively. In the MDD group, SC-FC coupling in precentral gyrus, orbital gyrus, and hippocampus showed the highest correlations with these clinical indicators. The middle temporal gyrus and parahippocampal gyrus were specifically associated with suicide scores in the BD group. ‌Notable intergroup differences between BD and MDD were thalamus (AUC = 0.86) and inferior temporal gyrus (AUC = 0.76), with SC-FC coupling features in these regions showing more prominent differences than metabolic features.

CONCLUSION: This study identifies the thalamus as a key region in adolescent BD. Differences in SC-FC coupling in thalamus and inferior temporal gyrus reflect notable exploratory features between adolescent BD and MDD patients, suggesting that SC-FC coupling parameters may represent potential neuroimaging signatures for differential diagnosis. The differences in correlations among SC-FC coupling, metabolism, and clinical parameters between the groups indicate distinct pathogenic mechanisms for BD and MDD.

PMID:42399489 | DOI:10.1007/s00259-026-08023-w

J Affect Disord. 2026 Jul 3:122212. doi: 10.1016/j.jad.2026.122212. Online ahead of print.

ABSTRACT

Although previous resting-state fMRI studies have revealed functional abnormalities in multiple brain regions of obsessive-compulsive disorder (OCD) patients, the underlying pathological mechanisms remain unclear. As the brain constitutes a complex dynamic system, capturing its dynamic features could advance our understanding of neural activity mechanisms. This study employed hidden Markov modeling (HMM) to investigate dynamic alterations in resting-state brain activity among OCD patients, offering a dynamic perspective on the disorder's pathophysiology. A total of 60 OCD patients and 57 healthy controls (HCs) were enrolled. The HMM was used to identify distinct brain states, characterizing their activation patterns. Temporal features of these states and differences in transition patterns between states were compared between the two groups. Compared to HCs, OCD patients spent significantly more time in the state characterized by mildly elevated global activation (p = 0.024, partial η2 = 0.070), while spending less time in the state characterized by high activation level in the auditory network (AUD) and salience network (SN), coupled with low activation level in default mode network (DMN) and frontoparietal network (FPN) (p = 0.030, partial η2 = 0.057). Additionally, OCD patients showed decreased switching rate (p = 0.025, partial η2 = 0.044) and particular difficulty transitioning from States 1, 2, and 5 into the state characterized by high SN and low DMN activation. These findings suggest altered dynamic patterns of brain network activity in OCD, characterized by abnormal temporal occupancy of specific brain states and reduced switching between states. These alterations may be related to salience network dysfunction and could contribute to our understanding of cognitive inflexibility in OCD from a dynamic perspective. However, further studies are needed to establish the specificity and clinical relevance of these findings.

PMID:42398672 | DOI:10.1016/j.jad.2026.122212

Res Sq [Preprint]. 2026 Jun 28:rs.3.rs-10021774. doi: 10.21203/rs.3.rs-10021774/v1.

ABSTRACT

Early-onset psychosis (EOP) is associated with marked cognitive impairment and poorer health-related and psychosocial outcomes compared to adult-onset illness, yet the neural mechanisms underlying these deficits during adolescence remain incompletely understood. Despite evidence implicating dorsolateral prefrontal cortex (dlPFC) dysfunction, the region's functional and structural correlates in youth with EOP are not well characterized. Adolescents with EOP (N=31) and healthy controls (HC; N=20) completed NIH Toolbox cognitive assessments and PROMIS self-reports. Prefrontal function was assessed using functional near-infrared spectroscopy (fNIRS) during a Stroop task and at rest. Multimodal MRI, including resting-state fMRI, diffusion tensor imaging, and structural imaging, was used to examine connectivity and morphology of prefrontal and fronto-cerebellar circuits. Adolescents with EOP exhibited poorer cognitive performance across executive functioning domains compared to HC. fNIRS revealed reduced right dlPFC activation on a cognitive control paradigm (p=0.007) and increased resting-state connectivity between right dlPFC and ventrolateral PFC (p=0.02). Furthermore, resting-state fMRI showed increased dlPFC-striatal connectivity and reduced connectivity with cerebellar Crus I/II (p-FDR<0.05). White matter integrity of the superior longitudinal fasciculus correlated with dlPFC activation during task performance. Structural analyses identified reduced frontal cortical thickness and decreased cerebellar Crus II volumes (p-FWE<0.05) in patients with EOP, with frontal morphology associating with cognitive measures. In summary, cognitive impairment in adolescents with EOP is associated with convergent abnormalities in dlPFC function, fronto-striatal connectivity, and fronto-cerebellar structure. These findings support a model of disrupted prefrontal circuit maturation in EOP and highlight multimodal imaging markers with potential relevance for early identification and targeted intervention.

PMID:42396496 | PMC:PMC13321269 | DOI:10.21203/rs.3.rs-10021774/v1

bioRxiv [Preprint]. 2026 Jun 24:2026.06.19.733190. doi: 10.64898/2026.06.19.733190.

ABSTRACT

Distinguishing healthy brain aging from early neurodegenerative disruption remains a major challenge in Alzheimer's disease. Here, we used resting-state fMRI from the Alzheimer's Disease Neuroimaging Initiative to model large-scale brain-state dynamics relative to a cognitively normal aging reference. A Hidden Markov Model trained exclusively on cognitively normal adults defined latent connectivity states, and a generalized additive model estimated an age-adjusted reference trajectory of transition entropy. Mild cognitive impairment and Alzheimer's disease showed progressively greater deviation from this reference, with the strongest disruption in Alzheimer's disease. A single absolute-deviation score retained much of the predictive information contained in higher-dimensional dynamic features, supporting its use as a compact and interpretable candidate biomarker of altered brain-state organization. These findings suggest that Alzheimer's disease is associated with measurable departure from healthy dynamic brain aging, providing an interpretable framework for future longitudinal and clinically validated studies.

PMID:42395576 | PMC:PMC13320867 | DOI:10.64898/2026.06.19.733190

bioRxiv [Preprint]. 2026 Jun 28:2026.06.25.734567. doi: 10.64898/2026.06.25.734567.

ABSTRACT

Primate brain function relies on distributed cortical networks. These networks are commonly identified through fMRI functional connectivity, defined as the spatial correlation of hemodynamic fluctuations measured at rest. To assess the contribution of distinct neuronal populations to fMRI functional connectivity, we obtained concurrent fMRI and dense single-unit recordings at rest in the macaque. Then, using standard waveform-based classification of action potential shape, we compared the activity of different neural subtypes to the local and brain-wide patterns of fMRI activity. Putative excitatory neurons were functionally intermixed, with approximately half having positive and half negative correlation with the local fMRI signal. By contrast, all putative inhibitory interneurons were positively correlated, with one subclass exhibiting brain-wide correlation that closely matched conventional seed-based functional connectivity. These findings indicate that, although excitatory projection neurons may underpin long-range network communication interneuron activity most closely matches the fMRI fluctuations at the heart of resting functional connectivity.

PMID:42395479 | PMC:PMC13321047 | DOI:10.64898/2026.06.25.734567

Front Neurol. 2026 Jun 18;17:1837032. doi: 10.3389/fneur.2026.1837032. eCollection 2026.

ABSTRACT

BACKGROUND: Herpes simplex virus (HSV) encephalitis is the most common sporadic viral encephalitis, frequently complicated by delirium. Intracranial pressure (ICP) elevation is a common feature, but its role in delirium via brain functional network disruption remains unclear. This study investigated whether surrogate marker of ICP burden (LP opening pressure) mediates delirium in HSV encephalitis by reducing degree centrality (DC), a measure of functional network hub integrity, in key brain regions.

METHODS: In a prospective two-center cohort study (December 2023-November 2025), 55 HSV encephalitis patients and 30 healthy controls underwent clinical assessments, ICP monitoring, and resting-state functional MRI (rs-fMRI). Delirium was evaluated using the Delirium Rating Scale (DRS; ≥12 indicating presence). DC was calculated from rs-fMRI data in regions of interest. Associations were analyzed via logistic regression, ANOVA, linear regression, and mediation models with bootstrapping.

RESULTS: Of 55 patients, 22 (40%) developed delirium. The delirium group had higher LP opening pressure (23.5 ± 4.8 vs. 15.5 ± 3.2 cmH₂O; p < 0.001), lower Glasgow Coma Scale scores, and worse outcomes. Elevated LP opening pressure (≥20 cmH₂O) increased delirium odds (OR 1.86; 95% CI 1.58-2.11; p < 0.001), amplified in subgroups with severe inflammation. DC was reduced in the delirium group in the right amygdala, right hippocampus, left insula, and left precuneus (all p < 0.05 vs. non-delirium and controls). LP opening pressure inversely correlated with DC in these regions (except left insula), and DC inversely correlated with DRS scores. Mediation analyses confirmed partial mediation by DC in the right amygdala (β = 0.129; 95% CI 0.079-0.158; p = 0.031), right hippocampus (β = 0.312; 95% CI 0.277-0.458; p = 0.002), and left precuneus (β = 0.275; 95% CI 0.187-0.398; p = 0.011).

CONCLUSION: Our exploratory findings suggest that LP opening pressure may be partially associated with delirium in HSV encephalitis through reduced degree centrality in limbic and default mode network hubs. As a preliminary investigation, these hypothesis-generating results highlight ICP management and DC as potential imaging biomarkers that warrant further validation in larger, longitudinal studies for delirium risk stratification and prevention.

PMID:42394934 | PMC:PMC13324997 | DOI:10.3389/fneur.2026.1837032

Eur J Phys Rehabil Med. 2026 Jun;62(3):245-253. doi: 10.23736/S1973-9087.26.09367-6.

ABSTRACT

BACKGROUND: Goal setting in stroke rehabilitation requires assessing patients' functional status, which involves both recovery and compensatory processes following stroke, underscoring the importance of identifying valid biomarkers. Functional connectivity measures derived from functional magnetic resonance imaging (fMRI) signals, which reflect underlying biological states, may offer value for characterizing functional status.

AIM: We tested whether functional connectivity correlates with functional status and discriminates post-stroke functional independence.

DESIGN: Observational study.

SETTING: Inpatient rehabilitation ward at Fujita Health University Hospital, Japan.

POPULATION: Fifty-eight patients with stroke.

METHODS: Resting-state fMRI data were used to compute group-sparse inverse covariance matrices, which were negated to estimate partial correlations representing direct functional connectivity. Correlations between connectivity measures and functional independence measure (FIM) scores were examined. Principal component analysis (PCA) was applied to extract dominant connectivity features from connections associated with functional status. Connectivity-based discriminative performance was evaluated using receiver operating characteristic analysis.

RESULTS: Connectome mapping revealed significant positive correlations between total FIM scores and interhemispheric connectivity in homotopic perisylvian regions, including the insulae, superior temporal sulci and temporoparietal junctions. PCA extracted a major connectivity component whose strength demonstrated within-sample discrimination of functional independence. Lesions were predominantly located in subcortical regions.

CONCLUSIONS: The results support a significant association between homotopic functional connectivity within perisylvian, multifunctional regions located at the intersection of lobes and post-stroke daily functioning. Intrinsic interhemispheric functional connectivity, assessed using fMRI, may offer valuable insights into post-stroke functional status.

CLINICAL REHABILITATION IMPACT: These findings highlight a promising direction for developing connectivity-based rehabilitation strategies for stroke-related disability.

PMID:42394539 | DOI:10.23736/S1973-9087.26.09367-6

Transl Psychiatry. 2026 Jul 2. doi: 10.1038/s41398-026-04217-w. Online ahead of print.

ABSTRACT

Cerebellar hypermetabolism measured with FDG-PET is interpreted as maladaptive plasticity, while increased cerebellar functional connectivity reported in fMRI studies indicates a compensatory role. Using Alcohol Use Disorder (AUD) as a neurobiological model, the combination of PET and fMRI examinations can extend our understanding of cerebellar mechanisms underlying brain reorganization within the fronto-thalamo-cerebellar circuit (FCC) supporting executive functions. The aim of the present study was to investigate resting-state functional connectivity (rs-FC) of the cerebellum and to examine its relationship with cerebellar metabolism, thalamic grey matter volume (GM) as a key node of the FCC, and executive functioning. In AUD patients, stronger negative rs-FC was found between the cerebellar lobule VIII seed and voxels in the left superior frontal gyrus compared with HC. Path analysis conducted in AUD patients indicated that cerebellar hypermetabolism was positively related to fronto-cerebellar rs-FC, and that fronto-cerebellar rs-FC was positively related to inhibition performance. In this model, after controlling for thalamic GM abnormalities, cerebellar hypermetabolism negatively impacted inhibition performance through rs-FC. Cerebellar hypermetabolism disrupts negative fronto-cerebellar rs-FC, resulting in desynchronization within the fronto-cerebellar loop that compromises compensation for executive deficits. Cerebellar hypermetabolism may represent a biomarker of alcohol-related brain dysfunction, as an initial mechanism in the cascade linking fronto-cerebellar desynchronization and executive impairment.

PMID:42393033 | DOI:10.1038/s41398-026-04217-w

J Psychiatr Res. 2026 Jul 1;201:455-462. doi: 10.1016/j.jpsychires.2026.06.053. Online ahead of print.

ABSTRACT

BACKGROUND: Tourette syndrome (TS) shows substantial clinical heterogeneity, but the factors contributing to this variability remain unclear. Socioeconomic status (SES) may influence brain functional connectivity (FC) and symptom expression, yet its role in pediatric TS is still not well understood.

OBJECTIVE: To clarify the role of SES in modulating brain-behavior relationships in TS.

METHODS: Resting-state fMRI data were acquired from 67 children and adolescents with TS and 49 matched controls. FC was computed within and between seven predefined brain networks. We identified group differences in FC, examined associations between SES and clinical measures, and tested whether SES indicators (family income, parental education, and residential location) moderated FC-symptom relationships.

RESULTS: Compared with controls, children with TS showed significant alterations in FC, mainly characterized by increased connectivity involving somatomotor, frontoparietal control, default mode, attention, and limbic networks, and decreased connectivity within the right frontoparietal control network. Within the TS group, higher parental education was associated with greater premonitory urge severity. Moderation analyses showed that family income, parental education, and residential location moderated FC-premonitory urge associations; higher family income and parental education strengthened these associations, whereas rural residence weakened them.

CONCLUSIONS: Socioeconomic context may contribute to variability in brain-symptom relationships in pediatric TS, particularly for premonitory urges rather than clinician-rated tic severity. These findings highlight the importance of considering socioeconomic context when interpreting neuroclinical heterogeneity in TS and may inform more context-sensitive clinical assessment and interpretation.

PMID:42391732 | DOI:10.1016/j.jpsychires.2026.06.053

J Pediatr Urol. 2026 Jun 9;22(5):106066. doi: 10.1016/j.jpurol.2026.106066. Online ahead of print.

ABSTRACT

INTRODUCTION: Nocturnal enuresis (NE) is a common neurodevelopmental condition, yet its underlying neural mechanisms remain unclear. This study leverages the large-scale Adolescent Brain Cognitive Development (ABCD) dataset to identify structural and functional brain correlates associated with active symptoms and the resolution of bedwetting.

METHODS: Using cross-sectional data from 3472 participants aged 9-10 years, children were categorized into three groups: active nocturnal enuresis (ANE, n = 225), history of nocturnal enuresis (HNE, n = 1171), and healthy control groups (CG, n = 2076). Multimodal neuroimaging protocol evaluated macrostructural properties via structural MRI (sMRI), microstructural white matter integrity via diffusion MRI (dMRI), and functional connectivity via resting-state fMRI (fMRI). Group differences were evaluated using linear models within an ANCOVA framework, adjusting for intracranial volume and handedness with False Discovery Rate (FDR) correction.

RESULTS: Compared to controls, the ANE group exhibited a significant volume deficit in the right caudate, decreased sulcal depth in the left insula, and lower internal correlation within the Cingulo-Opercular Network (CON). Conversely, the dry HNE group demonstrated significant structural adaptations, including bilaterally larger putamen volumes and increased right caudate volume compared to the ANE group. The HNE group also showed increased microstructural density (decreased mean diffusivity) in the bilateral hippocampus and an increased cortical surface area in the left insula. Both NE groups demonstrated persistently reduced functional coupling within the CON.

CONCLUSIONS: Nocturnal enuresis appears to be associated with a potential complex central signaling deficits. Reduced internal correlation within the CON across both active and former bedwetters indicates a potential for impairment in processing internal homeostatic bladder signals during sleep.

PMID:42391727 | DOI:10.1016/j.jpurol.2026.106066

Front Psychiatry. 2026 Jun 17;17:1846942. doi: 10.3389/fpsyt.2026.1846942. eCollection 2026.

ABSTRACT

BACKGROUND: This study examined the brain networks related to hyperactivity and impulsivity in children with Attention-Deficit/Hyperactivity Disorder (ADHD) and aimed to develop a resting-state functional connectivity marker that can predict symptom severity.

METHODS: A total of 44 children with ADHD (31 boys and 13 girls; mean age = 8.45 ± 1.52 years; range = 6-12 years) who met DSM-5 criteria were included. Resting-state fMRI data and clinical symptom scores were collected. Connectome-based predictive modeling (CPM) was used to predict hyperactivity-impulsivity symptoms based on whole-brain functional connectivity matrices. Symptoms were assessed using the SNAP-IV Parent and Teacher Rating Scales and the Conners Comprehensive Behavior Rating Scale. Model performance was tested with leave-one-out cross-validation and permutation testing.

RESULTS: The CPM model based on interregional functional connectivity successfully predicted hyperactivity-impulsivity symptoms (SNAP-IV parent and teacher ratings: r = 0.48, p = 0.001). The model also generalized well within the same dataset, as the selected functional connections significantly predicted hyperactivity-impulsivity scores on the Conners Parent Rating Scale (r = 0.49, p = 0.0009). Further analysis showed that stronger connectivity between the frontoparietal control network (FPN) and the dorsal attention network (DAN), and weaker connectivity between the FPN and the ventral attention network (VAN) and between the FPN and the somatomotor network (SMN), were related to symptom severity.

CONCLUSIONS: Whole-brain functional connectivity is associated with hyperactivity-impulsivity symptoms in children with ADHD. The findings highlight the key role of FPN-related networks in these symptoms and provide a neural correlate that, pending further validation, represents a preliminary candidate for a neuroimaging marker.

PMID:42389396 | PMC:PMC13318954 | DOI:10.3389/fpsyt.2026.1846942

Front Neurol. 2026 Jun 17;17:1868819. doi: 10.3389/fneur.2026.1868819. eCollection 2026.

ABSTRACT

OBJECTIVE: To determine whether high-frequency rTMS of the left angular gyrus improves behavioral signs of consciousness in prolonged disorders of consciousness (pDoC) and to identify network mechanisms and predictors of response.

METHODS: In a randomized, double-blind, sham-controlled crossover trial, patients with pDoC received 3 weeks of 20-Hz rTMS over the left angular gyrus and 3 weeks of sham stimulation in counterbalanced order, separated by a 1-week washout. Consciousness was evaluated using the Coma Recovery Scale-Revised (CRS-R). Resting-state fMRI was acquired at baseline and after each period to quantify network-level changes. Explainable machine-learning models related stimulation-induced network changes and baseline features to behavioral improvement.

RESULTS: Forty patients completed the trial. Behavioral improvement occurred more frequently during active than sham stimulation (p < 0.05), predominantly in visual, motor, and auditory domains. Explainable analyses indicated that post-rTMS recovery within the default mode network (DMN) and the subcortical network (SCN) best characterized responders (random forest AUC = 0.861). Stronger baseline within-DMN functional connectivity predicted greater likelihood of improvement (support vector machine AUC = 0.972).

CONCLUSION: Left angular gyrus rTMS improved consciousness-related behaviors in a subset of pDoC patients, potentially mediated by DMN-SCN network reconfiguration; patients with stronger baseline within-DMN connectivity were more likely to benefit.

CLINICAL TRIAL REGISTRATION: https://www.chictr.org.cn/index.html, identifier ChiCTR2300069980.

PMID:42388696 | PMC:PMC13318575 | DOI:10.3389/fneur.2026.1868819

Neuroimage. 2026 Jul 1:122085. doi: 10.1016/j.neuroimage.2026.122085. Online ahead of print.

ABSTRACT

Resting state functional magnetic resonance imaging (rs-fMRI) is a widely used imaging modality that can capture spontaneous neural activity of the brain. The human brain is a complex system, and emerging evidence suggests that the complexity of neural activity may serve as an index of its information processing capacity. In this study, we used multiscale sample entropy (MSE) to analyze the complexity of rs-fMRI time series from 504 healthy subjects aged 6 to 85 years. We constructed global and regional trajectories of the brain's functional complexity across the lifespan and examined its correlation with executive function. We observed a nonlinear trajectory of fMRI-complexity, with a peak occurring at 23 years of age (95% CI: 21.27,26.38 years). Males reached the peak complexity at 26 years (95% CI: 19.95, 33.14 years), whereas females peaked at 23 years (95% CI 20.16, 29.18 years). Significant correlations were found between complexity and Number-Letter Switching of the Trail Making Test in the parietal and medial temporal lobes, while Inhibition and Inhibition/Switching of the Color Word Interference Test showed significant negative correlations with rs-fMRI complexity in the lateral and medial frontal cortex. These results provide insight into the behavior of fMRI-complexity across lifespan stages and highlight its association with executive function. As a non-invasive biomarker, fMRI-complexity may offer a novel approach to understanding the brain's information processing capacity and its deficits in illness.

PMID:42386108 | DOI:10.1016/j.neuroimage.2026.122085

Neuroimage. 2026 Jul 1:122100. doi: 10.1016/j.neuroimage.2026.122100. Online ahead of print.

ABSTRACT

Schizophrenia (SZ) is a severe psychiatric disorder with heterogeneous symptoms. It often entails alterations in brain networks, conventionally probed by functional connectivity using resting-state data. This study investigated task-related alterations in brain organization, including the functional connectivity gradient and infra-slow sequential organization, during auditory tasks in individuals with SZ. Functional connectivity gradient analysis showed disrupted functional hierarchical organization in SZ. Sequential organization represented significant differences in phase shifts between SZ and healthy controls (HC) over the somatomotor, parietal, and supracallosal anterior cingulate cortex. Decoding analyses reliably classified SZ from HC using phase shift values. Particularly, phase shifts in the postcentral gyrus correlated with clinical courses. These findings indicate that SZ is characterized by abnormal hierarchical organization and infra-slow propagation during auditory processing.

PMID:42386106 | DOI:10.1016/j.neuroimage.2026.122100

Hum Brain Mapp. 2026 Jul;47(10):e70561. doi: 10.1002/hbm.70561.

ABSTRACT

Functional magnetic resonance imaging (fMRI) is a powerful tool for probing neuronal activity in vivo, but fMRI data are inherently noisy. To mitigate this, a wide range of denoising strategies have been developed, including volume censoring, anatomical component-based noise correction (aCompCor), ICA-based methods (e.g., AROMA, FIX), and multi-echo approaches (e.g., ME-ICA, tedana). These techniques are often applied in different combinations and have been predominantly evaluated on single-echo resting-state fMRI data-typically without incorporating more recent methodological advances known to improve modeling, such as order-independent "1-step regression", modeling temporal autocorrelation (pre-whitening), and temporal shifting of physiological nuisance regressors. To fill this gap, we used a framework that incorporates these methods and benchmarked a range of denoising pipelines across task and resting-state, single- and multi-band, and single- and multi-echo fMRI datasets, using different combinations of standard denoising confounds. Pipeline performance was evaluated using temporal signal-to-noise ratio (tSNR) and percentage remaining degrees-of-freedom (DoF), effectiveness of motion correction, and effectiveness of signal preservation. While pipelines only using ICA were insufficient, those that incorporated physiological nuisance regressors performed well. Additional improvements were observed when temporally shifted physiological regressors were accounted for. Based on these results, we provide recommendations for selecting denoising pipelines and emphasize the need for continued benchmarking as new methods are developed or applied in novel contexts.

PMID:42383403 | DOI:10.1002/hbm.70561

Front Neurol. 2026 Jun 3;17:1793397. doi: 10.3389/fneur.2026.1793397. eCollection 2026.

ABSTRACT

BACKGROUND: Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a common sequela following COVID-19. Although cognitive dysfunction is one of the most debilitating symptoms in ME/CFS, effective therapies are limited. Acupuncture is an important complementary and alternative therapy for ME/CFS and has been shown to have positive effects on cognitive dysfunction in other diseases. However, the effect and mechanism of acupuncture in treating cognitive dysfunction in post-COVID-19 ME/CFS(PCME/CFS) remain unclear. In this study, we designed a randomized controlled trial to evaluate the efficacy of acupuncture treatment in improving cognitive function in PCME/CFS and to investigate the neural mechanisms of acupuncture using multimodal magnetic resonance imaging (MRI) techniques.

METHODS: A total of 129 patients and 30 healthy controls (HCs) will be enrolled. The 129 patients with PCME/CFS will be randomly assigned in a 1:1:1 ratio to a verum acupuncture (VA), sham acupuncture (SA), or a waitlist control group. Participants in the VA and SA groups will receive three sessions of treatment per week for 8 weeks, while patients in the waitlist control group will be treated after the 8-week waiting period. The primary outcome is the change in the Symbol Digit Modalities Test (SDMT) score from baseline to week 8. The secondary outcome measures include changes from baseline to endpoint (week 8) in cognitive performance as assessed by the Digit Span Test (DST), Trail Making Test (TMT), Rey Auditory Verbal Learning Test (RAVLT), Rey-Osterrieth complex figure test (RCFT), Stroop Color and Word Test (SCWT), phonemic fluency test, category fluency test, action fluency test, and 30-item Boston Naming Test (BNT-30). In addition, changes in hippocampal metabolites and resting-state functional connectivity(RSFC) will be examined using 1H-magnetic resonance spectroscopy(1H-MRS) and functional MRI (fMRI), respectively. Moreover, the Multidimensional Fatigue Inventory (MFI-20), Pittsburgh Sleep Quality Index (PSQI), Generalized Anxiety Disorder 7-item scale (GAD-7), 24-item Hamilton Depression Scale (HAMD-24), and 36-Item Short Form Survey (SF-36) will also be assessed at baseline and week 8.

DISCUSSION: The results of this study will provide preliminary evidence regarding the efficacy of acupuncture therapy in improving cognitive function in PCME/CFS and will explore whether acupuncture improves cognitive function in this disease by modulating metabolism and RSFC in the hippocampus.

CLINICAL TRIAL REGISTRATION: www.clinicaltrials.gov, identifier: NCT07357688.

PMID:42383026 | PMC:PMC13318089 | DOI:10.3389/fneur.2026.1793397

Front Neurol. 2026 Jun 16;17:1846663. doi: 10.3389/fneur.2026.1846663. eCollection 2026.

ABSTRACT

BACKGROUND: Amnestic Mild Cognitive Impairment (aMCI) is a transitional stage between normal individuals and dementia. The basal forebrain is a key subcortical hub involved in memory-related cortico-subcortical networks.

METHODS: A total of 66 aMCI patients, including 5 single-domain and 61 multidomain aMCI patients, and 59 healthy controls (HCs) were recruited from the community and underwent comprehensive neuropsychological assessments. After excluding one HC participant due to excessive head motion, 66 aMCI patients and 58 HCs were included in the final imaging analysis. Resting-state functional magnetic resonance imaging (rs-fMRI) was performed, and seed-based functional connectivity analysis was conducted with the basal forebrain as the region of interest.

RESULTS: Compared with HCs, aMCI patients exhibited reduced basal forebrain functional connectivity overall, but showed increased connectivity between the right nucleus basalis of Meynert and specific cortical regions (p < 0.01, GRF corrected). Long-delayed recall was associated with connectivity between the basal forebrain and the left postcentral gyrus, while language and executive functions were associated with connectivity in the left inferior frontal gyrus (opercular part) and the calcarine cortex, respectively (p < 0.05).

CONCLUSION: These findings may help identify basal forebrain-associated network alterations as potential targets for future circuit-based neuromodulation studies in aMCI.

PMID:42383023 | PMC:PMC13314419 | DOI:10.3389/fneur.2026.1846663

Imaging Neurosci (Camb). 2026 Jun 29;4:IMAG.a.1285. doi: 10.1162/IMAG.a.1285. eCollection 2026.

ABSTRACT

The functional network architecture of the aging brain undergoes significant systematic and idiosyncratic changes. Emergent individualized network mapping approaches may yield better or more sensitive explanatory insight about age-related neural and behavioral variability, although most applications have focused on young adults. In the current study, we tested the validity and impact of mapping individual-specific topography in two fMRI datasets comprising 112 young (18-35 years) and 176 older adults (60-92 years). Older adults had more idiosyncratic network topography than young adults. Individualized maps from resting-state fMRI improved network homogeneity and fidelity to social cognitive task fMRI activations and exhibited intra-individual stability and inter-individual discriminability over a 2-year interval. Last, traditional group-averaged (vs. individualized) network mapping had a moderate-to-large impact on individual-level estimates of network segregation, a widely-studied measure of functional brain aging. Therefore, individualized network mapping captures important heterogeneity in older adulthood and may yield more precise characterization of neurocognitive aging.

PMID:42382506 | PMC:PMC13317017 | DOI:10.1162/IMAG.a.1285

IBRO Neurosci Rep. 2026 Jun 8;21:170-178. doi: 10.1016/j.ibneur.2026.06.002. eCollection 2026 Dec.

ABSTRACT

Aberrant static functional network connectivity (sFNC) and dynamic functional network connectivity (dFNC) have been inconsistently reported in Parkinson's disease (PD), reflecting the limitations of single-site studies with restricted sample sizes. To address these limitations, we leveraged pooled resting-state functional magnetic resonance imaging (rs-fMRI) data from a multisite study to explore FNC differences in PD. Data from 160 patients with PD and 75 age matched and sex-matched healthy controls (HCs) were collected from four repositories (PPMI, OpenfMRI, FCP/INDI). Independent component analysis combined with sliding-window and k-means clustering approach was applied to examine the FNC among seven resting-state networks: sensorimotor network (SMN), auditory network (AN), visual network (VN), cognitive control network (CCN), default mode network (DMN), subcortical network (SC), and cerebellar network (CB). The FNC and dynamic indices (fraction time, mean dwell time, transition number) were calculated. Static and dynamic measures were compared between the two groups. For sFNC, the PD group exhibited predominantly decreased connectivity between the VN and CCN/AN/SMN, and between the AN and SMN; alongside increased connectivity between the AN and SC/CB. For dFNC, four distinct recurrent states were identified. Significant group differences emerged in states 1, 3, and 4, characterized by widespread abnormalities in both intra- and inter-network connectivity (e.g., DMN, CCN, SC, CB, and sensory networks). Temporally, PD patients demonstrated altered dynamics, including reduced mean dwell time and fractional time in state 4, while showing increased mean dwell time in state 1 and fractional time in states 1 and 3. These multisite rs-fMRI findings demonstrate that PD is characterized by complex patterns of connectivity disruption and inflexible temporal network reconfigurations. Notably, dFNC metrics revealed extensive connectivity reductions that were not apparent in static analysis, highlighting their superior sensitivity and potential utility as biomarkers for tracking disease progression and therapeutic response.

PMID:42381885 | PMC:PMC13316611 | DOI:10.1016/j.ibneur.2026.06.002

Neuroimage Rep. 2026 Jun 20;6(3):100374. doi: 10.1016/j.ynirp.2026.100374. eCollection 2026 Sep.

ABSTRACT

OBJECTIVES: Alterations in neural language networks are common in patients with brain tumors, yet their nature varies substantially across individuals. By reducing data to group-level averages, conventional analyses fail to capture such heterogeneity, obscuring patient-specific information.

METHODS: The present study applied a resting-state connectivity fingerprinting approach to characterize language network alterations at the single-subject level, yielding individualized connectivity profiles ("fingerprints"). Fingerprints of 27 right-handed patients with a left-hemisphere brain tumor affecting language-relevant areas were assessed at three time points (preoperative, immediate postoperative and three-month follow-up). Connectivity patterns were compared to a normative reference derived from 30 healthy participants and linked to language performance.

RESULTS: Fingerprints remained temporally stable in healthy individuals. In patients, fingerprints revealed distinct, patient-specific deviations from the typical network structure with highly heterogeneous changes over time. Three main findings emerged: (1) patients with language deficits showed greater deviations from the typical fingerprint than those without deficits; (2) significant associations between larger deviations and poorer language performance were confined to the immediate postoperative phase, likely reflecting surgery- or treatment-related influences or differences in the (mal)adaptivity of reorganization over time; (3) in high-grade glioma, exploratory analyses provided preliminary evidence for an adaptive contribution of the contralesional hemisphere immediately after surgery.

CONCLUSIONS: The findings support connectivity fingerprinting as a promising approach for characterizing patient-specific network patterns and monitoring functional reorganization processes relevant to language function at the single-subject level. With continued methodological refinement, this approach holds potential for contributing to more individualized clinical decision-making within the context of personalized medicine.

PMID:42381864 | PMC:PMC13314782 | DOI:10.1016/j.ynirp.2026.100374