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Significance: Functional near-infrared spectroscopy (fNIRS) has been widely used to probe human brain function during task state and resting state. However, the existing analysis toolboxes mainly focus on task activation analysis, few software packages can assist resting-state fNIRS studies. Aim: We aimed to provide a versatile and easy-to-use toolbox to perform analysis for both resting state and task fNIRS. Approach: We developed a MATLAB toolbox called NIRS-KIT that works for both resting-state analysis and task activation detection. Results: NIRS-KIT implements common and necessary processing steps for performing fNIRS data analysis, including data preparation, quality control, preprocessing, individual-level analysis, group-level statistics with several popular statistical models, and multiple comparison correction methods, and finally results visualization. For resting-state fNIRS analysis, functional connectivity analysis, graph theory-based network analysis, and amplitude of low-frequency fluctuations analysis are provided. Additionally, NIRS-KIT also supports activation analysis for task fNIRS. Conclusions: NIRS-KIT offers an open source tool for researchers to analyze resting-state and/or task fNIRS data in one suite. It contains several key features: (1) good compatibility, supporting multiple fNIRS recording systems, data formats of NIRS-SPM and Homer2, and the shared near-infrared spectroscopy format data format recommended by the fNIRS society; (2) flexibility, supporting customized preprocessing scripts; (3) ease-to-use, allowing processing fNIRS signals in batch manner with user-friendly graphical user interfaces; and (4) feature-packed data viewing and result visualization. We anticipate that this NIRS-KIT will facilitate the development of the fNIRS field.

•Correlations between rest brain entropy and task activation were assessed.•In activated brain regions, lower rest entropy correlates with stronger activation.•In deactivated area, lower rest entropy correlates with stronger deactivation.•Rest brain entropy predicts the magnitude of brain activation or deactivation.•Rest entropy explains the individual differences of activation/deactivations. Brain entropy (BEN) calculated from resting state fMRI has been the subject of increasing research interest in recent years. Previous studies have shown the correlations between rest BEN and neurocognition and task performance, but how this relates to task-evoked brain activations and deactivations remains unknown. The purpose of this study is to address this open question using large data (n = 862). Voxel wise correlations were calculated between rest BEN and task activations/deactivations of five different tasks. For most of the assessed tasks, lower rest BEN was found to be associated with stronger activations (negative correlations) and stronger deactivations (positive correlations) only in brain regions activated or deactivated by the tasks. Higher workload evoked spatially more extended negative correlations between rest BEN and task activations. These results not only confirm that resting brain activity can predict brain activity during task performance but also for the first time show that resting brain activity may facilitate both task activations and deactivations. In addition, the results provide a clue to understanding the individual differences of task performance and brain activations.

The goal of this review is to describe, from a personal perspective, the development and emergence of the resting state fMRI. In particular, various concepts derived from the resting state data are discussed in detail, including connectivity, amplitude of the fluctuations, analysis techniques, and use in clinical populations. We also briefly summarize our efforts in creating an open data sharing platform as well as both a journal and a conference dedicated to brain connectivity. All three projects are aimed at significantly increasing the impact of resting state fMRI developments and enabling large, collaborative science projects. ► The development and emergence of the resting state fMRI is presented. ► Various concepts derived from resting state fMRI are discussed. ► We summarize our efforts in creating an open data sharing platform. ► Formation of a journal and a conference dedicated to brain connectivity is discussed.

Many studies have identified the role of localized and distributed cognitive functionality by mapping either local task-related activity or distributed functional connectivity (FC). However, few studies have directly explored the relationship between a brain region’s localized task activity and its distributed task FC. Here we systematically evaluated the differential contributions of task-related activity and FC changes to identify a relationship between localized and distributed processes across the cortical hierarchy. We found that across multiple tasks, the magnitude of regional task-evoked activity was high in unimodal areas, but low in transmodal areas. In contrast, we found that task-state FC was significantly reduced in unimodal areas relative to transmodal areas. This revealed a strong negative relationship between localized task activity and distributed FC across cortical regions that was associated with the previously reported principal gradient of macroscale organization. Moreover, this dissociation corresponded to hierarchical cortical differences in the intrinsic timescale estimated from resting-state fMRI and region myelin content estimated from structural MRI. Together, our results contribute to a growing literature illustrating the differential contributions of a hierarchical cortical gradient representing localized and distributed cognitive processes. •Task activations and functional connectivity changes are negatively correlated across cortex.•Task activation and connectivity dissociations reflect differences in localized and distributed processes in cortex.•Differences in localized and distributed processes are associated with differences in intrinsic timescale organization.•Differences in localized and distributed processes are associated with differences in cortical myelin content.•Cortical heterogeneity in localized and distributed processes revealed by activity flow mapping prediction error.

•We test a mechanistic model linking task activity and restFC dysfunction in AD.•This theorizes that altered restFC in AD disrupts activity flow across the brain.•The model transforms healthy into unhealthy activity via individual restFC patterns.•This reliably predicts task activity and related dysfunction in at-risk AD subjects.•We provide a theoretical basis for integrating restFC into AD biomarker development. Alzheimer's disease (AD) is linked to changes in fMRI task activations and fMRI resting-state functional connectivity (restFC), which can emerge early in the illness timecourse. These fMRI correlates of unhealthy aging have been studied in largely separate subfields. Taking inspiration from neural network simulations, we propose a unifying mechanism wherein restFC alterations associated with AD disrupt the flow of activations between brain regions, leading to aberrant task activations. We apply this activity flow model in a large sample of clinically normal older adults, which was segregated into healthy (low-risk) and at-risk subgroups based on established imaging (positron emission tomography amyloid) and genetic (apolipoprotein) AD risk factors. Modeling the flow of healthy activations over at-risk AD connectivity effectively transformed the healthy aged activations into unhealthy (at-risk) aged activations. This enabled reliable prediction of at-risk AD task activations, and these predicted activations were related to individual differences in task behavior. These results support activity flow over altered intrinsic functional connections as a mechanism underlying Alzheimer's-related dysfunction, even in very early stages of the illness. Beyond these mechanistic insights, this approach raises clinical potential by enabling prediction of task activations and associated cognitive dysfunction in individuals without requiring them to perform in-scanner cognitive tasks.

Repetitive transcranial magnetic stimulation (rTMS) has been used in the clinical treatment of Parkinson's disease (PD). Most of rTMS studies on PD used high‐frequency stimulation; however, excessive nonvoluntary movement may represent abnormally cortical excitability, which is likely to be suppressed by low‐frequency rTMS. Decreased neural activity in the basal ganglia on functional magnetic resonance imaging (fMRI) is a characteristic of PD. In the present study, we found that low‐frequency (1 Hz) rTMS targeting individual finger‐tapping activation elevated the amplitude of local neural activity (percentage amplitude fluctuation, PerAF) in the putamen as well as the functional connectivity (FC) of the stimulation target and basal ganglia in healthy participants. These results provide evidence for our hypothesis that low‐frequency rTMS over the individual task activation site can modulate deep brain functions, and that FC might serve as a bridge transmitting the impact of rTMS to the deep brain regions. It suggested that a precisely localized individual task activation site can act as a target for low‐frequency rTMS when it is used as a therapeutic tool for PD. A “Steady‐state” paradigm of finger‐tapping task detected that self‐initiated finger‐tapping was more intensively associated with the motor‐related brain area than visual‐guided. Low‐frequency rTMS targeting individual task peak activation could precisely elevated the local neural activity in the putamen. The basal ganglia neural activity may be sensitive to low‐frequency rTMS and the excessive nonvoluntary movement of PD is likely to be suppressed by low‐frequency rTMS rather than high frequency.

Insights into neurobiological mechanisms of depression are increasingly being sought via brain imaging studies. Our aim was to quantitatively summarize overlap and divergence in regions of altered brain activation associated with depression under emotionally valenced compared to cognitively demanding task conditions, and with reference to intrinsic functional connectivity. We hypothesized differences reflective of task demands. A co-ordinate-based meta-analysis technique, activation likelihood estimation, was used to analyze relevant imaging literature. These studies compared brain activity in depressed adults relative to healthy controls during three conditions: (i) emotionally valenced (cognitively easy) tasks (n = 29); (ii) cognitively demanding tasks (n = 15); and (iii) resting conditions (n = 21). The meta-analyses identified five, eight, and seven significant clusters of altered brain activity under emotion, cognition, and resting conditions, respectively, in depressed individuals compared to healthy controls. Regions of overlap and divergence between pairs of the three separate meta-analyses were quantified. There were no significant regions of overlap between emotion and cognition meta-analyses, but several divergent clusters were found. Cognitively demanding conditions were associated with greater activation of right medial frontal and insula regions while bilateral amygdala was more significantly altered during emotion (cognitively undemanding) conditions; consistent with task demands. Overlap was present in left amygdala and right subcallosal cingulate between emotion and resting meta-analyses, with no significant divergence. Our meta-analyses highlight alteration of common brain regions, during cognitively undemanding emotional tasks and resting conditions but divergence of regions between emotional and cognitively demanding tasks. Regions altered reflect current biological and system-level models of depression and highlight the relationship with task condition and difficulty.

The physiological pulsations that drive tissue fluid homeostasis are not well characterized during brain activation. Therefore, we used fast magnetic resonance encephalography (MREG) fMRI to measure full band (0 – 5 Hz) blood oxygen level-dependent (BOLDFB) signals during a dynamic visual task in 23 subjects. This revealed brain activity in the very low frequency (BOLDVLF) as well as in cardiac and respiratory bands. The cardiovascular hemodynamic envelope (CHe) signal correlated significantly with the visual BOLDVLF response, considered as an independent signal source in the V1-V2 visual cortices. The CHe preceded the canonical BOLDVLF response by an average of 1.3 (± 2.2) seconds. Physiologically, the observed CHe signal could mark increased regional cardiovascular pulsatility following vasodilation.

Background: Recently, it was reported that the extent of cortico-cortical functional connections can be estimated by the correlation coefficient based on electroencephalography (EEG) monitoring. We aimed to investigate whether the EEG correlation coefficient change with motor task activation can predict the functional outcomes of hemiparetic stroke patients. Methods: Sixteen post-stroke hemiparetic patients admitted to our rehabilitation ward were studied. On admission, EEG recording to calculate the correlation coefficient was performed at rest and during motor task activation. For the analysis of the EEG data, the program software FOCUS (NIHON KOHDEN, Japan) was used. The motor function of paretic limbs was evaluated with the Fugl–Meyer Assessment (FMA) on admission and 4 weeks after admission. Results: Significant increases in the correlation coefficient with motor task activation were noted in C3-F3 or C4-F4, C3-F7 or C4-F8, and F3-F7 or F4-F8 of the lesional hemisphere. Among them, the rate of the correlation coefficient change in F3-F7 or F4-F8 in the lesional hemisphere was significantly correlated with the rate of the upper-limb FMA score change. Conclusion: The extent of the EEG correlation coefficient change with motor task activation in F3-F7 or F4-F8 of the lesional hemisphere may help predict the motor functional outcomes of hemiparetic upper limbs after stroke.

[This corrects the article DOI: 10.3389/fnins.2025.1543206.].