Juvenile myoclonic epilepsy (JME) exhibits abnormal functional connectivity of brain networks at multiple frequencies. We used the multilayer network model to address the heterogeneous features at different frequencies and assess the mechanisms of functional integration and segregation of brain networks in JME patients. To address the possibility of false edges or missing edges during network construction, we combined multilayer networks with link prediction techniques. Resting-state functional magnetic resonance imaging (rs-fMRI) data were procured from 40 JME patients and 40 healthy controls. The Multilayer Network framework is utilized to integrate information from different frequency bands and to fuse similarity metrics for link prediction. Finally, calculate the entropy of the multiplex degree and multilayer clustering coefficient of the reconfigured multilayer frequency network. The results showed that the multilayer brain network of JME patients had significantly reduced ability to integrate and separate information and significantly correlated with severity of JME symptoms. This difference was particularly evident in default mode network (DMN), motor and somatosensory network (SMN), and auditory network (AN). In addition, significant differences were found in the precuneus, suboccipital gyrus, middle temporal gyrus, thalamus, and insula. Results suggest that JME patients have abnormal brain function and reduced cross-frequency interactions. This may be due to changes in the distribution of connections within and between the DMN, SMN, and AN in multiple frequency bands, resulting in unstable connectivity patterns. The generation of these changes is related to the pathological mechanisms of JME and may exacerbate cognitive and behavioral problems in patients.

The online version contains supplementary material available at 10.1007/s11571-024-10191-0.

Extensive neuroimaging studies have established that functional abnormalities and morphological alterations in the nucleus accumbens (NAc) are implicated in major depressive disorder (MDD), but changes in its white matter structural connectivity (SC) remain unclear. We aimed to elucidate the changes in the white matter fibre connectivity of the NAc in MDD patients.

This study used probabilistic fibre tracking to analyze the diffusion tensor imaging (DTI) data of 125 MDD patients and 129 healthy controls (HCs), calculating the strength of SC (sSC) from bilateral NAc to the entire brain and its correlation with depressive symptoms.

Compared to HCs, MDD exhibited increased sSC between the left NAc (L.NAc) and regions involving the left middle frontal gyrus, bilateral cingulate gyrus (CG), bilateral hippocampus, left caudate, left medial superior occipital gyrus, right globus pallidus, right superior and middle temporal gyrus, right precuneus, right insula, and right posterior parietal thalamus. Enhanced sSC was also observed between the right NAc (R.NAc) and the left temporal lobe, left posterior superior temporal sulcus (pSTS), bilateral lateral occipital cortex, left hippocampus, right putamen and right ventral occipital cortex. The sSC of L.NAc-left CG and R.NAc-left pSTS was positively correlated with HAMD scores in MDD.

Abnormal white matter connectivity of the NAc primarily affects the cortico-limbic circuit, cortico-basal ganglia circuit, and the temporal-occipital cortical regions in patients with MDD, along with the asymmetrical features of the inter-hemispheric SC related to NAc. These alteration may underlie the dysfunction of reward processing and emotion regulation in MDD.

Internet gaming disorder (IGD) is a pervasive global mental health issue, and finding effective treatments for the disorder has been challenging. Mindfulness meditation (MM), recognized for its holistic approach that involves integrating mental and physical facets, holds promise for addressing the multifaceted nature of addiction. Nevertheless, the effect of MM on IGD and its associated neural networks, particularly in terms of their dynamic characteristics, remains elusive.

A total of 61 eligible participants with IGD (30 in the MM group, 31 in the progressive muscle relaxation [PMR] group) completed the experimental protocol, which involved pretest, an 8-session MM/PMR training regimen, and posttests. The 142 brain regions of interest were categorized into 5 brain networks using dynamic network reconfiguration analysis based on Shen's functional template. A comparative analysis of network dynamic features, including recruitment and integration coefficients, was performed across different groups and tests using resting-state functional magnetic resonance imaging data.

While clinically nonspecific effects were observed in the PMR group, the MM group exhibited a significant reduction in addiction severity and cravings. In the dynamic brain network, MM training increased the recruitment coefficient within the frontoparietal network (FPN) and basal ganglia network (BGN) but decreased it within the default mode network (DMN). Furthermore, MM training increased the integration coefficient in the FPN-DMN and DMN-limbic network (LN).

MM has demonstrated pronounced efficacy in treating IGD. MM may enhance top-down control functions, cognitive and emotional functions, and reward-system processing, potentially through the reconfiguration of the FPN-DMN pathway, DMN-LN pathway, and BGN.

Regional neural response and network properties have traditionally been studied separately. However, growing evidence suggests a close interplay between regional activity and inter-regional connectivity. This study aimed to examine the relationship between global functional connectivity and regional spontaneous activity, termed the global-to-local relationship.

Resting-state fMRI data were parcellated using MOSI (modular analysis and similarity measurements), enabling multi-resolution functional partitioning. For each parcellated cluster, the mean amplitude of low-frequency fluctuations (node power) and its average functional connectivity with the remaining cortex (node strength) were computed. Correlation analyses assessed their relationship. A supplementary analysis was conducted on EEG data (1-30 Hz).

A significant negative correlation between node strength and regional power was observed in MRI datasets. One-sample t-tests confirmed robustness across different MOSI resolutions, with individual P values at the level 10-4 to 10-5. The negative relationship was also found in EEG data but was restricted to delta (1-4 Hz) and theta (4-8 Hz) bands.

This study introduces two key novel aspects. First, it applies MOSI to the entire cortex, enhancing the comprehensiveness of network analysis. Second, it examines the global influence on regional neural activity, rather than limiting the focus to a specific network.

A robust negative relationship between node strength and node power was consistently observed across both MRI and EEG datasets, particularly in lower frequency bands (up to 8 Hz). These findings suggest a framework for investigating how global connectivity shapes regional neural activity, with inhibitory coupling as a potential underlying mechanism.

Binge Drinking (BD) is characterized by consuming large amounts of alcohol on one occasion, posing risks to brain function. Nonetheless, it remains the most prevalent consumption pattern among students. Cross-sectional studies have explored the relationship between BD and anomalies in resting-state functional connectivity (RS-FC), but the medium/long-term consequences of BD on RS-FC during developmental periods remain relatively unexplored. In this two-year follow-up study, the impact of sustained BD on RS-FC was investigated in 44 college students (16 binge-drinkers) via two fMRI sessions at ages 18-19 and 20-21. Using a seed-to-voxel approach, RS-FC differences were examined in nodes of the main brain functional networks vulnerable to alcohol misuse, according to previous studies. Group differences in RS-FC were observed in four of the explored brain regions. Binge drinkers, compared to the control group, exhibited, at the second assessment, decreased connectivity between the right SFG (executive control network) and right precentral gyrus, the ACC (salience network) and right postcentral gyrus, and the left amygdala (emotional network) and medial frontal gyrus/dorsal ACC. Conversely, binge drinkers showed increased connectivity between the right Nacc (reward network) and four clusters comprising bilateral middle frontal gyrus (MFG), right middle cingulate cortex, and right MFG extending to SFG. Maintaining a BD pattern during critical neurodevelopmental years impacts RS-FC, indicating mid-to-long-term alterations in functional brain organization. This study provides new insights into the neurotoxic effects of adolescent alcohol misuse, emphasizing the need for longitudinal studies addressing the lasting consequences on brain functional connectivity.

Neuropsychiatric and neurodegenerative disorders involve diverse changes in brain functional connectivity. As an alternative to approaches that search for specific mosaic patterns of affected connections and networks, we used polyconnectomic scoring to quantify disorder-related whole-brain connectivity signatures into interpretable, personalized scores.

The polyconnectomic score (PCS) measures the extent to which an individual's functional connectivity mirrors the whole-brain circuitry characteristics of a trait. We computed PCSs for 8 neuropsychiatric conditions (attention-deficit/hyperactivity disorder, anxiety-related disorders, autism spectrum disorder, obsessive-compulsive disorder, bipolar disorder, major depressive disorder, schizoaffective disorder, and schizophrenia) and 3 neurodegenerative conditions (Alzheimer's disease, frontotemporal dementia, and Parkinson's disease) across 22 datasets with resting-state functional magnetic resonance imaging data from 10,667 individuals (5325 patients, 5342 control participants). We also examined PCSs in 26,673 individuals from the population-based UK Biobank cohort.

PCSs were consistently higher in out-of-sample patients across 6 of the 8 neuropsychiatric and across all 3 investigated neurodegenerative disorders ([minimum, maximum]: area under the receiver operating characteristic curve = [0.55, 0.73], false discovery rate-corrected p [pFDR] = [1.8 × 10-16, 4.5 × 10-2]). Individuals with elevated PCS levels for neuropsychiatric conditions exhibited higher neuroticism (pFDR < 9.7 × 10-5), lower cognitive performance (pFDR < 5.3 × 10-5), and lower general well-being (pFDR < 9.7 × 10-4).

Our findings reveal generalizable whole-brain connectivity alterations in brain disorders. Polyconnectomic scoring effectively aggregates disorder-related signatures across the entire brain into an interpretable, participant-specific metric. A toolbox is provided for PCS computation.

Excessive sedentary time (ST) is linked to dementia risk, poorer attentional control and episodic memory. These cognitive decrements have been associated with decreased functional connectivity (FC) in the frontoparietal network (FPN) and default mode networks (DMN) with ageing. Physical activity (PA) interventions can enhance FC in these networks, but these interventions are not designed to decrease ST among older adults. Prolonged sitting (ie, sitting continuously for ≥20 min) can acutely reduce frontoparietal brain function and attentional control, while a single PA bout lasting at least 20 min can enhance them. It has been theorised that stimulation of the cerebral norepinephrine release through peripheral increase in catecholamines may explain this effect. In contrast, the effects of shorter (<10 min) PA bouts used to interrupt prolonged sitting on neurocognitive functions remain poorly understood. This pilot randomised crossover feasibility trial capitalises on PA intensity as the major limiting factor in peripheral catecholamine increase and tests the effects of interrupting prolonged sitting every 30 min with 6 min high-intensity interval training (HIIT) compared with low-intensity interval training (LIIT) bouts. The study will address three aims: (1) to assess feasibility, acceptability, fidelity and safety of HIIT breaks to improve neurocognitive function in middle-aged and older adults; (2) to quantify the differences between conditions in the change in the amplitude and latency of the P3b component of event-related potentials (a marker for frontoparietal function) and (3) to explore the differences between conditions in attentional control, episodic memory and FC of the FPN and DMN in middle-aged and older adults.

54 healthy adults, aged 40-75 years, will be recruited from the local community and randomly assigned to a condition sequence (HIIT, LIIT vs LIIT and HIIT). Each HIIT bout comprises a 1 min warm-up, 2 min at 90% of the maximum heart rate (HRmax), 1 min passive rest and 2 min at 90% HRmax. During 2 min intervals in LIIT, participants exercise at 57%-60% of HRmax. The primary outcomes include the feasibility (recruitment and retention rates, percentage of valid electroencephalogram data), acceptability of time commitment, HIIT bouts and neurocognitive assessments, fidelity (the intensity of HIIT breaks, percentage of time spent sitting) and the amplitude and the latency of the P3b component of event-related brain potentials measured during the modified Eriksen flanker task at pretests, after the first and the third PA bout and at post-test. General linear mixed-effects models will be used to test the effects of the intervention on the P3b component.

The Institutional Review Board at the University of Illinois Urbana-Champaign provided the ethical approval for the study. Findings will be disseminated in peer-reviewed journals and at scientific conferences.

NCT06243016.

Low back pain (LBP) is a prevalent pain condition whose persistence can lead to changes in the brain regions responsible for sensory, cognitive, attentional, and emotional processing. Previous neuroimaging studies have identified various structural and functional abnormalities in patients with LBP; however, how the static and dynamic large-scale functional network connectivity (FNC) of the brain is affected in these patients remains unclear. Forty-one patients with chronic low back pain (cLBP) and 42 healthy controls underwent resting-state functional MRI scanning. The independent component analysis method was employed to extract the resting-state networks. Subsequently, we calculate and compare between groups for static intra- and inter-network functional connectivity. In addition, we investigated the differences between dynamic functional network connectivity and dynamic temporal metrics between cLBP patients and healthy controls. Finally, we tried to distinguish cLBP patients from healthy controls by support vector machine method. The results showed that significant reductions in functional connectivity within the network were found within the DMN,DAN, and ECN in cLBP patients. Significant between-group differences were also found in static FNC and in each state of dynamic FNC. In addition, in terms of dynamic temporal metrics, fraction time and mean dwell time were significantly altered in cLBP patients. In conclusion, our study suggests the existence of static and dynamic large-scale brain network alterations in patients with cLBP. The findings provide insights into the neural mechanisms underlying various brain function abnormalities and altered pain experiences in patients with cLBP.

This study aimed to investigate the changes in intrinsic brain activity (IBA) among individuals with Broca aphasia (BA) after a stroke.

We collected information from 60 participants. The participants were categorized into four groups according to language (Uyghur and Chinese) and BA status (BA and healthy): Uyghur aphasia patients (UA), Uyghur healthy control subjects (UH), Chinese aphasia patients (CA), and Chinese healthy control subjects (CH). Each group comprised 15 individuals. The shifting dynamics and concordance of regional IBA indices were examined via sliding time-window analysis. A two-way analysis of variance (ANOVA) was conducted with the IBA indices to test for regions with interactions between language and BA status. Partial correlation analysis was employed to evaluate the relationships between various indices and language behaviors.

Participants with head motion exceeding 3 mm translation or 3° rotation were excluded, leaving 9, 12, 13, and 15 participants in the UA, CA, UH, and CH groups, respectively. Seven IBA indices were activated in 16 brain regions in the four groups. In detail, two-way ANOVA revealed a significant interaction between language and BA status in four IBA dynamic indices (amplitude of low-frequency fluctuations (ALFF), Regional homogeneity (ReHo), degree centrality (DC), and functional connectivity (FC)) in 11 brain regions (P < 0.000). For the other three dynamic indices (fractional amplitude of low-frequency fluctuation (fALFF), Voxel-mirrored homotopic connectivity (VMHC), and Global signal connectivity (GSCorr)), no interaction was observed among the four groups. However, the main effect analysis of the BA state demonstrated significant differences across a total of six brain regions (P < 0.000). The concordance alterations in fALFF, ReHo, VMHC, DC, and GSCorr in the right calcarine fissure and the surrounding cortex were significantly lower in CA than in CH (P = 0.000), significantly higher in UA than in CA (P = 0.025), and significantly lower in UH than CH (P = 0.000).

In conclusion, alterations in IBA dynamics and concordance were observed in individuals from UA, UH, CA, and CH. These findings suggest that the IBA dynamic index varies across brain regions of BA patients with different local languages, providing a novel perspective for investigating brain alterations by analyzing temporal dynamics using rs-fMRI data.

Cognitive interference resolution (CIR) is the process of maintaining goal-directed focus despite the presence of distractions. While CIR has been extensively studied through localized activation analyses, its network-level dynamics remain underexplored with sufficient methodological diversity. In this study, we investigated the task-modulated intrinsic connectivity networks (ICNs) and their dynamic interactions with detailed subnetwork segmentation during CIR using fMRI data from 27 healthy adults performing the Multi-Source Interference Task (MSIT). We applied high-order group independent component analysis (ICA) to extract ICN subcomponents, followed by task-modulated component identification and dynamic functional connectivity analysis to examine network interactions. Our results reveal that the dorsal attention network (DAN) and cognitive control network (CCN) show increased activation and connectivity, while the default mode network (DMN) and limbic network exhibit decreased activation and connectivity. Additionally, the visual and cerebellum networks emerge as key intermediaries in CIR, as DAN and CCN strengthen their connectivity with these networks rather than directly interacting with each other. Furthermore, network reconfiguration patterns suggest functional segregation within the somatomotor network and CCN, indicating specialized subcomponent contributions. These findings provide a granular understanding of ICN activations and dynamic inter-network communication during CIR, offering new insights into the flexible reorganization of brain networks in response to cognitive interference.

Hidden Markov Models (HMMs) have emerged as a powerful tool for analyzing time series of neural activity. Gaussian HMMs and their time-resolved extension, Time-Delay Embedded HMMs (TDE-HMMs), have been instrumental in detecting discrete brain states in the form of temporal sequences of large-scale brain networks. To assess the performance of Gaussian HMMs and TDE-HMMs in this context, we conducted simulations that generated synthetic data representing multiple phase-coupled interactions between different cortical regions to mimic real neural data. Our study demonstrates that TDE-HMM performs better than Gaussian HMM in accurately detecting brain states from synthetic phase-coupled interaction data. Finally, for TDE-HMMs, we manipulated key parameters such as phase coupling variability, state duration, and influence of volume conduction effect to evaluate the models' performance under varying conditions.

Mental illnesses extract personal and societal costs, leading to significant challenges in cognitive function, emotional regulation, and social behavior. These disorders are thought to result from disruptions in how different brain regions communicate with each other. Despite advances in neuroimaging, current methods are not always precise enough to fully understand the complexity of these disruptions. More advanced approaches are needed to better identify and characterize the specific brain network alterations linked to different psychiatric conditions.

We employed a hierarchical approach to derive Double Functionally Independent Primitives (dFIPs) from resting-state functional magnetic resonance imaging (rs-fMRI) data. dFIPs represent independent patterns of functional network connectivity (FNC) across the brain. Our study utilized a large multi-site dataset comprising 5805 individuals diagnosed with schizophrenia (SCZ), autism spectrum disorder (ASD), bipolar disorder (BPD), major depressive disorder (MDD), and healthy controls. We analyzed how combinations of dFIPs differentiate psychiatric diagnoses.

Distinct dFIP patterns emerged for each disorder. Schizophrenia was characterized by heightened cerebellar connectivity and reduced cerebellar-subcortical connectivity. In ASD, sensory domain hyperconnectivity was prominent. Some dFIPs displayed disorder-specific connectivity patterns, while others exhibited commonalities across multiple conditions. These findings underscore the utility of dFIPs in revealing neural connectivity alterations unique to each disorder, serving as unique fingerprints for different mental disorders.

Our study demonstrates that dFIPs provide a novel, data-driven method for identifying disorder-specific functional connectivity patterns in psychiatric conditions. These distinct neural signatures offer potential biomarkers for mental illnesses, contributing to a deeper understanding of the neurobiological underpinnings of these disorders.

Inhibition mechanisms are essential in daily life, helping individuals adapt to environmental demands. However, the causal interactions between large-scale functional networks involved in response inhibition remain poorly understood.

In this study, we examined the effective connectivity (EC) underlying inhibitory processes in the brain using dynamic causal modeling (DCM) and independent component analysis (ICA). We conducted a Go-NoGo fMRI task with 19 healthy participants to investigate these networks.

Our results identified four functional networks activated during correct motor response inhibition: the salience network (SN), the right and left executive control networks (ECNs), and the ventral default mode network (vDMN). We observed a significant causal inhibitory influence from the vDMN to the left ECN (lECN). Under conditions of unsuccessful response inhibition, the SN, bilateral ECNs, and somatomotor network (SMN) were found to be prominently activated. Furthermore, we identified a significant correlation between the inhibitory influence from the SMN to the SN and the commission error rate. Finally, correlation analyses between self-reported impulsivity levels and causal network interactions revealed that highly impulsive individuals require greater interhemispheric integration between the right and left ECNs for effective inhibition, as well as a causal excitatory modulation from the right executive control network (rECN) to the vDMN.

In summary, our study reveals complex hierarchical dynamics among functional networks during response inhibition. These findings offer valuable insight into the neural mechanisms supporting inhibition and provide avenues for future research on the neural underpinnings of this critical cognitive function across the lifespan.

Alzheimer's disease (AD) pathogenesis involves astrocytic responses to extracellular amyloid beta deposits and phospho-tau neurofibrillary tangles, which drive inflammatory activation. RTP801, a stress-responsive protein, has been implicated in mediating neuroinflammation. Its levels are increased in AD hippocampal samples, correlating with disease severity and cognitive decline.

Using astrocyte-specific RTP801 silencing in the hippocampus of 5xFAD mice, we evaluated cognition, neuroinflammation, and hippocampal connectivity by magnetic resonance spectroscopy (MRS) and resting-state functional connectivity analyses. Histological and biochemical analyses assessed microgliosis, astrogliosis, and inflammasome-related protein levels.

Astrocytic RTP801 silencing in 5xFAD mice preserved spatial memory, maintained hippocampal γ-aminobutyric acid (GABA) levels, and preserved resting-state brain networks. In addition, RTP801 silencing significantly reduced markers of microgliosis, astrogliosis, and inflammasome effectors.

Astrocytic RTP801 contributes to AD-associated cognitive decline by disrupting GABAergic-regulated connectivity and amplifying inflammatory responses. Targeting astrocytic RTP801 may therefore offer therapeutic potential to mitigate AD progression by preserving neural connectivity and reducing neuroinflammation.

The 5xFAD mouse model of Alzheimer's disease presents higher levels of RTP801 in hippocampal astrocytes. Normalizing the levels of astrocytic RTP801 prevents cognitive decline and restores anxiety-like behavior in the 5xFAD mouse model. Knocking down astrocytic RTP801 preserves the resting-state functional connectivity in the 5xFAD mouse model. Astrocytic RTP801 mediates the loss of Parvalbumin+ interneurons, negatively affecting the levels of γ-aminobutyric acid (GABA) in the 5xFAD mouse model. Astrocytic RTP801 contributes to astro- and microgliosis and inflammasome expression in the 5xFAD mouse model.

Bipolar disorder (BD) is a severe psychiatric condition whose pathophysiology is complex and multifactorial. Genetic, environmental and social risk factors play a role in its development as well as in its progressive course. Research is currently focusing on the identification of the biological basis underlying these processes in order to suggest novel biomarkers capable to predict BD etiopathogenesis and staging. Staging has been recognized as of great value for the treatment and management of many illnesses and might also be suitable for mental health issues, particularly in disorders like BD, which progress from an initial mild phase to a more severe and thus difficult-to-treat situation. Thus, it would be of great help the characterization of to suggest better treatment requirements and improve prognosis across the different stages of the illness. Here, we summarize current research on the biological hypotheses of BD and the biomarkers associated with its progression, reviewing clinical studies available in the literature.

The mature cerebral cortex operates through the segregation and integration of specialized functions to generate complex cognitive states. In the mouse, the anatomical and functional correlates of this organization arise during the perinatal period and are critically shaped by neural activity. Understanding how early activity patterns distribute, interact, and generate large-scale cortical dynamics is essential to elucidate the proper development of the cortex. Here, we investigate spontaneous mesoscale cortical dynamics during the first two postnatal weeks by performing wide-field calcium imaging in GCaMP6s transgenic mice. Our results demonstrate a marked change in the spatiotemporal features of spontaneous cortical activity across fine stages of postnatal development. Already after birth, the cortical hemisphere presents a primordial macroscopic organization, which undergoes a steady refinement based on the parcellation of the cortex. As calcium activity transitions from large, widespread events to swift waves between the first and second postnatal week, significant topographic differences emerge across different cortical regions. Functional connectivity profiles of the cortex gradually segregate into main subnetworks and give rise to a highly modular network topology at the end of the second postnatal week. Overall, spontaneous mesoscale activity reflects the maturation of cortical networks, and reveals critical breakpoints in the development of the functional architecture of the cortex.

Major depressive disorder (MDD) is associated with hypoactivity in the frontoparietal (FP) system and hyperactivity in the limbic (LM) system. The widely accepted limbic-cortical dysregulation model has recently been extended by the concept of imbalanced reciprocal suppression between these 2 systems. This study investigates the refined theoretical framework. Neuroimaging datasets from 60 MDD and 60 healthy controls were obtained from the Canadian Biomarker Integration Network in Depression database, including structural magnetic resonance imaging (MRI) and resting-state functional MRI (rsfMRI). The cerebral cortex was parcellated using the modular analysis and similarity measurements (MOSI) technique. For each node, the average amplitude of low-frequency fluctuation (avgALFF) and nodal strength were calculated. Correlation analyses were conducted to establish an adjacency matrix and assess the relationship between nodal power and strength. The results indicated that the LM system in MDD displayed higher partition numbers and avgALFF (P < 0.005). A significant negative correlation between nodal strength and power was replicated (P < 1E-10), suggesting that greater functional input enhances regional neural suppression. Notably, MDD participants exhibited a higher negative correlation between FP nodal power and LM-FP connectivity (stronger suppression) but a lower negative correlation between LM nodal power and FP-LM connectivity (weaker suppression). These findings support the theory of abnormal cortical signal organization and reciprocal suppression in MDD.

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.

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.

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.

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.

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.

Non-suicidal self-injury (NSSI) is prevalent among adolescent populations worldwide, yet its neuropathological mechanisms remain unclear. This study aimed to investigate brain functional differences in NSSI patients by utilizing large-scale functional networks and examining their correlation with clinical outcomes. Cross-sectional clinical and functional magnetic resonance imaging (fMRI) data were collected from 42 patients and 47 healthy controls. Independent component analysis (ICA) was utilized to investigate changes in both intra-network and inter-network functional connectivity. We then investigated the potential association between functional network connectivity and clinical self-injurious behavior. The results revealed significant abnormalities in intra-network functional connectivity within the left middle cingulum gyrus, right angular gyrus, and middle frontal gyrus in patients with NSSI. Additionally, we found altered inter-network connectivity patterns, particularly between higher-order cognitive networks and primary sensory networks, suggesting potential disruptions in multisensory integration and emotional regulation in these patients. This study revealed significant alterations in large-scale functional network connectivity in adolescents with depression and NSSI, particularly in networks related to emotion regulation and cognitive control. These findings provide novel perspectives on the neurobiological mechanisms of NSSI and suggest possible avenues for early intervention and treatment.

Encoding new memories relies on functional connections between the medial temporal lobe and the frontoparietal cortices. Multi-scan fMRI showed changes in these functional connections before and after memory encoding, potentially influenced by the thalamus. As different thalamic nuclei are interconnected with distinct cortical networks, we hypothesized that variations in cortico-thalamic recruitment may impact individual memory performance.We used a multi-scan fMRI protocol including a resting-state scan followed by an associative memory task encompassing encoding and retrieval phases, in two independent samples of healthy adults (N1=29, mean age=26, males=35%; N2=108; mean age=28, males=52%). Individual activity and functional connectivity were analyzed in the native space to minimize registration bias. By modeling the direct and indirect effects of cortico-thalamic recruitment on memory using Structural Equation Modeling, we showed a positive association between resting-state functional connectivity of the medial thalamic subdivision within the frontoparietal network and memory performance across samples (effect size R2 ranging between 0.27 and 0.36; p-values between 0.01 and 4e-05). This direct relationship was mediated by decreased activation of the anterior subdivision during encoding (R2 ranging between 0.04 and 0.2; p-values between 0.05 and 0.006) and by increased activation of the medial subdivision during retrieval (R2 ranging between 0.04 and 0.2; p-values between 0.05 and 0.004). Moreover, three distinct clusters of individuals displayed different cortico-thalamic patterns across memory phases.We suggest that associative memory encoding relies on the distinct cortico-thalamic pathways involving medial thalamic recruitment and suppression of anterior subdivision to support the successful encoding of new memories.Significance statement Every person is unique in their learning process and related brain functional organization. Prior research has mainly aimed to find shared patterns in how the brain responds to external stimuli, often overlooking individual behavioral differences. We hypothesized that individuals may recruit different neural resources supporting their learning abilities. We investigated whether specific brain configurations are beneficial to individual memory performance. We found that the baseline configuration of select cortico-thalamic networks involving the medial thalamic subdivision supports memory performance via the indirect effects of the anterior thalamic subdivision deactivation and medial activation during the memory task. We propose that cortico-thalamic functioning involving the anterior and medial thalamus underlies interindividual variability in associative memory encoding.

Previous neuroimaging studies have identified substantial structural and functional abnormalities in the brains of patients with rhegmatogenous retinal detachment (RRD). Nonetheless, there remains a paucity of comprehensive research on the alterations in functional connectivity (FC) within large-scale static and dynamic brain networks in these patients.

This study utilized independent component analysis (ICA) to investigate alterations in large-scale brain network FC in patients with RRD. Additionally, it employed support vector machine (SVM) to classify RRD patients and healthy controls (HCs) and examined the relationship between abnormal brain regions and clinical ophthalmic parameters.

The RRD patients demonstrated significantly increased FC in the default mode network (DMN) and visual network (VN) compared to the HCs, whereas the FC in the auditory network (AN) and the sensorimotor network (SMN) was significantly decreased. Analysis of dynamic functional network connectivity (dFNC) revealed that the fraction of time (FT) spent in state 4 was significantly greater in RRD patients compared to HCs. SVM analysis showed that the AUC for classification using AN and FNC features reached 0.828 and 0.819, respectively. Additionally, the FC value of the right medial superior frontal gyrus (R-SFGmed) in RRD patients was positively correlated with axial length (r = 0.401, p = 0.038).

This study revealed that patients with RRD exhibit both damage and adaptive remodeling in their brain functional networks. Alterations in the AN and FNC may serve as potential neuroimaging biomarkers for distinguishing RRD patients from HCs, providing crucial neuroimaging evidence for understanding the mechanisms underlying visual loss in RRD.

Complex biological systems, like the brain, exhibit intricate multiway and multiscale interactions that drive emergent behaviors. In psychiatry, neural processes extend beyond pairwise connectivity, involving higher-order interactions critical for understanding mental disorders. Conventional brain network studies focus on pairwise links, offering insights into basic connectivity but failing to capture the complexity of neural dysfunction in psychiatric conditions. This study aims to bridge this gap by applying a matrix-based entropy functional to estimate total correlation, a mathematical framework that incorporates multivariate information measures extending beyond pairwise interactions. We apply this framework to fMRI-ICA-derived multiscale brain networks, enabling the investigation of interactions beyond pairwise relationships in the human multiscale brain. Additionally, this approach holds promise for psychiatric studies, providing a new lens through which to explore beyond pairwise brain network interactions. By examining both triple interactions and the latent factors underlying the triadic relationships among intrinsic brain connectivity networks through tensor decomposition, our study presents a novel approach to understanding higher-order brain dynamics. This framework not only enhances our understanding of complex brain functions but also offers new opportunities for investigating pathophysiology, potentially informing more targeted diagnostic and therapeutic strategies. Moreover, the methodology of analyzing multiway interactions beyond pairwise connections can be applied to any signal analysis. In this study, we specifically explore its application to neural signals, demonstrating its power in uncovering complex multiway interaction patterns of brain activity, which provide a window to explore connectivity beyond pairwise interactions in the multiscale functionality of the brain.

We must reconcile the needs of the internal world and the demands of the external world to make decisions relevant to homeostasis, well-being, and flexible behavior. Engagement with the internal (eg interoceptive) world is linked to medial brain systems, whereas the extrapersonal space (eg exteroceptive) is associated with lateral brain systems. Using Human Connectome Project data, we found three association tracts connecting the action-related frontal lobe with perception-related posterior lobes. A lateral dorsal tract and a medial dorsal tract interact independently with a ventral tract at frontal and posterior hubs. The two frontal and the two posterior hubs are interconnected, forming a meta-loop that integrates lateral and medial brain systems. The four anatomical hubs correspond to the common nodes of the intrinsic cognitive brain networks such as the default mode network. These functional networks depend on the integration of both realms. Thus, the positioning of functional cognitive networks can be understood as the intersection of long anatomical association tracts. The strength of structural connectivity within lateral and medial brain systems correlates with performance on behavioral tests assessing theory of mind. The meta-loop provides an anatomical framework to associate neurological and psychiatric symptoms with functional and structural changes.

Facial palsy, often resulting from trauma or iatrogenic treatments, leads to significant aesthetic and functional impairment. Surgical interventions, such as masseteric-to-facial nerve transfer combined with static suspension, are frequently recommended to restore facial nerve function and symmetry.

This study examined the impact of botulinum toxin type A (BoNT-A) treatment on the unaffected side with regard to facial symmetry and brain connectivity in patients with severe oral commissure droop from facial nerve damage. Patients were divided into 2 groups: 1 group received BoNT-A injections on the unaffected side, and the other did not.

The authors' findings revealed that BoNT-A treatment not only improved facial symmetry but also induced significant modifications in brain functional network connectivity. These modifications extended beyond the sensorimotor network, involving high-level cognitive processes, and exhibited a significant correlation with the degree of facial asymmetry.

These results provide valuable insights into the mechanisms underlying the positive effects of BoNT-A intervention on motor recovery and brain plasticity in facial palsy patients. Furthermore, the study emphasizes the importance of a multidisciplinary approach to facial palsy rehabilitation. Understanding these intricate interactions between facial symmetry restoration and brain network adaptations may pave the way for more effective treatments and improved quality of life for individuals dealing with facial palsy.

Therapeutic, II.

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disorder characterized by the progressive degeneration of motor neurons in the cortex, brainstem, and spinal cord. This degeneration leads to muscular weakness, progressively impairing motor functions and ultimately resulting in respiratory failure. The clinical, genetic, and pathological heterogeneity of ALS, combined with the absence of reliable biomarkers, significantly challenge the efficacy of therapeutic trials. Despite these hurdles, neuroimaging, and particularly spinal cord imaging, has emerged as a promising tool. It provides insights into the involvement of both upper and lower motor neurons. Quantitative spinal imaging has the potential to facilitate early diagnosis, enable accurate monitoring of disease progression, and refine the design of clinical trials. In this review, we explore the utility of spinal cord imaging within the broader context of developing spinal imaging biomarkers in ALS. We focus on a both diagnostic and prognostic biomarker in ALS, highlighting its pivotal role in elucidating the disease's underlying pathology. We also discuss the existing limitations and future avenues for research, aiming to bridge the translational gap between academic research and its application in clinical practice and therapeutic trials.

As scientists interested in fetal, infant, and toddler (FIT) neurodevelopment, our research questions often focus on how individual children differ in their neurodevelopment and the predictive value of those individual differences for long-term neural and behavioral outcomes. Measuring and interpreting individual differences in neurodevelopment can present challenges: Is there a "standard" way for the human brain to develop? How do the semantic, practical, or theoretical constraints that we place on studying "development" influence how we measure and interpret individual differences? While it is important to consider these questions across the lifespan, they are particularly relevant for conducting and interpreting research on individual differences in fetal, infant, and toddler neurodevelopment due to the rapid, profound, and heterogeneous changes happening during this period, which may be predictive of long-term outcomes. This article, therefore, has three goals: 1) to provide an overview about how individual differences in neurodevelopment are studied in the field of developmental cognitive neuroscience, 2) to identify challenges and considerations when studying individual differences in neurodevelopment, and 3) to discuss potential implications and solutions moving forward.

The study of brain connectivity, both functional and structural, can inform us on the development of psychopathology. The use of multimodal MRI methods allows us to study associations between structural and functional connectivity, and how this relates to psychopathology. This may be especially useful during childhood and adolescence, a period where most forms of psychopathology manifest for the first time. The current paper explores structure-function coupling, measured through diffusion and resting-state functional MRI, and quantified as the correlation between structural and functional connectivity matrices. We investigate associations between psychopathology and coupling in a transdiagnostic group of adolescents, including many treatment-seeking youth with relatively high levels of symptoms (n = 72, Mage = 13.3). We used a bifactor model to extract our main outcome measure, Negative Affectivity, from anxiety and irritability ratings. This provided the principal measure of psychopathology. Supplementary analyses investigated 'domain-specific' factors of anxiety and irritability. Findings indicate a positive association between negative affectivity and structure-function coupling between the default mode and the fronto-parietal control networks. Higher structure-function coupling may indicate heightened structural constraints on function, which limit functional network reorganization during adolescence required for healthy psychological outcomes.

This review aims to emphasize the diagnostic value of functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI) tractography in detecting and diagnosing central post-stroke pain (CPSP).

CPSP is a debilitating form of chronic neuropathic pain that develops in patients with a history of stroke. CPSP has a wide range of onset and non-specific clinical presentations, making it difficult to detect. Until now, CPSP has been considered a diagnosis of exclusion, often leading to delays in the initiation of the appropriate treatment plan. fMRI and DTI tractography are valuable tools for assessing cerebral metabolic activity and the structural characteristics of the spinothalamic tracts, respectively. By combining these sets of information, physicians can detect CPSP early and implement more effective treatment strategies.

Diagnosing CPSP has been challenging for physicians due to its complex nature. However, fMRI and DTI have the potential in enabling earlier detection of CPSP, giving physicians more time to initiate treatment. This review highlights the capacities of fMRI and DTI in identifying alterations in the spinothalamic pathways associated with CPSP.

The present study aimed to investigate changes in intranetwork functional connectivity (FC) and internetwork FC in middle-aged and elderly individuals with normal cognition (NC) and varying degrees of periodontitis to determine the effects of periodontitis on brain function.

Periodontal findings and resting-state functional magnetic resonance imaging data were acquired from 51 subjects with NC. Independent component analysis and correlation analysis were used for the statistical analysis of the data.

Differences in intranetwork FC were observed among groups in the anterior default-mode network (aDMN), dorsal attention network and dorsal sensorimotor network (dSMN). Compared with the nonperiodontitis (NP) group or the mild-periodontitis group, the analysis of internetwork FC showed increased FC between the auditory network and the ventral attention network (VAN), between the aDMN and the salience network (SN), and between the SN and the VAN and decreased FC between the posterior default-mode network and the right frontoparietal network in the moderate-to-severe periodontitis group. Additionally, internetwork FC between the dSMN and the VAN was also increased in the moderate-to-severe periodontitis group compared to the NP group. The altered intra- and internetwork FC were significantly correlated with the periodontal clinical index.

Our results confirmed that periodontitis was associated with both intra- and internetwork FC changes even in NC. The present study indicates that periodontitis might be a potential risk factor for brain damage and provides a theoretical clue and a new treatment target for the early prevention of Alzheimer disease.

Recent research has proposed that periodontitis is a potential risk factor for Alzheimer disease (AD). However, the relationship between periodontitis and the brain function of middle-aged and elderly individuals with normal cognition (NC) remains unclear. Analyzing the effect of periodontitis on brain function in the NC stage can provide clues to AD development and help achieve early prevention of dementia. The present study aimed to investigate changes in brain functional connectivity (FC) in NC with different severity of periodontitis to determine the effects of periodontitis on brain function. Both changed intranetwork FC and internetwork FC were found in the moderate-to-severe periodontitis group, and periodontitis was associated with brain network function impairment in NC. The present study indicates that periodontitis might be a potential risk factor for brain damage even in NC stage, and provides a theoretical clue and a new treatment target for the early prevention of AD.

To explore the functional brain imaging characteristics of patients with disorders of consciousness (DoC).

This prospective cohort study consecutively enrolled 27 patients in minimally conscious state (MCS), 23 in vegetative state (VS), and 25 age-matched healthy controls (HC). Resting-state functional magnetic resonance imaging (rs-fMRI) was employed to evaluate the amplitude of low-frequency fluctuation (ALFF), regional homogeneity (ReHo), degree centrality (DC), and functional connectivity (FC). Sliding windows approach was conducted to construct dynamic FC (dFC) matrices. Moreover, receiver operating characteristic analysis and Pearson correlation were used to distinguish these altered characteristics in DoC.

Both MCS and VS exhibited lower ALFF, ReHo, and DC values, along with reduced FC in multiple brain regions compared with HC. Furthermore, the values in certain regions of VS were lower than those in MCS. The primary differences in brain function between patients with varying levels of consciousness were evident in the cortico-striatopallidal-thalamo-cortical mesocircuit. Significant differences in the temporal properties of dFC (including frequency, mean dwell time, number of transitions, and transition probability) were also noted among the three groups. Moreover, these multimodal alterations demonstrated high classificatory accuracy (AUC > 0.8) and were correlated with the Coma Recovery Scale-Revised (CRS-R).

Patients with DoC displayed abnormal patterns in local and global dynamic and static brain functions. These alterations in rs-fMRI were closely related to the level of consciousness.

Systemic infection and inflammation impair mental function through a combination of altered attention and cognition. Here, we comprehensively review the relevant literature and report personal clinical observations to discuss the relationship between infection, peripheral inflammation, and cerebral and cognitive dysfunction in patients with myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS). Cognitive dysfunction in ME/CFS could result from low-grade persistent inflammation associated with raised pro-inflammatory cytokines. This may be caused by both infectious and non-infectious stimuli and lead to altered regional cerebral blood flow accompanied by disturbed neuronal function. Immune dysregulation that manifests as a subtle immunodeficiency or the autoimmunity targeting of one or more neuronal receptors may also be a contributing factor. Efforts to reduce low-grade systemic inflammation and viral reactivation and to improve mitochondrial energy generation in ME/CFS have the potential to improve cognitive dysfunction in this highly disabling condition.

Cerebral small vessel disease is a common disease endangering human health due to its insidious and repeated onset and progressive aggravation. White matter hyperintensities (WMHs) are one of the classic imaging markers of cerebral small vessel disease. The term 'WMHs' was first proposed by Hachinski in 1987. The WMHs in our study mainly refer to cerebral white matter damage caused by various vascular factors, known as vascularized white matter hyperintensity. WMHs are significantly correlated with stroke, cognitive dysfunction, emotional disturbance, and gait abnormality, and have drawn widespread attention. This article reviews the research progress on the pathogenesis of cognitive dysfunction associated with WMHs and provides a theoretical reference for understanding the pathogenesis of WMHs and the early assessment of associated cognitive dysfunction.

Since resting-state networks were first observed using magnetic resonance imaging (MRI), their cognitive relevance has been widely suggested. However, to date, the empirical cognitive characterization of these networks has been limited. The present study introduces the Groupe d'Imagerie Neurofonctionnelle Network Atlas, a comprehensive brain atlas featuring 33 resting-state networks. Based on the resting-state data of 1812 participants, the atlas was developed by classifying independent components extracted individually, ensuring consistent between-subject detection. We further explored the cognitive relevance of each GINNA network using Neurosynth-based meta-analytic decoding and generative null hypothesis testing. Significant cognitive terms for each network were then synthesized into appropriate cognitive processes through the consensus of six authors. The GINNA atlas showcases a diverse range of topological profiles, reflecting a broad spectrum of the known human cognitive repertoire. The processes associated with each network are named according to the standard Cognitive Atlas ontology, thus providing opportunities for empirical validation.

Resting state functional MRI (rsfMRI) exploits variations in blood-oxygenation-level-dependent (BOLD) signals to infer resting state functional connectivity (FC) within and between brain networks. However, there have been few reports quantifying and validating the results of rsfMRI analyses with other metrics of brain circuits. We measured longitudinal changes in FC both within and between brain networks in three squirrel monkeys after focal lesions of the thalamic ventroposterior lateral nucleus (VPL) that were intended to disrupt the input to somatosensory cortex and impair manual dexterity. Local field potential signals were recorded to assess electrophysiological changes during each animal's recovery, and behavioral performances were measured longitudinally using a sugar-pellet grasping task. Finally, end-point histological evaluations were performed on brain tissue slices to quantify the VPL damage. The rsfMRI data analysis showed significant decrease in FC measures both within and between networks immediately post-injury, which started to recover at different time-points for each animal. The trajectories of FC recovery for each animal mirrored their individual behavioral recovery time-courses. Electrophysiological measurements of inter-electrode coherences and end-point histological measures also aligned well with the graded injury effects measured using rsfMRI-based FC. A simple algorithm employing FC measures from the somatosensory network could accurately predict each monkeys' behavioral recovery timeframe after four weeks post-injury. Whole brain between-network FC measures further revealed that the injury effects were not limited to thalamocortical connections but were rather more widespread. Overall, this study provides evidence of the validity of rsfMRI based FC measures as indicators of the functional integrity and behavioral relevance following an injury to a specific brain circuit.

Microstate characterization of electroencephalogram (EEG) is a data-driven approach to explore the functional changes and interrelationships of multiple brain networks on a millisecond scale. This study aimed to explore the pathological changes of whole-brain functional networks in patients with obsessive-compulsive disorders (OCD) through microstate analysis and further to explore its potential value as an auxiliary diagnostic index.

Forty-eight OCD patients (33 with more than moderate anxiety symptoms, 15 with mild anxiety symptoms) and 52 healthy controls (HCs) were recruited. Brain activities during eyes-closed period were collected using 64-channel electroencephalography. The differences in microstate features between OCD patients and HCs were compared, and the relationship between the microstate features and clinical symptoms were explored. Key microstate features were selected for machine learning modeling to achieve targeted classifications.

The probability of transition from microstate B to C was significantly lower in OCD patients compared to HCs, and the obsessive thoughts factor scores were significantly correlated with the duration of microstate A, the occurrence of microstate B, and the transition probability from microstate C to B. The occurrence rate of microstate C was significantly negatively correlated with the Hamilton rating scale for anxiety (HAMA) scores. The AUC (Area Under the Receiver Operating Characteristic Curve) of the machine learning model in the test set classification between the above two groups and between OCD patients with more than moderate/mild anxiety symptoms could achieve 70.43% and 77.13%, respectively.

EEG microstate characteristics were altered in OCD patients, and these changes were closely associated with obsessive thoughts and anxiety symptoms. Besides, the machine learning classification model based on microstate features has limited ability to identify OCD, and further optimization on this classification approach is still needed in the future.

Irregular and unpredictable fetal movement is the most common cause of artifacts in in utero functional magnetic resonance imaging (fMRI), affecting analysis and limiting our understanding of early functional brain development. The accurate detection of corrupted functional connectivity (FC) resulting from motion artifacts or preprocessing, instead of neural activity, is a prerequisite for reliable and valid analysis of FC and early brain development. Approaches to address this problem in adult data are of limited utility in fetal fMRI. In this study, we evaluate a novel technique for robust computational assessment of motion artifacts, and the quantitative comparison of regression models for artifact removal in fetal FC analysis. It exploits the association between dynamic FC and non-stationarity of fetal movement, to detect residual noise. To validate our motion artifact detection technique in detail, we used a parametric generative model for neural events and fMRI blood oxygenation level-dependent (BOLD) signal. We conducted a systematic evaluation of 11 commonly used regression models in a sample of 70 fetuses with gestational age of 19-39 weeks. Results demonstrate that the proposed method has better accuracy in identifying corrupted FC compared to methods designed for adults. The technique, suggests that censoring, global signal regression and anatomical component-based regression models are the most effective models for compensating motion. The benchmarking technique, and the generative model for realistic fetal fMRI BOLD enables investigators conducting in utero fMRI analysis to effectively quantify the impact of fetal motion and evaluate alternative regression strategies for mitigating this impact. The code is publicly available at: https://github.com/cirmuw/fetalfMRIproc.

Functional brain fingerprinting has emerged as an influential tool to quantify reliability in neuroimaging studies and to identify cognitive biomarkers in both healthy and clinical populations. Recent studies have revealed that brain fingerprints reside in the timescale-specific functional connectivity of particular brain regions. However, the impact of the acquisition's temporal resolution on fingerprinting remains unclear. In this study, we examine for the first time the reliability of functional fingerprinting derived from resting-state functional MRI (rs-fMRI) with different whole-brain temporal resolutions (TR = 0.5, 0.7, 1, 2, and 3 s) in a cohort of 20 healthy volunteers. Our findings indicate that subject identifiability within a fixed TR is successful across different temporal resolutions, with the highest identifiability observed at TR 0.5 and 3 s (TR(s)/identifiability(%): 0.5/64; 0.7/47; 1/44; 2/44; 3/56). We discuss this observation in terms of protocol-specific effects of physiological noise aliasing. We further show that, irrespective of TR, associative brain areas make an higher contribution to subject identifiability (functional connections with highest mean ICC: within subcortical network [SUB; ICC = 0.0387], within default mode network [DMN; ICC = 0.0058]; between DMN and somato-motor [SM] network [ICC = 0.0013]; between ventral attention network [VA] and DMN [ICC = 0.0008]; between VA and SM [ICC = 0.0007]), whereas sensory-motor regions become more influential when integrating data from different TRs (functional connections with highest mean ICC: within fronto-parietal network [ICC = 0.382], within dorsal attention network [DA; ICC = 0.373]; within SUB [ICC = 0.367]; between visual network [VIS] and DA [ICC = 0.362]; within VIS [ICC = 0.358]). We conclude that functional connectivity fingerprinting derived from rs-fMRI holds significant potential for multicentric studies also employing protocols with different temporal resolutions. However, it remains crucial to consider fMRI signal's sampling rate differences in subject identifiability between data samples, in order to improve reliability and generalizability of both whole-brain and specific functional networks' results. These findings contribute to a better understanding of the practical application of functional connectivity fingerprinting, and its implications for future neuroimaging research.