Abnormalities in paracingulate gyrus (PCG) were found in both patients with temporal lobe epilepsy and patients with sleep disorders. These abnormalities include reduced functional connectivity between PCG and other brain regions, abnormal activity and reduced volume of PCG. PCG may be associated with comorbid sleep disorders in patients with temporal lobe epilepsy (TLE). This study aimed to explore the relationship between abnormal PCG function and comorbid sleep disorders in patients with TLE. We used Pittsburgh Sleep Quality Index -8/9 to divide fifty-eight patients into a group with sleep disorders and group without sleep disorders. Using the PCG as the seed, we examined the task-based seed-to-voxel functional connectivity identified by group-independent component analysis. Compared with the normal sleep group, we observed longer disease duration, higher frequency of seizures, higher Self-Rating Anxiety Scale scores in the comorbidity group (p < 0.05). During the verbal fluency character task, functional connectivity from the PCG to the right frontal and parietal regions was decreased in the comorbidity group (p < 0.05, FDR-corrected). Sensitivity analyses confirmed that our conclusions were not affected by factors such as laterality or hippocampal sclerosis. These abnormalities reveal brain lesions in patients with TLE comorbid sleep disorders, which may contribute to the pathogenesis of the comorbidity and be related to patients' preserved verbal functions.

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.

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.

Attention networks are loosely defined as the regions of the brain which interact to control behaviour during attentional tasks, but the specific definition of attention networks varies between research programs based on task demands and modalities. The Attention Network Task was designed to exemplify three aspects of attention, alerting, orienting, and executive control, using a visual cueing paradigm. Its proponents propose a system of networks which underlies these aspects. It is debated whether there exists a unified system of networks which underlies attention independently of other cognitive and sensory processing systems. We review the evidence for an attention system within the domain of visual attention. Neuroimaging research using fMRI, EEG, MEG, and others across a variety of tasks attributed to attention, visual cueing, visual search, and divided attention, is compared. This concludes with a discussion on the limitations of an independent "attention system" for describing how the brain flexibly controls many abilities attributed to visual attention.

Speech comprehension in noisy environments constitutes a critical challenge in everyday life and affects people of all ages. This challenging listening situation can be alleviated using semantic context to predict upcoming words (i.e., predictability gain)-a process associated with the domain-specific semantic network. When no such context can be used, speech comprehension in challenging listening conditions relies on cognitive control functions, underpinned by domain-general networks. Most previous studies focused on regional activity of pre-selected cortical regions or networks in healthy young listeners. Thus, it remains unclear how domain-specific and domain-general networks interact during speech comprehension in noise and how this may change across the lifespan. Here, we used correlational psychophysiological interaction (cPPI) to investigate functional network interactions during sentence comprehension under noisy conditions with varying predictability in healthy young and older listeners. Relative to young listeners, older adults showed increased task-related activity in several domain-general networks but reduced between-network connectivity. Across groups, higher predictability was associated with increased positive coupling between semantic and attention networks and increased negative coupling between semantic and control networks. These results highlight the complex interplay between the semantic network and several domain-general networks underlying the predictability gain. The observed differences in connectivity profiles with age inform the current debate on whether age-related changes in neural activity and functional connectivity reflect compensation or dedifferentiation.

Simultaneous interoceptive, emotional, and social cognition deficits are observed across neurodegenerative diseases. Indirect evidence suggests shared neurobiological bases underlying these impairments, termed the allostatic-interoceptive network (AIN). However, no study has yet explored the convergence of these deficits in neurodegenerative diseases or examined how structural and functional changes contribute to cross-domain impairments.

A Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) activated likelihood estimate meta-analysis encompassed studies that met the following inclusion criteria: interoception, emotion, or social cognition tasks; neurodegenerative diseases (behavioral variant frontotemporal dementia, primary progressive aphasias, Alzheimer's disease, Parkinson's disease, multiple sclerosis); and neuroimaging (structural: magnetic resonance imaging voxel-based morphometry; functional: magnetic resonance imaging and fluorodeoxyglucose-positron emission tomography).

Of 20,593 studies, 170 met inclusion criteria (58 interoception, 65 emotion, and 47 social cognition) involving 7032 participants (4963 patients and 2069 healthy control participants). In all participants combined, conjunction analyses revealed AIN involvement of the insula, amygdala, orbitofrontal cortex, anterior cingulate, striatum, thalamus, and hippocampus across domains. In behavioral variant frontotemporal dementia, this conjunction was replicated across domains, with further involvement of the temporal pole, temporal fusiform cortex, and angular gyrus. A convergence of interoception and emotion in the striatum, thalamus, and hippocampus in Parkinson's disease and the posterior insula in primary progressive aphasias was also observed. In Alzheimer's disease and multiple sclerosis, disruptions in the AIN were observed during interoception, but no convergence with emotion was identified.

Neurodegeneration induces dysfunctional AIN across atrophy, connectivity, and metabolism, more accentuated in behavioral variant frontotemporal dementia. Findings bolster the predictive coding theories of large-scale AIN, calling for more synergistic approaches to understanding interoception, emotion, and social cognition impairments in neurodegeneration.

Recent studies in patients with drug-resistant epilepsy (DRE) have shown that including areas of ictal spread in resections may result in better postoperative seizure control. However, it remains unclear whether the extent or speed of ictal spread affects neuropsychological test (NP) performance. In the present study, we assessed the relationship between the speed of initial ictal spread and preoperative measures of language and memory, as well as postoperative seizure outcomes in patients with refractory focal epilepsy.

A retrospective chart review and analysis of patients with DRE who underwent intracranial EEG (iEEG) monitoring and surgical resections from 2008 to 2016 was conducted at the level 4 epilepsy center at the University of Nebraska Medical Center. The scores reflecting immediate and delayed verbal and visual memory functions were extracted from the preoperative Logical Memory I, II, and Visual Reproduction I, II tests of the Wechsler Memory Scale III or IV, respectively. The scores defining the language function were extracted from the preoperative Boston Naming or Neuropsychology Assessment Battery Naming test. The relevant demographic and clinical data were also collected. Raw tracings of the ictal iEEG recordings were reviewed independently by two epileptologists, and the speed of ictal spread was labeled as early or late based on the 10-second cutoff. The postoperative seizure outcomes were assessed using the Engel score. The patient's performance on the preoperative NP tests and their postoperative seizure status were compared between the early and late ictal spread groups.

Of 111 patients who underwent iEEG during the target period, 68 met the inclusion criteria. Based on the iEEG recordings and other studies, temporal epilepsy was diagnosed in 64.7% of patients, while temporal plus epilepsy and extratemporal epilepsy were found in 25% and 10.3% of patients, respectively. Early ictal spread was identified in 38 (55.9%) patients, while late onset was found in 30 (44.1%) patients. Immediate verbal memory scores (median and interquartile range) in early and late ictal spread groups were 37.0 (5.0; 63.0) and 26.5 (5.0; 56.5), respectively, while immediate visual memory scores in the same groups were 25.0 (3.5; 50.0) and 37.0 (16.0; 63.0), respectively. The naming scores were 18.0 (7.0; 46.0) and 8.0 (1.0; 21.0) in the early and late ictal spread groups. There were no differences in the performance on the verbal and visual memory or language tests in patients with early and late ictal spreads. Further, there were no differences in postoperative seizure outcomes in these two groups.

We found that the speed of ictal spread assessed with iEEG does not influence performance on the standard preoperative tests of memory and language or postoperative seizure outcomes in patients with DRE. To detect such differences, a more refined approach targeting the selected subtests of the language and memory functions during presurgical evaluation may be required.

Neuromyelitis Optica Spectrum Disorder (NMOSD) affects the optic nerves and spinal cord, but its longitudinal effects on brain function remain unclear. This study aims to examine changes in brain functional connectivity over time in NMOSD patients and assess their correlation with clinical outcomes, and explore whether shifts in connectivity, especially within the default-mode hub Brodmann area 23 (BA23), are indicative of changes in clinical status.

Clinical assessments and resting-state fMRI data were analysed from 31 non-relapsing NMOSD patients at baseline and during one-year follow-up, and from 20 age- and gender-matched healthy controls (HCs) at baseline. We identified resting-state networks (RSNs) using independent component analysis (ICA). Functional connectivity (FC) was analyzed both within RSNs and between region-of-interest seeds and whole-brain voxels. Comparisons between groups (HCs vs. Baseline, HCs vs. Follow-up) and within the patient group (Baseline vs. Follow-up), as well as correlations with clinical evaluations, were conducted.

Significant FC changes were observed in NMOSD patients. At baseline, NMOSD patients exhibited significantly reduced FC in the lateral visual, sensorimotor, executive control, and left dorsal visual networks; however, these abnormalities showed partial recovery over time. Meanwhile, further decreases were noted in the medial visual and right dorsal visual networks at follow-up. Conversely, a significant increase in FC within the default mode network, particularly in the BA23 region, correlated with improvements in EDSS scores (r = 0.53, p < 0.01). Declines in connectivity between the BA23 region and both the lingual and fusiform gyri were associated with worsening Expanded Disability Status Scale (EDSS) scores (r =  - 0.38, p < 0.05) and reduced visual acuity (r =  - 0.45, p < 0.05).

NMOSD patients exhibit both compensatory and progressive changes in brain functional connectivity over time. Alterations in the BA23 region are closely associated with clinical outcomes, highlighting its potential as a functional biomarker. Highlights 1. Dynamic alterations in brain functional connectivity were observed in NMOSD patients during follow-up, reflecting both recovery and deterioration. 2. Increased connectivity within the default mode network, especially in the BA23 region, suggests compensatory neural adaptations. 3. Reduced connectivity between BA23 and visual processing regions (fusiform and lingual gyri) was linked to declines in visual acuity and neurological function.

Entropy is a critical measure for assessing the complexity and irregularity of brain signals. Understanding how brain entropy can be influenced by non-invasive neurostimulation in psychiatric patients remains a clinically relevant issue.

This study aims to explore whether low-frequency repetitive transcranial magnetic stimulation (rTMS) can modulate brain entropy in schizophrenia patient with auditory verbal hallucinations (AVH).

A case-control design was employed in this study. Low-frequency (1 Hz) rTMS targeting at left temporoparietal junction was administered to schizophrenia patients with AVH. Brain entropy (sample entropy) was calculated from resting-state functional magnetic resonance imaging (fMRI) data. Comparisons of sample entropy were made between the schizophrenia patients and healthy controls, as well as within the patient group pre- and post-rTMS.

Following rTMS treatment, patients showed a reduction in clinical symptoms, including positive symptoms and AVH. Neurocognitive improvements were also observed in domains such as verbal and visual memory. Furthermore, patients exhibited increased sample entropy in regions including the prefrontal cortices and temporal lobes compared to healthy controls. However, this elevated entropy was reduced post-rTMS, particularly in areas associated with AVH. The language network and default model network, initially showing high mean sample entropy, demonstrated a significant decrease after rTMS treatment. These changes in brain entropy were correlated with clinical improvements.

This modulation of neural activity complexity induced by the low-frequency rTMS may underlie the observed clinical and cognitive improvement in schizophrenia.

Multidomain lifestyle interventions are a promising approach to prevent cognitive decline, but their effects in subjective cognitive decline (SCD) remain controversial. We investigated the effects of lifestyle interventions on cognition and brain integrity in these at-risk individuals.

One-hundred twenty-eight older adults with SCD were randomly assigned to either Active Control Intervention (ACI), i.e. health education; Partial Intervention (PI), i.e. tramiprosate supplementation (100 mg/die) and dietary advice; or Multilevel Intervention (MI), i.e. PI plus computerized cognitive training and physical exercise, for one year. Neuropsychological assessment and MRI were performed at baseline and at 1-year follow-up. Analyses of covariance were used to measure the effects of interventions on predefined outcomes.

The MI group significantly improved in attention-executive functioning (p = 0.003) compared to ACI (Cohen's d: 0.47, 95% CI 0.13-0.79). In addition, depressive symptoms (Cohen's d: - 0.48, 95% C.I. - 0.81 to - 0.14) and memory concerns (Cohen's d: - 0.77, 95% C.I. - 1.12 to - 0.41) decreased in the MI and PI respectively, relative to the ACI. The MI group also showed increased resting-state (i.e., intrinsic) brain activity in the right fronto-parietal executive network. No significant intervention effects on brain structural or vascular outcomes were found.

The study shows that a multidomain lifestyle intervention can enhance attention-executive function, ameliorate depressive symptoms and increase functional connectivity in SCD. These findings support the role of lifestyle interventions in public health strategies to mitigate cognitive decline risk.

The trial has been registered at the United States National Library of Medicine at the National Institutes of Health Registry of Clinical Trials under the code NCT04744922 on December 9th, 2017 ( https://www.

gov/ct2/show/NCT03382353 ).

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.

Overwhelming evidence showed that obesity was associated with abnormal brain functional networks. However, the changes of structural covariance networks (SCNs) based on cortical thickness (CT) and cortical surface area (CSA) in obesity is still unclear.

In this study, 243 young adults with obesity and matched 243 lean individuals were enrolled from the Human Connectome Project Release S1200 dataset. All participants underwent magnetic resonance imaging scans following clinical and neuropsychological assessments. SCNs matrices were constructed by Brain Connectivity Toolbox based on both CT and CSA. Nonparametric permutation tests were adopted to examine group differences of these matrices.

Young adults with obesity exhibited lower CSA of left entorhinal cortex, but higher CT of both left rostral anterior cingulate cortex and right superior parietal lobule, as well as lower CT of left temporal pole. While in terms of global network measures, there were no significant group differences; in terms of nodal network measures, young adults with obesity exhibited alterations in widespread brain regions including left posterior cingulate cortex, bilateral superior frontal gyrus, left entorhinal cortex and right insula.

Young adults with obesity exhibited abnormal nodal network measures in widespread brain regions involved in default mode network, central executive network and salience network. These findings indicate the adverse effects of obesity on young adults might be associated with the altered triple network.

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.

Major Depressive Disorder (MDD) has a 50-80% recurrence rate highlighting the urgent need for more efficient recurrence prevention programs. Currently, recurrences are often identified too late, while existing preventive strategies may not sufficiently address ethio-patho-physiological mechanisms for recurrence. Negative memory bias (the tendency to better remember negative than positive events), negative attention bias (selective attention favoring mood-congruent information), and cognitive control deficits are important factors involved in the onset, maintenance, and recurrence of depressive episodes.

Here we describe the protocol for the Smartphone-based Monitoring and cognition Modification Against Recurrence of Depression (SMARD) study, aiming to investigate different forms of cognitive training programs administered via smartphones, in order to develop a second-generation recurrence prevention program. In addition, we will gather Experience Sampling Method (ESM) assessments during a 6-day period, and during the follow-up period we will obtain behavioral data on (social) activities with BEHAPP, a smartphone-based Mobile Passive Monitoring application for remote behavioral monitoring to identify behavioral changes indicative of an imminent depressive episode. In a randomized controlled trial, SMARD will compare the effects of a smartphone-based Memory Bias Modification Training (MBT), Cognitive Control Training (CCT), and Attention Bias Modification Training (ABT) versus cognitive domain-specific (active-) sham trainings in 120 remitted MDD-patients with recurrent-MDD. Over the course of three weeks, participants receive multiple daily training sessions. Thereafter, participants will be followed up for 1.5 years with 3-monthly interviews to assess recurrences.

The SMARD study aims to 1. assess the effects of the cognitive training programs versus their training-specific (active-) sham conditions on changes in memory, cognitive control dysfunction and attention; 2. relate training effects to neural networks previously identified in (recurrence of) MDD (therefore we obtain functional Magnetic Resonance Imaging ((f)MRI) scans before and after the training in a subset of participants); 3. link baseline and change in memory, cognitive control, attention and neural functioning, and ESM data to prospective recurrences; 4. examine whether passive smartphone-use monitoring can be used for prediction of recurrences.

NL-OMON26184 and NL-OMON27513. Registered 12 August 2021-Retrospectively registered,  https://onderzoekmetmensen.nl/en/trial/26184 en https://onderzoekmetmensen.nl/en/trial/27513 .

Individual differences in brain intrinsic functional connectivity (FC) and reactivity to nicotine cues are linked to variability in clinical outcomes in nicotine dependence. However, the relative contributions and potential interdependencies of these brain imaging-derived phenotypes in the context of craving and nicotine dependence are unclear. Moreover, it is unknown whether these relationships differ in individuals who smoke versus vape nicotine. To investigate these questions, eighty-six individuals who use nicotine daily (n = 67 smoking, n = 19 vaping) completed either a smoking or vaping cue-reactivity task and a resting-state scan during functional magnetic resonance imaging (fMRI). Validating the efficacy of the smoking and vaping tasks, both cohorts displayed robust reactivity to nicotine versus neutral cues in the default mode network (DMN) and the anterior insula (AI), a primary node of the salience network (SN), which did not habituate over time. In the smoking and vaping groups, lower prefrontal reactivity to nicotine versus neutral cues and greater resting-state FC between nodes of the SN and DMN were associated with higher cue-induced craving. Moreover, we found that the former partially mediated the latter, suggesting a mechanism in which high resting SN-DMN connectivity increases craving susceptibility partly via a constraining effect on regulatory prefrontal reactivity to cues. These relationships were not impacted by group, suggesting that links between brain function and craving are similar regardless of smoking or vaping nicotine.

Based on the near-infrared functional brain imaging system, this research studied the hemoglobin concentration signal in resting state and task state. The purpose of this research was to analyze the activated brain regions and functional connections by exploring the changes in hemoglobin concentration and the differences in brain network functional connections between healthy people and mild to moderate AD patients. So as to identify the cognitive dysfunction of patients at an early stage. By accurately locating the area of cognitive impairment in patients, it provides a basis for precise neural regulation of physical therapy.

Patients who came to our hospital from January 2022 to December 2022 were recruited and selected according to the exclusion criteria. After receiving their informed consent, MMSE scale examination and near-infrared brain function imaging examination were performed in a relatively quiet environment.

A total of 24 subjects were included in this study, including 7 in the control group (age: 72.57 ± 7.19) and 17 (age: 76.88 ± 9.29) in the AD group. The average cognitive scores were (28.00 ± 1.16), (19.24 ± 4.89), respectively. There were no statistically significant differences in gender, years of education, age, and past medical history between the AD group and the control group (P > 0.05). In the verbal fluency test (VFT) task, there was a significant difference in the activation values of the two groups in channels 01, 06, 07, 09, 13, 14, 15, 16, 19, 21, 22, 23, 27, 29, 31, 35, 38, 40, 43, 44, 45, 51, and 52II (p < 0.05), and the activation values of the normal group were greater than those of the AD group. There was a significant difference in the mean oxygenated hemoglobin concentration in channels 01, 07, 15, 16, 21, 22, 23, 31, 35, 40, 41, 44, and 48 (p < 0.05), and the average oxygenated hemoglobin concentration in the AD group was lower than that in the normal group. There was no significant difference in activation speed between the two groups. In the resting state, the number of total network edges, DLPFC-L to PreM and SMC-L, DLPFC-L to FEF-L, DLPFC-L to DLPFC-L, FPA-L to PreM and SMC-L, FPA-L to FPA-L, FPA-R to FPA-L, DLPFC-L to DLPFC-R, FEF-R to PreM and There was a statistically significant difference in the number of network edges in SMC-L (p < 0.05). Among the different groups, the number of network edges in the AD group was smaller than that in the normal group. Correlation analysis showed that T14, T31, T16, T23, T27, M16, M22, M41 (T: represents activation value, M: represents mean hemoglobin concentration, and number represents channel number). There was a positive correlation between the total number of network edges, DLPFC-L to PreM and SMC-L, DLPFC-L to DLPFC-L, FPA-L to PreM and SMC-L, FPA-L to FPA-L, DLPFC-L to DLPFC-R, FEF-R to PreM and SMC-L, and MMSE scores (p < 0.05).

In this study, we found hemodynamic changes in the prefrontal lobes of AD patients under the VFT task, and the decrease in the functional connectivity of the prefrontal brain network in AD patients in the resting state, and these changes were associated with cognitive decline in patients. Our findings suggest that fNIRS may be used as a tool for future clinical screening for cognitive impairment, and may also be used to develop personalized preventive measures and treatment plans through accurate assessment.

In the last decade, dictionary learning (DL) has gained popularity over independent component analysis (ICA) within the blind source separation (BSS) framework for functional magnetic resonance imaging (fMRI) signals. Despite its rising popularity, a primary challenge in DL remains model fitting. It is susceptible to overfitting because the conventional loss function strives to correspond too closely to the training data. However, in the case of multi-subject (MS) analysis, it becomes imperative to overfit in order to acquire the source diversities across different brains. In this paper, an attempt has been made to resolve this predicament by concurrently preserving and mitigating the effect of high variance. A novel algorithm named joint analysis and synthesis DL (JASDL) has been proposed that simultaneously learns the overfitted trends to retain the data-centric cross-subject diversities and wellfitted trends by adequately regularizing the model complexity. This fusion was achieved by benefiting from modeling each subject's data in terms of both spatiotemporal (ST) prior information (PI) and MS-ST components. The PI consisted of biological priors derived from neuroscience knowledge, such as brain network templates, and mathematical priors derived from basis functions, such as three-dimensional (3D) cubic basis splines (B-splines). In contrast, MS-ST components were estimated using the computationally most parsimonious sparse ST blind source separation (ssBSS) method. Using the proposed analysis/synthesis cost function that exploits tri and quad-factorization for matrix approximation, the JASDL algorithm can model temporal smoothness and spatial reduction of false positives while retaining MS variations. Its efficacy was evaluated by comparing it with existing DL techniques using both experimental and synthetic fMRI datasets. Overall, the mean of correlation and F-score was found to be [Formula: see text] higher for the JASDL synthesis dictionary than the state-of-the-art subject-wise sequential DL (swsDL).

The hierarchical modular functional structure in the human brain has not been adequately depicted by conventional functional magnetic resonance imaging (fMRI) acquisition techniques and traditional functional connectivity reconstruction methods. Fortunately, rapid advancements in fMRI scanning techniques and deep learning methods open a novel frontier to map the spatial hierarchy within Brain Connectivity Networks (BCNs). The novel multiband multi-echo (MBME) fMRI technique has increased spatiotemporal resolution and peak functional sensitivity, while the advanced deep linear model (multilayer-stacked) named DEep Linear Matrix Approximate Reconstruction (DELMAR) enables the identification of hierarchical features without extensive hyperparameter tuning. We incorporate a multi-echo blood oxygenation level-dependent (BOLD) signal and DELMAR for denoising in its first layer, thereby eliminating the need for a separate multi-echo independent component analysis (ME-ICA) denoising step. Our results demonstrate that the DELMAR/Denoising/Mapping strategy produces more accurate and reproducible hierarchical BCNs than traditional ME-ICA denoising followed by DELMAR. Additionally, we showcase that MBME fMRI outperforms multiband (MB) fMRI in terms of hierarchical BCN mapping accuracy and precision. These reproducible spatial hierarchies in BCNs have significant potential for developing improved fMRI diagnostic and prognostic biomarkers of functional connectivity across a wide range of neurological and psychiatric disorders.

Naturalistic stimuli have become an effective tool to uncover the dynamic functional brain networks triggered by cognitive and emotional real-life experiences through multimodal and dynamic stimuli. However, current research predominantly focused on exploring dynamic functional connectivity generated via chosen templates under resting-state paradigm, with relatively limited investigation into the dynamic functional interactions among large-scale brain networks. Moreover, these studies might overlook the longer time-scale adaptability and information transmission that occur over extended periods during naturalistic stimuli. In this study, we introduced an unsupervised deep recurrent autoencoder (DRAE) model combined with a sliding window approach, effectively capturing the brain's long-term temporal dependencies, as measured in functional magnetic resonance imaging (fMRI), when subjects viewing a long-duration and emotional film. The experimental results revealed that naturalistic stimuli can induce dynamic large-scale brain networks, of which functional interactions covary with the development of the film's narrative. Furthermore, the dynamic interactions among brain networks were temporally synchronized with specific features of the movie, especially with the emotional arousal and valence. Our study provided novel insight to the underlying neural mechanisms of dynamic functional interactions among brain regions in an ecologically valid sensory experience.

Even during periods of rest, the brain exhibits spontaneous activity that dynamically fluctuates across spatially distributed regions in a globally coordinated manner, which has significant cognitive implications. However, the relationship between the temporal variability of resting-state networks and working memory (WM) remains largely unexplored. This study aims to address this gap by employing an EEG-based protocol combined with fuzzy entropy. First, we identified both flexible and robust patterns of dynamic resting-state networks. Subsequently, we observed a significant positive correlation between WM performance and network variability, particularly in connections associated with the frontal, right central, and right parietal lobes. Moreover, we found that the temporal variability of network properties was positively and significantly associated with WM performance. Additionally, distinct patterns of network variability were delineated, contributing to inter-individual differences in WM abilities, with these distinctions becoming more pronounced as task demands increased. Finally, using a multivariable predictive model based on these variability metrics, we effectively predicted individual WM performances. Notably, analogous analyses conducted in the source space validated the reproducibility of the temporal variability of resting-state networks in predicting individual WM behavior at higher spatial resolution, providing more precise anatomical localization of key brain regions. These results suggest that the temporal variability of resting-state networks reflects intrinsic dynamic changes in brain organization supporting WM and can serve as an objective predictor for individual WM behaviors.

This study aims to investigate the characteristics of functional connectivity (FC) in neurologically asymptomatic patients with end-stage renal disease (ESRD) undergoing hemodialysis (HD) and experiencing cognitive impairment (CI).

36 early-stage ESRD patients undergoing HD (ESHD) and 31 healthy control subjects underwent MRI scans. Abnormal FCs and networks were identified between the two groups, and correlation analysis and Area Under the Curve (AUC) analysis were conducted between abnormal FC regions and clinical variables.

The ESHD group exhibited abnormal FCs in the posterior default mode network (DMN), attention network, and external visual network (VN). Significant correlations were observed between FC values of multiple brain regions and neurocognitive scores in the ESHD group. Additionally, the FC value of the right median cingulate gyrus negatively correlated with serum calcium levels. AUC analysis demonstrated that altered FC values in the left angular gyrus and the right supramarginal gyrus effectively distinguished patients with or without CI.

In conclusion, our study reveals multiple abnormal FC regions in asymptomatic ESHD patients, affecting visual-spatial processing, short-term memory, language, attention, and executive function. Altered FCs and their negative correlation with serum calcium levels highlight a potential link between metabolic disturbances and cognitive decline, suggesting new opportunities for targeted interventions in this vulnerable population.

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.

People with Multiple Sclerosis (PwMS) often show a mismatch between disability and T2-hyperintense white matter (WM) lesion volume (LV), that in general is referred to as the clinico-radiological paradox.

This study aimed to understand how an extensive clinical, neuropsychological, and MRI analysis could better elucidate the clinico-radiological dissociation in a large cohort of PwMS.

Clinical scores, such as Expanded Disability Status Scale (EDSS), 9 Hole Peg Test (9HPT), 25-foot Walking Test (25-FWT), Paced Auditory Serial Addition Test at 3 s (PASAT3), Symbol digit Modalities Test (SDMT), demographics, and 3 T-MRI of 717 PwMS and 284 healthy subjects (HS) were downloaded from the INNI database. Considering medians of LV and EDSS scores, PwMS were divided into four groups: low LV and disability (LL/LD); high LV and low disability (HL/LD); low LV and high disability (LL/HD); high LV and disability (HL/HD). MRI measures included: volumes of gray matter (GM), WM, cerebellum, basal ganglia and thalamus, spinal cord (SC) area, and functional connectivity of resting-state networks.

The clinico-radiological dissociation involved 36% of our sample. HL/LD showed worse SDMT scores and lower global and deep GM volumes than HS and LL/LD. LL/HD showed lower GM, thalamus, and cerebellum volumes, and SC area than HS, and lower SC area than LL/LD.

A more extensive clinical assessment, including cognitive tests, and MRI evaluation including deep GM and SC, could better describe the real status of the disease and help clinicians in early and tailored treatment in PwMS.

The human brain exhibits a modular and hierarchical structure, spanning low-order sensorimotor to high-order cognitive/affective systems. What is the mechanistic significance of this organization for brain dynamics and information processing properties? We investigated this question using rare simultaneous multimodal electrophysiology (stereotactic and scalp electroencephalography - EEG) recordings in 36 patients with drug-resistant focal epilepsy during presurgical intracerebral electrical stimulation (iES) (323 stimulation sessions). Our analyses revealed an anatomical gradient of excitability across the cortex, with stronger iES-evoked EEG responses in high-order compared to low-order regions. Mathematical modeling further showed that this variation in excitability levels results from a differential dependence on recurrent feedback from non-stimulated regions across the anatomical hierarchy, and could be extinguished by suppressing those connections in-silico. High-order brain regions/networks thus show an activity pattern characterized by more inter-network functional integration than low-order ones, which manifests as a spatial gradient of excitability that is emergent from, and causally dependent on, the underlying hierarchical network structure. These findings offer new insights into how hierarchical brain organization influences cognitive functions and could inform strategies for targeted neuromodulation therapies.

Traumatic brain injury commonly impairs attention and executive function and disrupts the large-scale brain networks that support these cognitive functions. Abnormalities of functional connectivity are seen in corticostriatal networks, which are associated with executive dysfunction and damage to neuromodulatory catecholaminergic systems caused by head injury. Methylphenidate, a stimulant medication that increases extracellular dopamine and noradrenaline, can improve cognitive function following traumatic brain injury. In this experimental medicine add-on study to a randomized, double-blind, placebo-controlled clinical trial, we test whether administration of methylphenidate alters corticostriatal network function and influences drug response. Forty-three moderate-severe traumatic brain injury patients received 0.3 mg/kg of methylphenidate or placebo twice a day in 2-week blocks. Twenty-eight patients were included in the neuropsychological and functional imaging analysis (four females, mean age 40.9 ± 12.7 years, range 20-65 years) and underwent functional MRI and neuropsychological assessment after each block. 123I-Ioflupane single-photon emission computed tomography dopamine transporter scans were performed, and specific binding ratios were extracted from caudate subdivisions. Functional connectivity and the relationship to cognition were compared between drug and placebo conditions. Methylphenidate increased caudate to anterior cingulate cortex functional connectivity compared with placebo and decreased connectivity from the caudate to the default mode network. Connectivity within the default mode network was also decreased by methylphenidate administration, and there was a significant relationship between caudate functional connectivity and dopamine transporter binding during methylphenidate administration. Methylphenidate significantly improved executive function in traumatic brain injury patients, and this was associated with alterations in the relationship between executive function and right anterior caudate functional connectivity. Functional connectivity is strengthened to brain regions, including the anterior cingulate, that are activated when attention is focused externally. These results show that methylphenidate alters caudate interactions with cortical brain networks involved in executive control. In contrast, caudate functional connectivity reduces to default mode network regions involved in internally focused attention and that deactivate during tasks that require externally focused attention. These results suggest that the beneficial cognitive effects of methylphenidate might be mediated through its impact on the caudate. Methylphenidate differentially influences how the caudate interacts with large-scale functional brain networks that exhibit co-ordinated but distinct patterns of activity required for attentionally demanding tasks.

Schizophrenia patients with auditory verbal hallucinations (AVH) frequently exhibit brain structural alterations, particularly reductions in gray matter volume (GMV).Understanding the neurobiological mechanisms underlying the changes is essential for advancing treatment strategies. To address this, a meta-analysis was conducted to identify GMV changes in schizophrenia patients with AVH and their associations with gene expression and neurotransmitter receptor profiles. The results indicated significant GMV reductions in the left and the right insula, as well as the left anterior cingulate cortex. Ontology analysis of genes associated with GMV alternations revealed enrichment in biological processes related to ion transport and synaptic transmission. Hub genes from the KCN, SCN, GN, and PRK families, along with neurotransmitter receptors such as D2, VAChT, and mGluR5, showed significant correlations with GMV changes. Furthermore, multivariate linear regression analysis demonstrated that GNB2, GNB4, PRKCG, D2, and mGluR5 significantly predicted GMV alternations. These findings suggest that GMV reductions in schizophrenia with AVH are linked to disruptions in neurobiological processes involving specific genes and neurotransmitter systems, highlighting the potential targets for therapeutic intervention.

Isolated rapid-eye movement (REM) sleep behavior disorder (iRBD) is characterized by abnormal behaviors in REM sleep and is considered as a prodromal symptom of alpha-synucleinopathies. Resting-state functional magnetic resonance imaging (rsfMRI) studies have unveiled altered functional connectivity (rsFC) in patients with iRBD. However, the associations between intra- and inter-network rsFC with clinical symptoms and neuropsychological functioning in iRBD remain unclear.

To characterize intra- and inter-network rsFC in iRBD patients using a data-driven approach and to assess its associations with clinical features and cognitive functioning.

Forty-two patients with iRBD and 45 healthy controls (HC) underwent rsfMRI and comprehensive neuropsychological testing. Resting-state networks were characterized using independent component analyses. Group differences in intra- and inter-network rsFC and their associations with clinical and neuropsychological data were studied. A threshold of corrected P < 0.05 was used in all the analyses.

iRBD patients displayed lower intra-network rsFC within basal ganglia, visual, sensorimotor, and cerebellar networks, relative to HC. Mean rsFC strength within the basal ganglia network positively correlated with processing speed and negatively with the non-motor symptoms in iRBD patients. Reduced inter-network rsFC between sensorimotor and visual medial networks was observed in iRBD patients, which was associated with global cognitive status.

iRBD is characterized by both reductions in intra-network rsFC in cortical and subcortical networks and inter-network dysconnectivity between sensorimotor and visual networks. Abnormalities in intra- and inter-network rsFC are associated with cognitive performance and non-motor symptoms, suggesting the utility of both rsFC measures as imaging markers in prodromal alpha-synucleinopathies. © 2025 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

In the current study, we used functional near-infrared spectroscopy (fNIRS) to examine functional connectivity (FC) in relation to measures of cognitive flexibility and autistic features in non-autistic children. Previous research suggests that disruptions in FC between brain regions may underlie the cognitive and behavioral traits of autism. Moreover, research has identified a broader autistic phenotype (BAP), which refers to a set of behavioral traits that fall along a continuum of behaviors typical for autism but which do not cross a clinically relevant threshold. Thus, by examining FC in relation to the BAP in non-autistic children, we can better understand the spectrum of behaviors related to this condition and their neural basis. Results indicated age-related differences in performance across three measures of cognitive flexibility, as expected given the rapid development of this skill within this time period. Additionally, results showed that across the flexibility tasks, measures of autistic traits were associated with weaker FC along the executive control network, though task performance was not associated with FC. These results suggest that behavioral scores may be less sensitive than neural measures to autistic traits. Further, these results corroborate the use of broader autistic traits and the BAP to better understand disruptions to neural function associated with autism.

It is well accepted that the brain is functionally organized into multiple networks and extensive literature has demonstrated that the organization of these networks shows major changes during adolescence. Yet, there is limited option for a reference functional brain atlas derived from typically-developing adolescents, which is problematic as the reliable identification of functional brain networks crucially depends on the use of such reference functional atlases. In this context, we utilized resting-state functional MRI data from 1391 typically-developing youth aged 8-17 years to create an adolescent-specific reference atlas of functional brain networks. We further investigated the impact of age and sex on these networks. Using a multiscale individual component clustering algorithm, we identified 24 reliable functional brain networks, classified within six domains: Default-Mode (5 networks), Control (4 networks), Salience (3 networks), Attention (4 networks), Somatomotor (5 networks), and Visual (3 networks). We identified reliable and large effects of age on the spatial topography of these majority of networks, as well as on the functional network connectivity. Sex effects were not as widespread. We created a novel brain atlas, named Dev-Atlas, focused on a typically-developing sample, with the hope that this atlas can be used in future developmental neuroscience studies.

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.

Cleft lip and palate (CLP) may induce alterations in functional connectivity (FC) throughout the whole brain, potentially leading to speech dysfunctions; however, the precise neurobiological mechanisms involved remain unknown. This study aimed to systematically examine the consequences of neurological impairments associated with CLP on whole-brain FC and speech functionality. A total of 33 CLP individuals and 41 control participants were included in this study. Eight meaningful brain networks were identified through independent component analysis (ICA). The intergroup differences and correlations with speech scores for both intranetwork and internetwork FC were calculated. We observed decreased FC within the sensorimotor network (SMN), default mode network (DMN), and cerebellar network (CN) and increased FC within the executive control network (ECN). Additionally, FC was enhanced between the SMN and the auditory network (AN), attention network (ATN), and salience network (SAN); between the DMN and the visual network (VN) and ECN; and between two independent components of the DMN. Furthermore, significant correlations were observed between altered FC and speech assessment scores. Our research demonstrated that brain plasticity in CLP individuals with speech deficits involves widespread changes in brain connectivity, significantly improving our understanding of the neural basis of speech impairment in CLP individuals.

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.

Network neuroscience is an interdisciplinary field, which can be used to understand the brain by examining the connections between its constituent elements. In recent years, the application of network neuroscience approaches to study the intricate nature of the structural and functional relationships within the human brain has yielded unique insights into its organization. In this review, we begin by defining network neuroscience and providing an overview of the common metrics that describe the topology of human structural and functional brain networks. Then, we present a detailed overview of a limited but growing body of literature that has leveraged network neuroscience metrics to demonstrate the impact of mindfulness meditation on modulating the fundamental structural and functional network properties of segregation, integration, and influence. Although preliminary, results across studies suggest that mindfulness meditation results in a shift in connector hubs, such as the anterior cingulate cortex, the thalamus, and the mid-insula. Although there is mixed evidence regarding the impact of mindfulness training on global metrics of connectivity, the default mode network exhibits reduced intraconnectivity following mindfulness training. Our review also underscores essential directions for future research, including a more comprehensive examination of mindfulness training and its potential to influence structural and functional connections at the nodal, network, and whole-brain levels. Furthermore, we emphasize the importance of open science, adoption of rigorous study designs to improve the internal validity of studies, and the inclusion of diverse samples in neuroimaging studies to comprehensively characterize the impact of mindfulness on brain organization.

Emotion is a major area of research in psychology and neuroscience. However, the role of direct electrical stimulation (DES) in emotional localization has not yet been fully explored. This study aimed to analyze the use of DES in examining the local connectivity of brain regions eliciting emotional responses, thereby providing evidence for a new perspective of local changes in brain networks during emotional responses.

We reviewed the clinical data of 500 patients with refractory epilepsy who underwent stereoencephalogram (SEEG) implantation to locate the epileptogenic area and functional mapping of the brain. The three-dimensional reconstruction was employed for the qualitative and positioning analysis on the emotional responses elicited using DES. We used Granger causality (GC), directed transfer function (DTF), and partial directed coherence (PDC) to analyze the changes in functional connectivity before and after stimulation in selected patients.

Emotional responses were evoked without aura using DES in 85 contacts in 31 patients, including 35 (41.2%) contacts with fear, 37 (43.5%) contacts with happiness, 6 (7.1%) contacts with anxiety, and 7 (8.2%) contacts with depression. Three contacts of interest in two patients experiencing transient emotional symptoms underwent GC, DTF, and PDC analyses; the analysis revealed significant differences in brain networks before and after stimulation in selected patients.

DES can evoke emotions across various brain regions, such as the bilateral amygdala, hippocampus, temporal lobe, frontal lobe, insula, cingulate cortex, paracentral gyrus, fusiform, orbitofrontal cortex, left thalamus, and putamen. These elicited emotional experiences may largely result from the alterations in local brain networks.

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.