Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging

Aberrant interhemispheric resting state functional connectivity and corpus callosum microstructure in acute carbon monoxide poisoning

Acute carbon monoxide poisoning (ACOP) is a significant contributor to acute poisoning incidents worldwide, with numerous patients suffering from cognitive impairment. Growing evidence indicates that patients with ACOP exhibit both disrupted functional connectivity and corpus callosum (CC) degeneration. Nevertheless, how interhemispheric connectivity is altered in ACOP and how such alterations relate to cognitive deficits remain largely unexplored. In this study, multimodal magnetic resonance imaging was performed on 30 patients with ACOP and 28 healthy controls (HC), and their cognitive functions were evaluated. Group differences in the voxel-mirrored homotopic connectivity (VMHC) index and CC white matter microstructure were analyzed. Furthermore, mediation analysis was conducted to elucidate the interrelationships among CC integrity, interhemispheric connectivity, and cognitive impairment. Compared to HC, patients with ACOP exhibited reduced VMHC values in the middle frontal gyrus, inferior parietal lobule, precentral gyrus, and temporal regions, along with decreased fractional anisotropy values in the subregions of the CC, including the genu, body, and splenium. Partial correlation analyses showed that VMHC in the inferior parietal lobule positively correlated with Glasgow Coma Scale scores. In addition, VMHC in both the inferior parietal lobule and the middle temporal gyrus positively correlated with Montreal Cognitive Assessment-Basic scores. Mediation analysis indicated that changes in interhemispheric connectivity played a crucial role in mediating the effect of CC integrity on cognitive impairment. Together, these findings may offer novel insights into the neurobiological mechanisms underlying ACOP.

Robust computation of subcortical functional connectivity guided by quantitative susceptibility mapping: An application in Parkinson's disease diagnosis

Previous resting state functional MRI (rs-fMRI) analyses of the basal ganglia in Parkinson's disease heavily relied on T1-weighted imaging (T1WI) atlases. However, subcortical structures are characterized by subtle contrast differences, making their accurate delineation challenging on T1WI. In this study, we aimed to introduce and validate a method that incorporates quantitative susceptibility mapping (QSM) into the rs-fMRI analytical pipeline to achieve precise subcortical nuclei segmentation and improve the stability of RSFC measurements in Parkinson's disease. A total of 321 participants (148 patients with Parkinson's Disease and 173 normal controls) were enrolled. We performed cross-modal registration at the individual level for rs-fMRI to QSM (FUNC2QSM) and T1WI (FUNC2T1), respectively.The consistency and accuracy of resting state functional connectivity (RSFC) measurements in two registration approaches were assessed by intraclass correlation coefficient and mutual information. Bootstrap analysis was performed to validate the stability of the RSFC differences between Parkinson's disease and normal controls. RSFC-based machine learning models were constructed for Parkinson's disease classification, using optimized hyperparameters (RandomizedSearchCV with 5-fold cross-validation). The consistency of RSFC measurements between the two registration methods was poor, whereas the QSM-guided approach showed better mutual information values, suggesting higher registration accuracy. The disruptions of RSFC identified with the QSM-guided approach were more stable and reliable, as confirmed by bootstrap analysis. In classification models, the QSM-guided method consistently outperformed the T1WI-guided method, achieving higher test-set ROC-AUC values (FUNC2QSM: 0.87-0.90, FUNC2T1: 0.67-0.70). The QSM-guided approach effectively enhanced the accuracy of subcortical segmentation and the stability of RSFC measurement, thus facilitating future biomarker development in Parkinson's disease.

Time-dependent enhancement of corticospinal excitability during cortico-cortical paired associative stimulation of the hV6A-M1 network in the human brain

Cortico-cortical paired associative stimulation (ccPAS) is a powerful transcranial magnetic stimulation (TMS) protocol thought to rely on Hebbian plasticity and known to strengthen effective connectivity, mainly within frontal lobe networks. Here, we expand on previous work by exploring the effects of ccPAS on the pathway linking the medial posterior parietal area hV6A with the primary motor cortex (M1), whose plasticity mechanisms remain largely unexplored. To assess the effective connectivity of the hV6A-M1 network, we measured motor-evoked potentials (MEPs) in 30 right-handed volunteers at rest during dual-site, paired-pulse TMS. Consistent with previous findings, we found that MEPs were inhibited when the conditioning stimulus over hV6A preceded the test stimulus over M1 by 12 ms, highlighting inhibitory hV6A-M1 causal interactions. We then manipulated the hV6A-M1 circuit via ccPAS using different inter-stimulus intervals (ISI) never tested before. Our results revealed a time-dependent modulation. Specifically, only when the conditioning stimulus preceded the test one by 12 ms did we find a gradual increase of MEP amplitude during ccPAS, and excitatory aftereffects. In contrast, when ccPAS was applied with an ISI of 4 ms or 500 ms, no corticospinal excitability changes were observed, suggesting that temporal specificity is a critical factor in modulating the hV6A-M1 network. These results suggest that ccPAS can induce time-dependent Hebbian plasticity in the dorsomedial parieto-frontal network at rest, offering novel insights into the network's plasticity and temporal dynamics.

Default mode network-basal ganglia network connectivity predicts the transition to postherpetic neuralgia

Background

Neuroimaging studies have revealed aberrant network functional connectivity in postherpetic neuralgia (PHN) patients. However, there is a lack of knowledge regarding the relationship between the brain network connectivity during the acute period and disease prognosis.

Objective

The purpose of this study was to detect characteristic network connectivity in the process of herpes zoster (HZ) pain chronification and to identify whether abnormal network connectivity in the acute period can predict the outcome of patients with HZ.

Methods

In this cross-sectional study, 31 patients with PHN, 33 with recuperation from herpes zoster (RHZ), and 28 with acute herpes zoster (AHZ) were recruited and underwent resting-state functional magnetic resonance imaging (fMRI). We investigated the differences in the connectivity of four resting-state networks (RSN) among the three groups. Receiver operating characteristic (ROC) curve analysis was performed to identify whether abnormal network connectivity in the acute period could predict the outcome of patients with HZ.

Results

First, we found within-basal ganglia network (BGN) and default mode network (DMN)-BGN connectivity differences, with PHN patients showing increased DMN-BGN connectivity compared to AHZ and RHZ patients, while RHZ patients showing increased within-BGN connectivity compared to AHZ and PHN patients. Moreover, DMN-BGN connectivity was associated with the ID pain score in patients with AHZ. Finally, the DMN-BGN connectivity of AHZ patients could predict the outcome of HZ patients with sensitivity and specificity of 77.8 % and 63.2 %, respectively.

Conclusions

Our results provide evidence that DMN-BGN connectivity during the acute period confers a risk for the development of chronic pain and can act as a neuroimaging biomarker to predict the outcome of patients with HZ.

Neuroimaging in Neuro-Ophthalmology: Past, Present, and Future

BACKGROUND

Before 1895, all anatomic, pathologic, and functional understanding of the visual system was provided by postmortem studies. The advent of neuroimaging in 1895 with the development of X-ray technology enabled the living brain to be visualized, including the intraorbital and intracranial visual pathways. This has been augmented with the development of computed tomography, magnetic resonance imaging, and positron emission tomography.

EVIDENCE ACQUISITION

A literature review of the history of neuroimaging of the visual axis was completed from the time of antiquity to the present day.

RESULTS

The ability to visualize intracranial and orbital anatomy has been completely transformed. Imaging the visual axis has become faster, easier, and more precise allowing earlier diagnosis and management of a multitude of neuro-ophthalmic conditions. As we look to the future of neuroimaging, there is momentum to improve techniques enabling the assessment of microstructural architecture, metabolic and functional changes, and genetic biomarkers of disease.

CONCLUSIONS

The development of high-resolution, multiplanar neuroimaging revolutionized the ability to visualize neuro-ophthalmic anatomy and pathology. Continued research will expand our ability to integrate the metabolic, anatomic, and connectivity profiles of the visual system.

Aberrant white matter function and structure in Rhegmatogenous retinal detachment: A study utilizing functional network clustering and TractSeg methods

Rhegmatogenous retinal detachment (RRD) has been linked to abnormal functional changes in visual pathways and gray matter regions; however, alterations in white matter (WM) function and structure remain poorly understood. Using functional clustering networks and TractSeg methodologies, we investigated WM alterations in RRD patients and employed Support Vector Machine (SVM) algorithms for classification. RRD patients demonstrated reduced functional covariance connectivity (FCC) between the Superior Temporal Network and the Cerebellar Network, along with increased WM amplitude in the Anterior Corpus Callosum Network. Distinct differences in WM fiber bundles associated with visual and cognitive functions were observed, with visual acuity negatively correlating with amplitudes in the Occipital Networks. The SVM model based on WM7_amplitude achieved the highest AUC, highlighting its potential as a neurobiological marker for distinguishing RRD patients from healthy controls (HCs). These findings reveal critical disruptions in WM functional and structural integrity linked to cognitive and visual deficits in RRD, offering novel insights into the neural mechanisms underlying these impairments.

Correlation Between Neurocognitive Outcomes and Neuroaxonal Connectome Alterations After Whole Brain Radiotherapy: A Proof-of-Concept Study

Background/Objectives: Connectomics is an evolving branch of neuroscience that determines structural and functional connectivity in the brain. The objective of this prospective imaging study is to evaluate the effect of whole brain radiotherapy (WBRT) on the connectome. Methods: A combination of diffusion tensor imaging (DTI) and functional magnetic resonance imaging (fMRI) was used to study the structural and functional connectivity of the brain, and a machine learning algorithm trained to analyze subject-specific data was applied to create individualized brain maps with 15 neuronal networks for each patient. These brain maps were compared to normal brains from the human connectome project, producing an anomaly matrix. Connectome analysis and multi-dimensional neurocognitive testing on a web-based platform were performed at baseline and 3 months post-WBRT. The change in anomaly frequency was co-related with neurocognitive outcomes. Results: At baseline, connectome analysis revealed that the multiple demand network had the most anomalies (46%). Pre- and post-WBRT comparison revealed increases in proportional anomaly frequency across multiple networks. Pearson correlation showed correlation between neurocognitive domain decline and anomaly changes: learning and memory domain with subcortical network [Verbal recall (Pearson coefficient -0.94; p < 0.01), verbal revision (Pearson coefficient -0.89; p = 0.01), and verbal recognition (Pearson coefficient -0.94; p < 0.01)]. Conclusions: This proof-of-concept study integrated data from DTI and fMRI in the form of connectome and revealed significant changes in brain connectivity, with WBRT that also correlated with neurocognitive outcomes. Further studies in a larger cohort are underway, and correlations with white matter changes and tumor locations/numbers will be performed.

Atypical segregation of frontoparietal and sensorimotor networks is related to social and executive function impairments in children with ASD

Two possible indicators of a deficient segregation of functional networks are within-underconnectivity and between-overconnectivity. Both these processes can be observed in Autism Spectrum Disorder (ASD) to be associated with different core and co-occurring atypicalties of behavior. We focused on within- and between-network connectivity of Frontoparietal and Sensorimotor networks in ASD compared to typically developed (TD) peers and its links to social difficulties and impairments of executive and motor functions. To our knowledge, this study for the first time described between-network connectivity of Frontoparietal and Sensorimotor networks in ASD with relations to symptoms of ASD. In this study, we utilised resting-state functional MRI to investigate 121 participants with ASD and 84 TD children. We investigated between-group differences of the connectivity between Frontoparietal and Sensorimotor regions. We also conducted brain-behavior analysis for beta values of these connections and behavioral scores. Controlling for age and sex, we found a significant group difference within- Frontoparietal network (right and left posterior parietal cortices were underconnected in ASD) and between-networks (right posterior parietal and right lateral sensorimotor cortices were overconnected in ASD). In the ASD group, we also showed that within-Frontoparietal underconnectivity was related to lower scores of social and executive functions as well as between-networks overconnectivity was associated with social difficulties only. There were no significant relationships between scores of motor functions and beta values. We confirmed the hypothesis of deficient segregation for Frontoparietal and Sensorimotor networks in ASD. These findings highlight the importance of between-network connectivity investigation.

The benefit and neural mechanisms of computerized inhibitory control training for insomnia with short sleep duration phenotype: a rs-fMRI study

BACKGROUND

Inhibitory control (IC) impairment is characteristic of insomnia disorder with short sleep duration (ISSD), but not with normal sleep duration (INSD). IC is critical for sleep-wake regulation. This study evaluates whether computerized IC training can improve sleep in ISSD and explores related neural mechanisms using resting-state fMRI (rs-fMRI).

METHODS

Twenty ISSD patients participated in a three-week computerized IC training program (15 sessions), alongside a control group of 17 participants. Sleep quality was assessed using the Pittsburgh Sleep Quality Index (PSQI) and the Insomnia Severity Index (ISI), complemented by objective measures from overnight EEG recordings. Neuroimaging analyses focused on changes in regional homogeneity (ReHo), fractional amplitude of low-frequency fluctuations (fALFF), and functional connectivity (FC) in brain regions associated with IC.

RESULTS

Computerized IC training led to significant improvements in both subjective and objective sleep quality, demonstrated by reductions in PSQI and ISI scores, as well as decreased wake time during sleep. Neuroimaging revealed increased ReHo in the left medial orbitofrontal cortex (MOFC), elevated fALFF in the right middle frontal gyrus (MFG), and enhanced FC between the MOFC and the right rectus gyrus (RG), which correlated with improvements in sleep measures.

CONCLUSION

Computerized IC training appears to be an effective intervention for improving sleep in ISSD, likely by inducing functional changes in prefrontal cortex regions. These findings underscore the potential of IC-targeted treatments for ISSD and highlight the need for future research to evaluate the long-term effects of such interventions.

TRIAL REGISTRATION

The study was prospectively registered on May 30, 2024, in Chinese Clinical Trials registry (ChiCTR2400085063).

Behavioral manifestations and neural mechanisms of empathic pain

Empathy is an important trait that allows individuals to comprehend and share the emotions and sentiments of others. It not only facilitates effective interpersonal communication, but also helps in establishing meaningful connections and fostering trust and understanding. Impaired empathy development can manifest as excessive self-centeredness, extreme egoism, and antisocial behaviors. Many psychiatric disorders, such as autism, narcissistic personality disorder, and schizophrenia, are often accompanied by empathy disorders. Pain empathy, which is a common behavioral paradigm of empathic behavior, is not only observed in humans but also in animals. By delving into the study of pain empathy, we can gain a deeper understanding of empathy itself. This understanding not only contributes to the advancement of scientific, clinical, and social fields, but also promotes the cultivation of emotional resonance and social harmony among humans, with profound significance and impact. This article provides a brief overview of the current understanding and mechanistic studies of pain empathy, as well as suggests future research directions.

How to measure functional connectivity using resting-state fMRI? A comprehensive empirical exploration of different connectivity metrics

BACKGROUND

Functional connectivity in the context of functional magnetic resonance imaging is typically quantified by Pearson´s or partial correlation between regional time series of the blood oxygenation level dependent signal. However, a recent interdisciplinary methodological work proposes >230 different metrics to measure similarity between different types of time series.

OBJECTIVE

Hence, we systematically evaluated how the results of typical research approaches in functional neuroimaging vary depending on the functional connectivity metric of choice. We further explored which metrics most accurately detect presumed reductions in connectivity related to age and malignant brain tumors, aiming to initiate a debate on the best approaches for assessing brain connectivity in functional neuroimaging research.

METHODS

We addressed both research questions using four independent neuroimaging datasets, comprising multimodal data from a total of 1187 individuals. We analyzed resting-state functional sequences to calculate functional connectivity using 20 representative metrics from four distinct mathematical domains. We further used T1- and T2-weighted images to compute regional brain volumes, diffusion-weighted imaging data to build structural connectomes, and pseudo-continuous arterial spin labeling to measure regional brain perfusion.

RESULTS

First, our findings demonstrate that the results of typical functional neuroimaging approaches differ fundamentally depending on the functional connectivity metric of choice. Second, we show that correlational and distance metrics are most appropriate to cover reductions in connectivity linked to aging. In this context, partial correlation performs worse than other correlational metrics. Third, our findings suggest that the FC metric of choice depends on the utilized scanning parameters, the regions of interest, and the individual investigated. Lastly, beyond the major objective of this study, we provide evidence in favor of brain perfusion measured via pseudo-continuous arterial spin labeling as a robust neural entity mirroring age-related neural and cognitive decline.

CONCLUSION

Our empirical evaluation supports a recent theoretical functional connectivity framework. Future functional imaging studies need to comprehensively define the study-specific theoretical property of interest, the methodological property to assess the theoretical property, and the confounding property that may bias the conclusions.

The brain's "dark energy" puzzle upgraded: [18F]FDG uptake, delivery and phosphorylation, and their coupling with resting-state brain activity

The brain's resting-state energy consumption is expected to be driven by spontaneous activity. We previously used 50 resting-state fMRI (rs-fMRI) features to predict [18F]FDG SUVR as a proxy of glucose metabolism. Here, we expanded on our effort by estimating [18F]FDG kinetic parameters Ki (irreversible uptake), K1 (delivery), k3 (phosphorylation) in a large healthy control group (n = 47). Describing the parameters' spatial distribution at high resolution (216 regions), we showed that K1 is the least redundant (strong posteromedial pattern), and Ki and k3 have relevant differences (occipital cortices, cerebellum, thalamus). Using multilevel modeling, we investigated how much spatial variance of [18F]FDG parameters could be explained by a combination of a) rs-fMRI variables, b) cerebral blood flow (CBF) and metabolic rate of oxygen (CMRO2) from 15O PET. Rs-fMRI-only models explained part of the individual variance in Ki (35%), K1 (14%), k3 (21%), while combining rs-fMRI and CMRO2 led to satisfactory description of Ki (46%) especially. Ki was sensitive to both local rs-fMRI variables (ReHo) and CMRO2, k3 to ReHo, K1 to CMRO2. This work represents a comprehensive assessment of the complex underpinnings of brain glucose consumption, and highlights links between 1) glucose phosphorylation and local brain activity, 2) glucose delivery and oxygen consumption.

A human-specific enhancer fine-tunes radial glia potency and corticogenesis

Humans have evolved an extraordinarily expanded and complex cerebral cortex associated with developmental and gene regulatory modifications1-3. Human accelerated regions (HARs) are highly conserved DNA sequences with human-specific nucleotide substitutions. Although there are thousands of annotated HARs, their functional contribution to species-specific cortical development remains largely unknown4,5. HARE5 is a HAR transcriptional enhancer of the WNT signalling receptor Frizzled8 that is active during brain development6. Here, using genome-edited mouse (Mus musculus, Mm) and primate models, we demonstrated that human (Homo sapiens, Hs) HARE5 fine-tunes cortical development and connectivity by controlling the proliferative and neurogenic capacities of neural progenitor cells. Hs-HARE5 knock-in mice have significantly enlarged neocortices, containing more excitatory neurons. By measuring neural dynamics in vivo, we showed that these anatomical features result in increased functional independence between cortical regions. We assessed underlying developmental mechanisms using fixed and live imaging, lineage analysis and single-cell RNA sequencing. We discovered that Hs-HARE5 modifies radial glial cell behaviour, with increased self-renewal at early developmental stages, followed by expanded neurogenic potential. Using genome-edited human and chimpanzee (Pan troglodytes, Pt) neural progenitor cells and cortical organoids, we showed that four human-specific variants of Hs-HARE5 drive increased enhancer activity that promotes progenitor proliferation. Finally, we showed that Hs-HARE5 increased progenitor proliferation by amplifying canonical WNT signalling. These findings illustrate how small changes in regulatory DNA can directly affect critical signalling pathways to modulate brain development. Our study uncovered new functions of HARs as key regulatory elements crucial for the expansion and complexity of the human cerebral cortex.

Aberrant white matter and subcortical gray matter functional network connectivity associated with static and dynamic characteristics in subjects with temporal lobe epilepsy

Temporal lobe epilepsy (TLE) is a common type of epilepsy, with seizures primarily originating in the deep temporal lobe. This condition results in changes in connectivity across gray matter (GM), and white matter (WM) regions. This altered connectivity categorizes TLE as a network disorder, highlighting the need to investigate functional network connectivity (FNC) in WM areas. Dynamic functional connectivity (dFC) measures time-varying correlations between two or multiple regions of interest and derives clusters highlighting functional networks (FNs) where connectivity among regions behaves in a similar fashion. In this study, we included a total of 103 subjects from the Epilepsy Connectome Project, comprising 51 healthy controls (HC), and 52 subjects with TLE. We obtained static FNs (sFNs) and dynamic FNs (dFNs) using K-means clustering on ROI-based static functional connectivity (sFC) and dFC, respectively. Both static and dynamic FNCs were then separately investigated in HC and TLE subjects, with the latter demonstrating significant differences in WM networks. The static FNC was significantly decreased between the Forceps minor-Anterior corona radiata (ACR) - genu and left inferior longitudinal fasciculus (ILF) in TLE. Dynamic FNC significantly decreased between the corpus callosum (CC) (body) - superior corona radiata - right superior longitudinal fasciculus network and the Forceps minor - ACR - medial frontal gyrus network in subjects with TLE. This result implies that this WM connection changes with lower variability in TLE. On the other hand, the dynamic connections between the left temporal sub gyral - left thalamus - left pallidus - left hippocampus and right thalamus - right putamen - right temporal sub gyral - right pallidus network and the connections between the cingulum network and right thalamus - right putamen - right temporal sub gyral - right pallidus network significantly increased. These results indicate that these two GM subcortical connections change with higher variability in TLE. The study also demonstrates that the static functional connectivity strength (FCS) of the left ILF decreased significantly in subjects with TLE. However, the dynamic FCS of the splenium and brain stem were altered significantly in TLE, implying that the total dynamic connections of this network with all other networks experienced greater changes. Furthermore, the FNC suggests that the WM regions - ILF, superior and ACR, and CC exhibit connectivity changes related to the clinical features.

Gray and white matter alterations in Obsessive-Compulsive Personality Disorder: a data fusion machine learning approach

Introduction

Obsessive-Compulsive Personality Disorder (OCPD) is a complex mental condition marked by excessive perfectionism, orderliness, and rigidity, often starting in adolescence or early adulthood; it affects 1.9% to 7.8% of the population. The disorder differs from Obsessive-Compulsive Disorder (OCD) in an apparent compromise of personality, distorted self-representation, and altered perception of others. Although the two disorders present evident differences, unlike OCD, the neural bases of OCPD are understudied. The few studies conducted so far have identified gray matter alterations in brain regions such as the striatum and prefrontal cortex. However, a comprehensive model of its neurobiology and the eventual contribution of white matter abnormalities are still unclear. One intriguing hypothesis is that regions ascribed to the Default Mode Network are involved in OCPD, similar to what has been shown for OCD and other anxiety disorders.

Methods

To test this hypothesis, the gray and white matter images of 30 individuals diagnosed with OCPD (73% female, mean age=29.300), and 34 non-OCPD controls (82% female, mean age = 25.599) were analyzed for the first time with a data fusion unsupervised machine learning method known as Parallel Independent Component Analysis (pICA) to detect the joint contribution of these modalities to the OCPD diagnosis.

Results

Results indicated that two gray matter networks (GM-05 and GM-23) and one white matter network (WM-25) differed between the OCPD and the control group. GM-05 included brain regions belonging to the Default Mode Network and the Salience Network and was significantly correlated with anxiety; GM-23 included portions of the cerebellum, the precuneus, and the fusiform gyrus; WM-25 included white matter portions adjacent to Default Mode Network regions.

Discussion

These findings shed new light on the gray and white matter contributions to OCPD and may pave the way to developing objective markers of this disorder.

Neural correlates of executive dysfunction in alcohol use disorder: preliminary evidence from 18F-FDG-PET

Neuroimaging studies have shown that cognitive impairments in Alcohol Use Disorder (AUD), particularly involving executive functions, reflect widespread structural and functional brain alterations. However, these findings mostly result from magnetic resonance imaging (MRI). To complement previous MRI findings with a more direct measure of brain metabolism, we therefore explored the neural bases of executive impairments in AUD using FDG-PET. Twenty-three AUD patients and 18 healthy controls underwent a neurocognitive assessment, and patients also an 18F-FDG-PET scan. Using as reference for brain metabolism a FDG-PET dataset of age-matched healthy controls, we assessed a relationship between executive impairment and regional hypometabolism in AUD patients, while also considering a possible moderating age effect. Compared with controls, AUD patients exhibited widespread hypometabolism in the anterior/midcingulate cortex, fronto-insular cortex, and medial precuneus, supporting the hypothesis that their impaired executive performance might reflect an altered transition from automatic to controlled processing. Patients' worse executive performance reflected in higher metabolism in the midcingulate cortex and medial precuneus, suggesting a possible compensatory neural mechanism. This relationship was moderated by age in the right anterior insula, where the decrease of metabolism is steeper, in older patients, at the lowest level of cognitive performance. This finding suggests that an age-related decrease in the compensatory capacity of the insular node of the salience network might contribute to cognitive decline in older patients. While supporting the use of FDG-PET to improve the understanding of AUD-related cognitive decline, and differential diagnosis in older patients, these findings might help design personalized innovative treatment protocols.

The predictive nature of spontaneous brain activity across scales and species

Emerging research suggests the brain operates as a "prediction machine," continuously anticipating sensory, motor, and cognitive outcomes. Central to this capability is the brain's spontaneous activity-ongoing internal processes independent of external stimuli. Neuroimaging and computational studies support that this activity is integral to maintaining and refining mental models of our environment, body, and behaviors, akin to generative models in computation. During rest, spontaneous activity expands the variability of potential representations, enhancing the accuracy and adaptability of these models. When performing tasks, internal models direct brain regions to anticipate sensory and motor states, optimizing performance. This review synthesizes evidence from various species, from C. elegans to humans, highlighting three key aspects of spontaneous brain activity's role in prediction: the similarity between spontaneous and task-related activity, the encoding of behavioral and interoceptive priors, and the high metabolic cost of this activity, underscoring prediction as a fundamental function of brains across species.

Altered Amplitude of Low-Frequency Fluctuation of the Brain Regions Associated with Pain Symptoms and Negative Emotion in Trigeminal Neuralgia

OBJECTIVE

Using whole-brain functional magnetic resonance imaging, we aimed to investigate abnormal spontaneous brain activity in the resting state of patients with trigeminal neuralgia (TN) and explore their relationship with pain symptoms and negative emotions.

METHODS

This study included 46 patients with TN diagnosed at our hospital from December 2022 to June 2023 and 35 healthy controls. All patients with TN completed questionnaires related to pain and emotions. The data analysis compared amplitude of low-frequency fluctuation (ALFF) in the brain between TN and healthy control groups. Pearson correlation analysis was used to explore the intricate relationships between pain symptoms, negative emotions, and brain functional abnormalities in patients with TN.

RESULTS

Compared with healthy controls, patients with TN exhibited significantly reduced ALFF in the left superior frontal gyrus (SFG), bilateral middle frontal gyrus, bilateral inferior frontal gyrus, right precentral gyrus, right superior temporal gyrus, bilateral middle temporal gyrus, left inferior temporal gyrus, and right cingulate gyrus (CG) (P < 0.05). In correlation analysis, ALFF in the left SFG and right CG was negatively correlated with pain symptoms and negative emotions in patients with TN.

CONCLUSIONS

Patients with TN show functional abnormalities in several key brain regions that are involved in pain perception and emotion regulation. These abnormalities primarily manifest as a reduction in spontaneous neural activity. ALFF in the left SFG and right CG is negatively correlated with the severity of pain and negative emotions, indicating that as pain and negative emotions become more severe in patients with TN, neural activity decreases more significantly in specific brain regions. This suggests that the left SFG and right CG may be characteristic brain regions in the pathophysiological mechanism of TN.

Brain-thyroid crosstalk: 18F-FDG-PET/MRI evidence in patients with follicular thyroid adenomas

OBJECTIVE

The hypothalamic-pituitary-thyroid axis has been well-known. However, whether follicular thyroid adenoma (FTA) could affect brain glucose metabolism is still unknown. Therefore, we explored the brain glucose metabolic characteristics of FTA with Fluorodeoxyglucose F18 positron emission tomography/magnetic resonance imaging.

METHODS

Totally 30 FTA patients without clinical symptoms (FTA group), and 60 age- and sex-matched healthy controls (HC group) were included and randomly divided into cohort A and B in 2:1 ratio. Cohort A was analyzed with scaled sub-profile model/principal component analysis (SSM/PCA) for pattern identification. Cohort B was calculated the individual scores to validate expression of the pattern. Then we calculated the metabolic connectivity based on characteristics of the pattern to investigate the underlying mechanism. Finally, we constructed metabolic brain networks and analyzed the topological properties to further explore the brain metabolic model.

RESULTS

In SSM/PCA, FTA group showed an almost global, left-right symmetrical pattern. In metabolic connectivity, FTA group showed increased metabolic connectivity in brain regions of the sensorimotor network, ventral default mode network (DMN), posterior salient network, right executive control network (ECN), visuospatial network and language network when compared to HC group, and showed decreased connectivity in dorsal DMN and left ECN. In topological properties of brain network, FTA group showed an increased betweenness centrality (BC) in left rolandic operculum, a decreased BC in superior temporal gyrus, increased BC and Degree in right precentral gyrus, increased D in right parahippocampal gyrus and left hippocampus, and decreased D and efficiency in right orbital part of middle frontal gyrus (FDR correction for multiple comparisons, P < 0.05).

CONCLUSION

Although FTA patients are not yet symptomatic, their brain metabolic characteristics include extensive brain alterations, disrupted internal connectivity, not only involving brain regions associated with endocrine activity, but also brain networks and regions associated with motor, emotion and cognition.

Brain structural and functional magnetic resonance imaging alterations in individuals with convergence insufficiency

PURPOSE

Individuals with convergence insufficiency (CI) encounter challenges in turning their eyes inward during near work. It is unclear how this relates to brain structural and functional alterations. This study aimed to explore the neural mechanism underlying CI using multimodal brain magnetic resonance imaging (MRI).

METHODS

Thirty-four CI participants and 35 healthy controls (HC) were recruited, who underwent visual examinations and brain MRI scanning. Structural MRI data were analysed to calculate cortical thickness, volume and surface area. Fractional amplitude of low-frequency fluctuation (fALFF) and seed-based functional connectivity were obtained from resting-state functional MRI data. The brain structural and functional metrics were compared between the two groups followed by correlation analyses between clinical measurements and significant brain features.

RESULTS

Relative to HC, individuals with CI had lower grey matter volume (GMV) and surface area in the right frontal eye fields, parietal eye fields and left medial orbitofrontal cortex, higher GMV and surface area in the right middle frontal and inferior temporal gyri and higher fALFF of the left cerebellum and functional connection between bilateral cerebellums. GMV of the right middle frontal gyrus and fALFF in the left cerebellum were positively correlated with the near point of convergence in all participants.

CONCLUSIONS

Lower structural metrics in the visual and oculomotor cortices and higher functional activity in the cerebellum may underpin convergence dysfunction and visual fatigue, while higher structural metrics in the right middle frontal and inferior temporal gyri reflect partial compensation for the visual and oculomotor cortex defects, thereby maintaining attention and parallax information processing. This study may enhance understanding of the neural mechanism of CI by revealing the impact of abnormal visual experiences of CI on the brain with disassociated structural and functional alterations in the vergence system.

Investigating brain network dynamics in state-dependent stimulation: A concurrent electroencephalography and transcranial magnetic stimulation study using hidden Markov models

BACKGROUND

Systems neuroscience studies have shown that baseline brain activity can be categorized into large-scale networks (resting-state-networks, RNSs), with influence on cognitive abilities and clinical symptoms. These insights have guided millimeter-precise selection of brain stimulation targets based on RSNs. Concurrently, Transcranial Magnetic Stimulation (TMS) studies revealed that baseline brain states, measured by EEG signal power or phase, affect stimulation outcomes. However, EEG dynamics in these studies are mostly limited to single regions or channels, lacking the spatial resolution needed for accurate network-level characterization.

OBJECTIVE

We aim at mapping brain networks with high spatial and temporal precision and to assess whether the occurrence of specific network-level-states impact TMS outcome. To this end, we will identify large-scale brain networks and explore how their dynamics relates to corticospinal excitability.

METHODS

This study leverages Hidden Markov Models to identify large-scale brain states from pre-stimulus source space high-density-EEG data collected during TMS targeting the left primary motor cortex in twenty healthy subjects. The association between states and fMRI-defined RSNs was explored using the Yeo atlas, and the trial-by-trial relation between states and corticospinal excitability was examined.

RESULTS

We extracted fast-dynamic large-scale brain states with unique spatiotemporal and spectral features resembling major RSNs. The engagement of different networks significantly influences corticospinal excitability, with larger motor evoked potentials when baseline activity was dominated by the sensorimotor network.

CONCLUSIONS

These findings represent a step forward towards characterizing brain network in EEG-TMS with both high spatial and temporal resolution and underscore the importance of incorporating large-scale network dynamics into TMS experiments.

Functionally specialized spectral organization of the resting human cortex

Ample studies across various neuroimaging modalities have suggested that the human cortex at rest is hierarchically organized along the spectral and functional axes. However, the relationship between the spectral and functional organizations of the human cortex remains largely unexplored. Here, we reveal the confluence of functional and spectral cortical organizations by examining the functional specialization in spectral gradients of the cortex. These spectral gradients, derived from functional magnetic resonance imaging data at rest using our temporal de-correlation method to enhance spectral resolution, demonstrate regional frequency biases. The grading of spectral gradients across the cortex - aligns with many existing brain maps - is found to be highly functionally specialized through discovered frequency-specific resting-state functional networks, functionally distinctive spectral profiles, and an intrinsic coordinate system that is functionally specialized. By demonstrating the functionally specialized spectral gradients of the cortex, we shed light on the close relation between functional and spectral organizations of the resting human cortex.

Spontaneous activity and functional connectivity in patients with hoarding disorder comorbid with attention-deficit/hyperactive disorder

Despite recent studies suggesting an important association of hoarder disorder (HD) and attention-deficit/hyperactive disorder (ADHD), no neuroimaging study has investigated the differences between patients with HD comorbid with ADHD and those without ADHD. This study investigated the regional spontaneous activity and functional connectivity in HD, focusing on the comorbidity with ADHD. Resting-state functional magnetic resonance imaging (MRI) data were obtained from 24 patients with HD and 31 healthy individuals. We investigated the group differences using the fractional amplitude of low-frequency fluctuation (fALFF). The altered regions in the fALFF were used as seeds in a functional connectivity analysis where we conducted group comparisons among the three groups: healthy controls (HCs), HD with ADHD (HD +ADHD), and HD without ADHD (HD -ADHD). Compared to HCs, patients with HD had a reduced fALFF in the right inferior frontal gyrus (IFG). Functional connectivity analysis revealed that patients with HD + ADHD had reduced functional connectivity between the IFG and dorsolateral prefrontal cortex (DLPFC) compared to HCs, while the HD -ADHD group was intermediate level between HD +ADHD and HCs groups. In conclusion, patients with HD have altered spontaneous activity of the IFG. Additionally, patients with HD + ADHD had significantly reduced functional connectivity between the IFG and the DLPFC. Our findings suggest the potential need to distinguish between subgroups of HD+ADHD to identify novel neurobiological models of HD that could guide future therapeutic strategies.

Seed-based resting-state connectivity as a neurosignature in fibromyalgia and depression: a narrative systematic review

Background

Major depressive disorder (MDD) often co-occur with fibromyalgia (FM), and both conditions have been associated with impaired resting state functional connectivity (rs-FC). The present systematic review aims to summarize the evidence on rs-FC in individuals with MDD and FM compared with healthy controls and explore overlapping connectivity patterns and their relationships with clinical symptoms.

Methods

A systematic search of the EMBASE, PubMed, Scopus and ScienceDirect databases was conducted according to PRISMA guidelines. Studies were included that addressed rs-FC using seed-based analysis in MDD and FM patients compared to HC. Methodological quality and risk of bias were assessed using a 13-point checklist adapted from previous neuroimaging meta-analyzes.

Results

A total of 33 articles were included in the analysis (17 with MDD and 16 with FM). The sample comprised 1,877 individuals, including 947 patients and 930 controls, with a mean age of 39.83 years. The seeds were categorized into six neural networks. Shared disruptions across MDD and FM studies have been identified in key circuits, including decreased connectivity between the insula and anterior cingulate cortex (ACC), middle frontal gyrus (MFG), superior frontal gyrus (SFG), and putamen. Increased FC was observed between the dorsolateral prefrontal cortex (DLPFC) and ACC, as well as between the thalamus and precuneus. Decreased insula-ACC connectivity correlated with greater pain intensity and catastrophizing in FM and with more severe depressive symptoms in MDD. Unique patterns of rs-FC were also observed: FM-specific changes involved the periaqueductal gray, hypothalamus, and thalamus, indicating impaired pain modulation and emotional processing. In contrast, MDD-specific changes were primarily observed in the reward, salience, and default mode networks, reflecting impaired emotional regulation. The studies showed considerable heterogeneity in the selection of seeds and study designs, which limits the feasibility of meta-analyses and underlines the need for standardized methods.

Findings

This study provides information about overlapping and distinct neural mechanisms in FM and MDD, suggesting potentially the presence of a potential neurosignature that reflects shared disruptions in pain and emotion regulation networks while highlighting unique pathways underlying their respective pathophysiology.

Differential effects of aging, Alzheimer's pathology, and APOE4 on longitudinal functional connectivity and episodic memory in older adults

BACKGROUND

Both aging and Alzheimer's disease (AD) affect brain networks, with early disruptions occurring in regions involved in episodic memory. Few studies have, however, focused on distinguishing region-specific effects of AD-biomarker negative "normal" aging and early amyloid- and tau pathology on functional connectivity. Further, longitudinal studies combining imaging, biomarkers, and cognition are rare.

METHODS

We assessed resting-state functional connectivity (rsFC) strength and graph measures in the episodic memory network including the medial temporal lobe (MTL), posteromedial cortex (PMC), and medial prefrontal cortex alongside cognition over two years. For this preregistered study, we included 100 older adults who were amyloid- and tau-negative using CSF and PET measurements to investigate "normal" aging, and 70 older adults who had longitudinal CSF data available to investigate functional changes related to early AD pathology. All participants were cognitively unimpaired older adults from the PREVENT-AD cohort. We used region of interest (ROI)-to-ROI bivariate correlations, graph analysis, and multiple regression models.

RESULTS

In the amyloid- and tau-negative sample, rsFC strength within PMC, between parahippocampal cortex and inferomedial precuneus, and between posterior hippocampus and inferomedial precuneus decreased over time. Additionally, we observed a longitudinal decrease in global efficiency. Further, there was a steeper longitudinal decrease in rsFC and global efficiency with higher baseline age particularly of parahippocampal-gyrus regions. Further, lower rsFC strength within PMC was associated with poorer longitudinal episodic memory performance. In the sample with available CSF data, a steeper increase in rsFC between anterior hippocampus and superior precuneus was related to higher baseline AD pathology. Higher MTL-PMC rsFC strength was differentially associated with episodic memory trajectories depending on APOE4 genotype.

CONCLUSIONS

Our findings suggest differential effects of aging and AD pathology. Hypoconnectivity within PMC was related to aging and cognitive decline. MTL-PMC hyperconnectivity was related to early AD pathology and cognitive decline in APOE4 carriers. Future studies should investigate more diverse samples, nonetheless, our approach allowed us to identify longitudinal functional changes related to aging and early AD pathology, enhancing cross-sectional research. Hyperconnectivity has been proposed as a mechanism related to early AD pathology before, we now contribute specific functional connections to focus on in future research.

Dynamic grouping of ongoing activity in V1 hypercolumns

Neurons' spontaneous activity provides rich information about the brain. A single neuron's activity has close relationships with the local network. In order to understand such relationships, we studied the spontaneous activity of thousands of neurons in macaque V1 and V2 with two-photon calcium imaging. In V1, the ongoing activity was dominated by global fluctuations in which the activity of majority of neurons were correlated. Neurons' activity also relied on their relative locations within the local functional architectures, including ocular dominance, orientation, and color maps. Neurons with similar preferences dynamically grouped into co-activating ensembles and exhibited spatial patterns resembling the local functional maps. Different ensembles had different strengths and frequencies. This observation was consistent across all hypercolumn-sized V1 locations we examined. In V2, different imaging sites had different orientation and color features. However, the spontaneous activity in the sampled regions also correlated with the underlying functional architectures. These results indicate that functional architectures play an essential role in influencing neurons' spontaneous activity.

Combined fMRI and eye-tracking evidence on the neural processing of visual ambiguity in photographic aesthetics

While visual ambiguity is known to play a central role in modern art, the neural correlates of its processing remain substantially unexplored in the case of aesthetic stimuli. To fill this gap, we combined eye-tracking and functional magnetic resonance imaging (fMRI) to investigate both visual exploration, and the associated brain activity and connectivity, when observing (WO) or evaluating (WE) either ambiguous (AMB+) or non-ambiguous (AMB-) artistic photographic stimuli. These manipulations highlighted more fixations (suggestive of higher loading on exploratory processes) when evaluating compared with observing, and stronger right fronto-parietal and occipito-temporal activity (possibly supporting the resolution of visual ambiguity through attentional reorienting to global vs. local aspects) when processing ambiguous compared with non-ambiguous stimuli. Task-by-stimulus type interaction analyses showed that evaluating ambiguous stimuli was specifically associated with stronger fixation-related activity in the left medial prefrontal cortex, as well as decreased connectivity from this region to its right-hemispheric homologue, possibly supporting in-depth visuospatial analyses of complex visual images. These findings pave the way for future studies addressing the role of visual ambiguity in aesthetic appreciation, as well as the factors that might ease vs. hamper its processing and resolution, and their neural correlates.

Sequential patterning of dynamic brain states distinguish Parkinson's disease patients with mild cognitive impairments

Parkinson's disease (PD) is a neurodegenerative disease which presents clinically with progressive impairments in motoric and cognitive functioning. Pathophysiologic mechanisms underlying these impairments are believed to be attributable to a breakdown in the spatiotemporal coordination of functional neural networks across multiple cortical and subcortical regions. The current investigation used resting state, functional magnetic resonance imaging (rs-fMRI) to determine whether the temporal characteristics or sequential patterning of dynamic functional network connectivity (dFNC) states could accurately distinguish among people with PD who had normal cognition (PD-NC, n = 18), those with PD who had mild cognitive impairment (PD-MCI, n = 15), and older-aged healthy control (HC, n = 22) individuals. Results indicated that the proportion of time during the rs-fMRI scan that was spent in each of three identified dFNC states (dwell time) differed among these three groups. Individuals in the PD-MCI group spent significantly more time in a dFNC state characterized by low functional network connectivity, relative to participants in both the PD-NC (p = 0.0226) and HC (p = 0.0027) cohorts and tend to spend less time in a state characterized by anti-correlated thalamo-cortical connectivity, relative to both the PD-NC (p = 0.016) and HC (p = 0.0562) groups. A machine-learning method using sequential pattern mining was also found to distinguish among the groups with moderate accuracies ranging from 0.53 to 0.80, revealing distinct sequential patterns in the temporal ordering of dFNC states. These findings underscore the potential of dFNC and sequential pattern mining as relevant methods for further exploration of the pathophysiologic underpinnings of cognitive impairment among people living with PD.

Thalamic oscillatory dysrhythmia and disrupted functional connectivity in thalamocortical loops in perinatal stroke

Periventricular venous infarction (PVI) is a subtype of perinatal stroke localized to subcortical white matter occurring before 34 weeks of gestation. An emerging body of literature has reported life-long motor impairments and compromised quality of life in patients with PVI. However, there remains a paucity of foundational knowledge regarding the underlying neurobiological mechanisms that underpin these outcomes. Recent studies (Ferradal et al. in Cereb Cortex 29:1218-1229, 2019) in brain imaging suggest that healthy development of thalamocortical connections is instrumental in coordinating brain connectivity in both prenatal and postnatal periods given the central role the thalamus and basal ganglia play in motor circuitry. Therefore, we provide a regional and cross-network approach to the analysis of interactive pathways of the thalamus, basal ganglia, and cortex to explore possible neurobiological disruptions responsible for clinical motor function in children with PVI. A resting-state fMRI protocol was administered to children with left periventricular venous infarction (PVI) (n = 23) and typically developing children (TDC) (n = 22) to characterize regional oscillatory and thalamocortical disturbances and compare them to clinical motor function. We hypothesized that PVI would affect resting-state measures of both regional and global brain function, marked by abnormally high amplitudes of regional oscillatory activity, as well as lower local and cross-network communication. Using a combination of robust functional metrics to assess spontaneous, oscillatory activity (Amplitude of Low-Frequency Fluctuations [ALFF] and fractional ALFF), as well as local (Regional Homogeneity [ReHo]) and cross-network connectivity (Degree Centrality [DC] and Functional Connectivity [FC]). We found that compared with TDC, children with PVI exhibited higher levels of ALFF, and these functional differences were associated with the severity of motor impairment. Moreover, the thalamus in children with PVI also showed lower connectivity in relaying thalamocortical pathways. These disruptions in thalamocortical pathways from the thalamus were localized to the medial prefrontal cortex (mPFC), a key hub of the default mode network). Collectively, our findings suggest that heightened levels of regional, oscillatory activity in the thalamus may disrupt more widespread thalamocortical cross-network circuity, possibly contributing to motor impairments in children with PVI.

Genetic mechanisms of dynamic functional connectivity density in diabetic retinopathy brains: a combined transcriptomic and resting-state functional magnetic resonance imaging study

Background

Diabetic retinopathy (DR) is a condition characterized by fundus lesions resulting from retinal microvascular leakage and obstruction linked to chronic progressive diabetes mellitus. Previous neuroimaging research has revealed both structural and functional changes in the brains of DR patients. Nevertheless, the variations in dynamic functional connectivity density (dFCD) within the brains of DR patients, along with the underlying molecular mechanisms connected to these changes, have yet to be fully understood.

Methods

Forty-seven diabetic retinopathy (DR) patients and 46 healthy controls (HCs) matched for sex, age, and education were recruited for this study from the Department of Ophthalmology at the Jiangxi Provincial People's Hospital. All subjects underwent resting-state functional magnetic resonance imaging scans to analyze the differences in dFCD between the two groups. Utilizing the Allen Human Brain Atlas, we conducted spatial correlation analyses integrating transcriptomic and neuroimaging data to pinpoint genes showing correlated expression levels with dFCD alterations in DR patients. Subsequently, we carried out gene enrichment, specific expression, and protein-protein interaction analyses.

Results

In comparison to the HC group, the DR group exhibited significantly reduced dFCD variability in the left anterior cingulum, left superior occipital gyrus, and right postcentral gyrus. The abnormal dFCD variability is linked to 1,318 positively and 1,318 negatively associated genes, primarily enriched for biological functions such as ion channels, synapses, and cellular junctions. Specific expression analysis revealed that these genes were distinctly expressed in Purkinje neurons, cortex, and striatum brain regions. Furthermore, protein-protein interaction (PPI) analyses indicated that these positive and negative genes could organize PPI networks with the support of respective hub genes.

Conclusion

our study identified altered dFCD variability in brain regions linked to visual and cognitive functions in DR patients. Moreover, transcriptome-neuroimaging correlation analyses revealed a spatial association between these dFCD changes and the genes with unique functional profiles. These genes were enriched in biologically significant functions and pathways, specific to certain cells and brain areas. Our research offers novel understandings of the genetic mechanisms influencing dFCD alterations in DR.

Neuroimaging Changes in the Sensorimotor Network and Visual Network in Bipolar Disorder and Their Relationship with Genetic Characteristics

Objective: Patients with bipolar disorder (BD) may exhibit common and significant changes in brain activity across different networks. Our aim was to investigate the changes in functional connectivity (FC) within different brain networks in BD, as well as their neuroimaging homogeneity, heterogeneity, and genetic variation. Methods: In this study, we analyzed the seed points and whole-brain FC of the sensorimotor network (SMN) and visual network (VN) in 83 healthy controls (HCs) and 77 BD patients, along with their genetic neuroimaging associations. Results: The results showed that, compared to HCs, BD patients exhibited abnormal FC in the SMN and VN brain regions. However, after three months of treatment, there were no significant differences in SMN and VN FC in the brain regions of the patients compared to pre-treatment levels. Enrichment analysis indicated that genes associated with changes in FC were shared among different SMN seed points, but no shared genes were found among VN seed points. Conclusions: In conclusion, changes in SMN FC may serve as a potential neuroimaging marker in BD patients. Our genetic neuroimaging association analysis may help to comprehensively understand the molecular mechanisms underlying FC changes in BD patients.

Scalp EEG predicts intracranial brain activity in humans

Inferring deep brain activity from noninvasive scalp recordings remains a fundamental challenge in neuroscience. Here, we analyzed concurrent scalp and intracranial recordings from 1918 electrode contacts across 20 patients affected by drug-resistant epilepsy undergoing intracranial depth electrode monitoring for pre-surgical evaluation to establish predictive relationships between surface and deep brain signals. Using regularized and cross-validated linear regression within subjects, we demonstrate that scalp recordings can predict spontaneous intracranial activity, with accuracy varying by region, depth, and frequency. Low-frequency signals (<12 Hz) were most predictable, with our models explaining approximately 10% of intracranial signal variance across contacts. Prediction accuracy decreased with contact depth, particularly for high-frequency signals. Using Bayesian modeling with leave-one-patient-out cross-validation, we observed generalizable prediction of activity in mesial temporal, prefrontal, and orbitofrontal cortices, explaining 10-12% of low-frequency signal variance. This scalp-to-intracranial mapping derived from spontaneous activity was further validated by its correlation with scalp responses evoked by direct electrical stimulation. These findings support the development of improved inverse models of brain activity and potentially more accurate scalp-based markers of disease and treatment response.

Abnormal brain functional networks in systemic lupus erythematosus: a graph theory, network-based statistic and machine learning study

Systemic lupus erythematosus patients' brain functional network impairments are incompletely clarified. This study investigates the brain functional network topological alterations in systemic lupus erythematosus and the application of machine learning to the classification of systemic lupus erythematosus and healthy controls. Resting-state functional MRI data from 127 systemic lupus erythematosus patients and 102 healthy controls were used. The pre-processing process involved using automated anatomical labelling atlas to compute time series data for 116 brain regions. A functional connectivity network was then created by assessing the Pearson correlation between the time series of these brain regions. The GRETNA toolbox was used to compute the difference in topological attributes between groups. Variations in regional networks among groups were evaluated using non-parametric permutation tests that rely on network-based statistical analysis. With the functional connectivity network metrics as features and network-based statistical analysis as the feature selection method, network-based statistical analysis Predict software was used to classify systemic lupus erythematosus from controls by support vector machine. The subnets that contributed the most to systemic lupus erythematosus classification were also identified. For global indicators, the systemic lupus erythematosus group exhibited significantly lower values for the normalized clustering coefficient (P = 0. 0317) and small-world index (P = 0.0364) compared to the healthy controls group. After false discovery rate correction, the differences in Betweeness Centrality, Degree Centrality, Node Efficiency, Node Local Efficiency and other local indexes between the two groups were not retained. No correlation was found between clinical data and network indicators. Systemic lupus erythematosus group had a significantly reduced connection with a 12-node, 11-edge subnetwork (P = 0.024). In conclusion, systemic lupus erythematosus patients exhibit suboptimal global brain functional connectivity network topology and the presence of a subnetwork with abnormally reduced connectivity.

Human Sensorimotor Cortex Reactivates Recent Visuomotor Experience during Awake Rest

The re-emergence of task-related activation patterns during awake rest has been reported to play a role in memory consolidation and perceptual learning. This study aimed to test whether such reactivation occurs in the primary sensorimotor cortex following a visuomotor task. During functional magnetic resonance imaging (fMRI) scanning, 42 healthy participants (13 women and 29 men) learned visuomotor tracking, while a rotational perturbation was introduced between the cursor position and joystick angle. This visuomotor task block was interleaved with a control block, during which participants passively viewed a replay of their previously performed cursor movements. Half of the participants used their right hand, whereas the other half used their left hand to control the joystick. Resting-state scans were acquired before and after the visuomotor task sessions. A multivariate pattern classifier was trained to classify task and control blocks and was then tested on resting-state scans collected before and after the task session. Results revealed a significant increase in the number of volumes classified as "task" during post-task rest compared with pre-task rest, indicating re-emergence of task-related activity. Representational similarity analysis also showed a greater similarity to task-related patterns during the post-task rest period. Furthermore, this effect was specific to the left primary sensorimotor cortex contralateral to the hand used and significantly correlated with motor improvement following rest. Our findings reveal the reactivation of recent task-related experience in the primary sensorimotor cortex.

Reduced local functional connectivity correlates with atypical performances in children with autism spectrum disorder

To characterize local functional connectivity (FC) differences in children with autism spectrum disorder (ASD) compared to typically developed (TD) children, and to analyze the correlation between local FC and the atypical behavior in autistic children. Thirty children with ASD and 25 TD children were recruited. Participants underwent rs-fMRI scans, and regional homogeneity (ReHo) of specific brain regions was measured. Performance was assessed using the Autism Behavior Checklist (ABC) and the Gesell Development Diagnosis Scale (GDDS). Children with ASD demonstrated reduced ReHo in the right occipital lobe lingual, left postcentral, and left precuneus compared with TD children. Within the ASD group, the ABC total score was negatively related to ReHo values in both the left postcentral and left precuneus. The ReHo value in the left postcentral was negatively correlated with ABC scores related to sensory and body/object use, while the ReHo value in the left precuneus was negatively correlated with scores related to social skills and self-help. The mean Developmental Quotient (DQ) of GDDS was positively correlated with the ReHo value in the right occipital lobe lingual. Besides, the ReHo value in this region was positively correlated with the DQ of adaptive behavior. The ReHo value in the left postcentral was positively correlated with the DQ of fine motor skills (p < 0.05 for all). Children with ASD exhibit reduced local FC in specific brain regions, which are associated with specific performances in autism. These findings may provide a novel insight into the pathophysiological mechanisms of ASD.

Impaired brain ability of older adults to transit and persist to latent states with well-organized structures at wakeful rest

The intrinsic brain functional network organization continuously changes with aging. By integrating spatial and temporal information, the process of how brain networks temporally reconfigure and remain well-organized spatial structure largely reflects the brain function, thereby holds the potential to capture its age-related declines. In this study, we examined the spatiotemporal brain dynamics from resting-state functional Magnetic Resonance Imaging (fMRI) data of healthy young and older adults using a Hidden Markov Model (HMM). Six brain states were generated by HMM, with the young group showing higher fractional occupancy and mean dwell time in states 1, 3, and 4 (SY1, SY2 and SY3), and the older group in states 2, 5, and 6 (SO1, SO2 and SO3). Importantly, comparisons of transition probabilities revealed that the older group showed a reduced brain ability to transition into states dominated by the younger group, as well as a diminished capacity to persist in them. Moreover, graph analysis revealed that these young-specific states exhibited higher modularity and k-coreness. Collectively, these findings suggested that the older group showed impaired brain ability of both transition into and sustain well spatially organized states. This emphasized that the temporal changes in brain state organization, rather than its static mode, could be a key biomarker for detecting age-related functional decline. These insights may pave the way for targeted interventions aimed at mitigating cognitive decline in the aging population.

A functional parcellation of the whole brain in high-functioning individuals with autism spectrum disorder reveals atypical patterns of network organization

Researchers studying autism spectrum disorder (ASD) lack a comprehensive map of the functional network topography in the ASD brain. We used high-quality resting state functional MRI (rs-fMRI) connectivity data and a robust parcellation routine to provide a whole-brain map of functional networks in a group of seventy high-functioning individuals with ASD and a group of seventy typically developing (TD) individuals. The rs-fMRI data were collected using an imaging sequence optimized to achieve high temporal signal-to-noise ratio (tSNR) across the whole-brain. We identified functional networks using a parcellation routine that intrinsically incorporates internal consistency and repeatability of the networks by keeping only network distinctions that agree across halves of the data over multiple random iterations in each group. The groups were tightly matched on tSNR, in-scanner motion, age, and IQ. We compared the maps from each group and found that functional networks in the ASD group are atypical in three seemingly related ways: (1) whole-brain connectivity patterns are less stable across voxels within multiple functional networks, (2) the cerebellum, subcortex, and hippocampus show weaker differentiation of functional subnetworks, and (3) subcortical structures and the hippocampus are atypically integrated with the neocortex. These results were statistically robust and suggest that patterns of network connectivity between the neocortex and the cerebellum, subcortical structures, and hippocampus are atypical in ASD individuals.

Common and unique network basis for externally and internally driven flexibility in cognition: From a developmental perspective

Flexibility is a hallmark of cognitive control and can be driven externally and internally, corresponding to reactive and spontaneous flexibility. However, the convergence and divergence between these two types of flexibility and their underlying neural basis during development remain largely unknown. In this study, we aimed to determine the common and unique networks for reactive and spontaneous flexibility as a function of age and sex, leveraging both cross-sectional and longitudinal resting-state functional magnetic resonance imaging datasets with different temporal resolutions (N = 249, 6-35 years old). Functional connectivity strength and nodal flexibility, derived from static and dynamic frameworks respectively, were utilized. We found similar quadratic effects of age on reactive and spontaneous flexibility, which were mediated by the functional connectivity strength and nodal flexibility of the frontoparietal network. Divergence was observed, with the nodal flexibility of the ventral attention network at the baseline visit uniquely predicting the increase in reactive flexibility 24-30 months later, while the nodal flexibility or functional connectivity strength of the dorsal attention network could specifically predict the increase in spontaneous flexibility. Sex differences were found in tasks measuring reactive and spontaneous flexibility simultaneously, which were moderated by the nodal flexibility of the dorsal attention network. This study advances our understanding of distinct types of flexibility in cognition and their underlying mechanisms throughout developmental stages. Our findings also suggest the importance of studying specific types of cognitive flexibility abnormalities in developmental neuropsychiatric disorders.

Unraveling the mesoscale functional connectivity of the human primary visual cortex using high-resolution functional MRI

Despite their importance in shaping visual perception, functional connectivity between ocular dominance columns (ODCs), the building blocks of neuronal processing within the human primary visual cortex (V1), remains largely unknown. Using high-resolution functional MRI (fMRI), we localized ODCs and assessed their resting-state functional connectivity (rs-FC) in 11 human adults (3 females). Consistent with anatomical studies in animals, we found stronger rs-FC in the middle compared to deep and superficial cortical depths and selectively stronger rs-FC between ODCs with alike compared to unalike ocular polarity. Beyond what was known from animal models, and consistent with human perceptual biases, we found stronger intra- and interhemispheric rs-FC in peripheral (compared to central) and in dorsal (compared to ventral) V1 subregions. Lastly, we found a significant correlation between rs-FC patterns and ODC maps, supporting the hypothesis that ODC maps can be predicted from rs-FC patterns within V1. These results highlight the heterogeneity in functional connectivity between ODCs across cortical depths and V1 subfields, underscoring their likely association with human perceptual biases.

Significance Statement

Our findings provide evidence for selective mesoscale rs-FC between ODCs, aligning with prior anatomical findings in animals.Beyond what is known from animal studies, we demonstrate that the mesoscale rs-FC pattern varies across V1 subregions, aligning with the expected heterogeneity in global visual processing across visual subfields.We provide evidence for the predictability of ODC maps from the rs-FC pattern, establishing one of the first steps toward leveraging rs-FC for segmentation of the visual cortex at mesoscale levels.

Association between functional alterations and specific transcriptional expression patterns in craniocervical dystonia

PURPOSE

Craniocervical dystonia (CCD) is a large-scale network disorder that involves functional changes in multiple brain regions. However, the association between these functional changes and the underlying molecular mechanisms has not been explored.

OBJECTIVE

We aimed to characterize the molecular changes associated with the imaging-defined functional architecture of the brain in CCD.

METHODS

Resting-state functional magnetic resonance imaging (rs-fMRI) data were obtained from 146 patients with CCD and 137 healthy controls (HCs). Differences in the amplitude of low-frequency fluctuations (ALFF), fractional ALFF (fALFF), and regional homogeneity (ReHo) were compared between groups. Transcriptomic data were obtained from the Allen Human Brain Atlas to identify the gene expression patterns underlying the affected functional architecture in CCD using partial least squares regression.

RESULTS

Compared to HCs, patients with CCD showed common functional alterations, mainly in the left middle occipital gyrus, right middle occipital gyrus, right calcarine, right precentral gyrus, and left postcentral gyrus. These functional alteration patterns were positively associated with 1763 genes (including five risk genes for dystonia) enriched for synaptic signaling, regulation of trans-synaptic signaling, and neuronal systems, while they were negatively associated with 2318 genes (including eight risk genes for dystonia), which were enriched for monoatomic cation transport, DNA damage response and neurodevelopment.

CONCLUSIONS

Our study reveals a genetic pathological mechanism explaining CCD-related brain functional changes.

Volumetric mesoscopic electrophysiology: a new imaging modality for the nonhuman primate

The primate brain is a densely interconnected organ whose function is best understood by recording from the entire structure in parallel, rather than parts of it in sequence. However, available methods either have limited temporal resolution (functional magnetic resonance imaging; fMRI), limited spatial resolution (macroscopic electroencephalography), or a limited field of view (microscopic electrophysiology). To address this need, we developed a volumetric, mesoscopic recording approach (MePhys) by tessellating the volume of a monkey hemisphere with 992 electrode contacts that were distributed across 62 chronically implanted multielectrode shafts. We showcase the scientific promise of MePhys by describing the functional interactions of local field potentials between the more than 300,000 simultaneously recorded pairs of electrodes. We find that a subanesthetic dose of ketamine-believed to mimic certain aspects of psychosis-can create a pronounced state of functional disconnection and prevent the formation of stable large-scale intrinsic states. We conclude that MePhys provides a new and fundamentally distinct window into brain function whose unique profile of strengths and weaknesses complements existing approaches in synergistic ways.NEW & NOTEWORTHY We created a new imaging modality for the nonhuman primate, mesoscopic electrophysiology, or MePhys by sampling local field potentials (LFPs) in a dense three-dimensional grid from across the volume of one entire hemisphere. MePhys combines the millisecond temporal resolution of electrophysiology with the large field of view and millimeter spatial resolution of functional magnetic resonance imaging (fMRI). MePhys' unique profile of strengths and limitations makes it an ideal imaging method for the nonhuman primate brain observatories of the future.

Evaluating the evolution and inter-individual variability of infant functional module development from 0 to 5 yr old

The segregation and integration of infant brain networks undergo tremendous changes due to the rapid development of brain function and organization. In this paper, we introduce a novel approach utilizing Bayesian modeling to analyze the dynamic development of functional modules in infants over time. This method retains inter-individual variability and, in comparison with conventional group averaging techniques, more effectively detects modules, taking into account the stationarity of module evolution. Furthermore, we explore gender differences in module development under awake and sleep conditions by assessing modular similarities. Our results show that female infants demonstrate more distinct modular structures between these 2 conditions, possibly implying relative quiet and restful sleep compared with male infants.

Pre-choice midbrain fluctuations affect self-control in food choice: A functional magnetic resonance imaging (fMRI) study

Recent studies have shown that spontaneous pre-stimulus fluctuations in brain activity affect higher-order cognitive processes, including risky decision-making, cognitive flexibility, and aesthetic judgments. However, there is currently no direct evidence to suggest that pre-choice activity influences value-based decisions that require self-control. We examined the impact of fluctuations in pre-choice activity in key regions of the reward system on self-control in food choice. In the functional magnetic resonance imaging (fMRI) scanner, 49 participants made 120 food choices that required self-control in high and low working memory load conditions. The task was designed to ensure that participants were cognitively engaged and not thinking about upcoming choices. We defined self-control success as choosing a food item that was healthier over one that was tastier. The brain regions of interest (ROIs) were the ventral tegmental area (VTA), putamen, nucleus accumbens (NAc), and caudate nucleus. For each participant and condition, we calculated the mean activity in the 3-s interval preceding the presentation of food stimuli in successful and failed self-control trials. These activities were then used as predictors of self-control success in a fixed-effects logistic regression model. The results indicate that increased pre-choice VTA activity was linked to a higher probability of self-control success in a subsequent food-choice task within the low-load condition, but not in the high-load condition. We posit that pre-choice fluctuations in VTA activity change the reference point for immediate (taste) reward evaluation, which may explain our finding. This suggests that the neural context of decisions may be a key factor influencing human behavior.

Disruptions of resting-state functional connectivity in post-stroke motor dysfunctions: a meta-analysis

This study aims to unravel the consistent abnormalities in functional connectivity (FC) with the primary motor cortex (M1) for post-stroke motor dysfunctions and the dynamic shifts of FC across distinct phases (acute/subacute/chronic) following stroke onset. Eleven studies with 269 stroke patients and 257 healthy controls (HCs) were included after screening articles in PubMed, Web of Science, and Embase. Voxel-wise meta-analysis and subgroup analysis on three phases after stroke onset were applied using the anisotropic effect size-signed differential mapping toolbox. Additionally, a M1-seeded FC analysis from an independent dataset with 29 stroke patients and 40 HCs was applied to validate the results of the meta-analyses. The abnormal connectivity with M1 in patients with post-stroke motor dysfunctions extended beyond motor-related regions to non-motor domains. A consistent interhemispheric connectivity reduction between M1 and motor-related regions emerged as a hallmark, persisting across different phases after stroke onset. These alterations were largely replicable through validation analysis. Our findings indicated the imbalance of connectivity in patients with post-stroke motor dysfunctions.

Increased functional connectivity of motor regions and dorsolateral prefrontal cortex in musicians with focal hand dystonia

BACKGROUND

Musician's dystonia is the most common form of focal task-specific dystonia and is suggested to be the result of dysfunctional communication among sensory-motor networks. Thus far, few functional connectivity studies have investigated musician's dystonia specifically, leaving its exact pathophysiological mechanisms unclear. The goal of this study was to verify connectivity findings from other task-specific dystonias on a large sample of musician's hand dystonia patients and to analyze associations with possible adverse childhood experiences, a suggested risk factor for dystonia.

METHODS

Forty professional musicians suffering from musician's hand dystonia and a matched control group of healthy musicians underwent resting-state functional magnetic resonance imaging and answered the childhood trauma questionnaire. Using a seed-to-whole brain approach, functional connectivity alterations between motor cortices, the prefrontal cortex, the basal ganglia and the thalamus were analyzed.

RESULTS

Musician's dystonia patients showed increased functional connectivity of the dorsolateral prefrontal cortex with the putamen and the pallidum, especially in right-side affected patients. Patients further displayed increased connectivity of the left thalamus and the right lateral premotor cortex. No associations between functional connectivity, duration of disorder and childhood adversity were observed.

CONCLUSION

The findings are consistent with previous research, highlighting the pathophysiological importance of the basal ganglia. Altered resting-state functional connectivity may reflect underlying neuroplastic changes in musicians with dystonia that lead to an altered flow of information, disrupting movement inhibition. Involvement of the dorsolateral prefrontal and premotor cortices further suggests that motor disturbances occur in the early planning phase of a movement. The findings indicate that a holistic re-training approach with and without the instrument could be beneficial for regaining motor control.

Translational network neuroscience: Nine roadblocks and possible solutions

Translational network neuroscience aims to integrate advanced neuroimaging and data analysis techniques into clinical practice to better understand and treat neurological disorders. Despite the promise of technologies such as functional MRI and diffusion MRI combined with network analysis tools, the field faces several challenges that hinder its swift clinical translation. We have identified nine key roadblocks that impede this process: (a) theoretical and basic science foundations; (b) network construction, data interpretation, and validation; (c) MRI access, data variability, and protocol standardization; (d) data sharing; (e) computational resources and expertise; (f) interdisciplinary collaboration; (g) industry collaboration and commercialization; (h) operational efficiency, integration, and training; and (i) ethical and legal considerations. To address these challenges, we propose several possible solution strategies. By aligning scientific goals with clinical realities and establishing a sound ethical framework, translational network neuroscience can achieve meaningful advances in personalized medicine and ultimately improve patient care. We advocate for an interdisciplinary commitment to overcoming translational hurdles in network neuroscience and integrating advanced technologies into routine clinical practice.

Disease-specific alterations of effective connectivity across anti-correlated networks in major depressive disorder and bipolar disorder

Major depressive disorder (MDD) and bipolar disorder (BD) share various clinical behaviors and have confounded clinical diagnoses. Converging studies have suggested MDD and BD as disorders with abnormal communication among functional brain networks involved in mental activity and redirection. However, whether MDD and BD show disease-specific alterations in network information interaction remains unclear. This study collected resting-state functional MRI data of 98 patients with MDD, 55 patients with BD, and sex-, age-, and education-matched 95 healthy controls. Spectral dynamic causal model (spDCM) was used to investigate effective connectivities among three large-scale intrinsic functional networks including the default mode network (DMN), salience network (SN), and dorsal attention network (DAN). Effective connectivities showing disease-specific changes were then used as input features of support vector models to predict clinical symptoms and classify individuals with MDD and BD. Compared with healthy controls, both the MDD and BD groups showed increased DAN → SN connectivity. However, within-network connectivities of DMN and DAN showed opposite effects on the diseases. Notably, MDD and BD also showed different alterations on a connectivity loop of SN → DAN → DMN → SN, which could be used to predict the clinical symptom severity of either MDD or BD. Individuals with MDD and BD could be further classified by using connectivities showing opposite disease effects. Our findings reveal common and unique alterations of network interactions in MDD and BD, and further suggest disease-specific neuroimaging markers for clinical diagnosis.

Mood symptoms, cognitive function, and changes of brain hemodynamics in patients with COVID-19: A functional near-infrared spectroscopy study

BACKGROUND

Many patients with coronavirus disease 2019 (COVID-19) may experience emotional issues and cognitive impairment. However, it remains unclear whether the brain mediates the impact of COVID-19 on the emergence of psychopathological symptoms. It remains unclear whether anxiety and depression are caused by stressors or viral infection.

AIM

To use functional near-infrared spectroscopy (fNIRS) to detect cortical hemodynamic changes in patients with COVID-19 and their relationship with mental symptoms (mainly depression and anxiety), to investigate whether COVID-19 causes these changes by affecting brain function.

METHODS

A total of 58 subjects, comprising 29 patients with first acute COVID-19 infection and 29 healthy controls without COVID-19 infection and without anxiety or depression were recruited. Then cortical activation during the performance of the verbal fluency test (VFT) and brain connectivity during the resting state (rs) were evaluated by 53-channel fNIRS. For the COVID-19-infected group, Patient Health Questionnaire-9 (PHQ-9) and General Anxiety Disorder-7 (GAD-7) were used to assess the emotional state before fNIRS measures.

RESULTS

For the rs, compared to the uninfected group, the infected group exhibited lower rs functional connectivity (FC) in the dorsolateral prefrontal cortex (DLPFC), which was correlated with both the PHQ score and GAD score. During the VFT, the infected group exhibited significantly lower cortical activation than the uninfected group in both Broca-left and Broca-right. Besides, the integral value in the DLPFC-L showed a significant negative correlation with the PHQ-9 score during the VFT in the infected group.

CONCLUSION

There were significant differences in the bilateral Broca area and DLPFC between the COVID-19-infected and uninfected groups, which may be the reason why COVID-19 infection impairs cognitive function and language function and leads to psychiatric symptoms. In addition, the rsFC in patients with COVID-19 was positively correlated with the severity of depression and anxiety, which may be related to the fact that the mental symptoms of patients with COVID-19 are characterized by depression and anxiety, rather than depression or anxiety alone. Our study provides evidence that the psychological and emotional issues caused by COVID-19 are not only due to external social factors but also involve more direct brain neural mechanisms and abnormal neural circuits, which also provide insights into the future treatment and prognosis of individuals with COVID-19.

Brain-wide dynamic coactivation states code for hand movements in the resting state

Resting brain activity, in the absence of explicit tasks, appears as distributed spatiotemporal patterns that reflect structural connectivity and correlate with behavioral traits. However, its role in shaping behavior remains unclear. Recent evidence shows that resting-state spatial patterns not only align with task-evoked topographies but also encode distinct visual (e.g., lines, contours, faces, places) and motor (e.g., hand postures) features, suggesting mechanisms for long-term storage and predictive coding. While prior research focused on static, time-averaged task activations, we examine whether dynamic, time-varying motor states seen during active hand movements are also present at rest. Three distinct motor activation states, engaging the motor cortex alongside sensory and association areas, were identified. These states appeared both at rest and during task execution but underwent temporal reorganization from rest to task. Thus, resting-state dynamics serve as strong spatiotemporal priors for task-based activation. Critically, resting-state patterns more closely resembled those associated with frequent ecological hand movements than with an unfamiliar movement, indicating a structured repertoire of movement patterns that is replayed at rest and reorganized during action. This suggests that spontaneous neural activity provides priors for future movements and contributes to long-term memory storage, reinforcing the functional interplay between resting and task-driven brain activity.

Comparative Effects of Temporal Interference and High-Definition Transcranial Direct Current Stimulation on Spontaneous Neuronal Activity in the Primary Motor Cortex: A Randomized Crossover Study

Background: Modulating spontaneous neuronal activity is critical for understanding and potentially treating neurological disorders, yet the comparative effects of different non-invasive brain stimulation techniques remain underexplored. Objective: This study aimed to systematically compare the effects of temporal interference (TI) stimulation and high-definition transcranial direct current stimulation (HD-tDCS) on spontaneous neuronal activity in the primary motor cortex. Methods: In a randomized, crossover design, forty right-handed participants underwent two 20 min sessions of either TI or HD-tDCS. Resting-state fMRI data were collected at four stages: pre-stimulus baseline (S1), first half of stimulation (S2), second half of stimulation (S3), and post-stimulation (S4). We analyzed changes in regional homogeneity (ReHo), dynamic ReHo (dReHo), fractional amplitude of low-frequency fluctuations (fALFFs), and dynamic fALFFs (dfALFFs) to assess the impact on spontaneous neuronal activity. Results: The analysis revealed that TI had a more significant impact on ReHo, especially in the left superior temporal gyrus and postcentral gyrus, compared with HD-tDCS. Both stimulation methods exhibited their strongest effects during the second half of the stimulation period, but only TI maintained significant activity in the post-stimulation phase. Additionally, both TI and HD-tDCS enhanced fALFFs in real-time, with TI showing more pronounced effects in sensorimotor regions. Conclusions: These findings suggest that TI exerts a more potent and sustained influence on spontaneous neuronal activity than HD-tDCS. This enhanced understanding of their differential effects provides valuable insights for optimizing non-invasive brain stimulation protocols for therapeutic applications.