Functional imaging of memory processes in humans: positron emission tomography and functional magnetic resonance imaging

Human memory is not a unitary function; it consists of multiple memory systems, with different characteristics and specialisations that are implemented in the brain. The cognitive neuroscience of human memory tries to comprehend how we encode, store, and retrieve memory items within and across those systems. The emergence of functional neuroimaging techniques offered the unprecedented opportunity to directly observe the brain regions engaged in memory functions. Brain imaging techniques can roughly be divided into those measuring the electric or magnetic fields generated by neuronal activity (EEG, magnetencephalography [MEG]) and those measuring the haemodynamic or metabolic sequelae of neuronal activity (positron emission tomography [PET], functional magnetic resonance imaging [fMRI]). Out of these techniques, the following two will be discussed in detail: fMRI and PET. Although functional neuroimaging is able to acquire images of the brain engaged in consolidating or retrieving memories, these processes are not clearly visible in the data. Statistical techniques are needed to reduce the complexity of the data and to extract the processes of interest. This article outlines the experimental and analytical procedures of neuroimaging studies with PET and fMRI. We will use a PET-study on episodic memory in human volunteers to illustrate design, analysis, and interpretation of functional imaging studies on memory.     

Spatio-temporal modeling of localized brain activity

Functional neuroimaging, including positron emission tomography (PET) and functional magnetic resonance imaging (fMRI), plays an important role in identifying specific brain regions associated with experimental stimuli or psychiatric disorders such as schizophrenia. PET and fMRI produce massive data sets that contain both temporal correlations from repeated scans and complex spatial correlations. Several methods exist for handling temporal correlations, some of which rely on transforming the response data to induce either a known or an independence covariance structure. Despite the presence of spatial correlations between the volume elements (voxels) comprising a brain scan, conventional methods perform voxel-by-voxel analyses of measured brain activity. We propose a two-stage spatio-temporal model for the estimation and testing of localized activity. Our second-stage model specifies a spatial auto-regression, capturing correlations within neural processing clusters defined by a data-driven cluster analysis. We use maximum likelihood methods to estimate parameters from our spatial autoregressive model. Our model protects against type-I errors, enables the detection of both localized and regional activations (including volume of interest effects), provides information on functional connectivity in the brain, and establishes a framework to produce spatially smoothed maps of distributed brain activity for each individual. We illustrate the application of our model using PET data from a study of working memory in individuals with schizophrenia.     

Evidence for Anomalous Network Connectivity during Working Memory Encoding in Schizophrenia: An ICA Based Analysis

Background

Numerous neuroimaging studies report abnormal regional brain activity during working memory performance in schizophrenia, but few have examined brain network integration as determined by “functional connectivity” analyses.

Methodology/Principal Findings

We used independent component analysis (ICA) to identify and characterize dysfunctional spatiotemporal networks in schizophrenia engaged during the different stages (encoding and recognition) of a Sternberg working memory fMRI paradigm. 37 chronic schizophrenia and 54 healthy age/gender-matched participants performed a modified Sternberg Item Recognition fMRI task. Time series images preprocessed with SPM2 were analyzed using ICA. Schizophrenia patients showed relatively less engagement of several distinct “normal” encoding-related working memory networks compared to controls. These encoding networks comprised 1) left posterior parietal-left dorsal/ventrolateral prefrontal cortex, cingulate, basal ganglia, 2) right posterior parietal, right dorsolateral prefrontal cortex and 3) default mode network. In addition, the left fronto-parietal network demonstrated a load-dependent functional response during encoding. Network engagement that differed between groups during recognition comprised the posterior cingulate, cuneus and hippocampus/parahippocampus. As expected, working memory task accuracy differed between groups (p<0.0001) and was associated with degree of network engagement. Functional connectivity within all three encoding-associated functional networks correlated significantly with task accuracy, which further underscores the relevance of abnormal network integration to well-described schizophrenia working memory impairment. No network was significantly associated with task accuracy during the recognition phase.

Conclusions/Significance

This study extends the results of numerous previous schizophrenia studies that identified isolated dysfunctional brain regions by providing evidence of disrupted schizophrenia functional connectivity using ICA within widely-distributed neural networks engaged for working memory cognition.     

Intrinsic connectivity reveals functionally distinct cortico-hippocampal networks in the human brain

Episodic memory depends on interactions between the hippocampus and interconnected neocortical regions. Here, using data-driven analyses of resting-state functional magnetic resonance imaging (fMRI) data, we identified the networks that interact with the hippocampus—the default mode network (DMN) and a “medial temporal network” (MTN) that included regions in the medial temporal lobe (MTL) and precuneus. We observed that the MTN plays a critical role in connecting the visual network to the DMN and hippocampus. The DMN could be further divided into 3 subnetworks: a “posterior medial” (PM) subnetwork comprised of posterior cingulate and lateral parietal cortices; an “anterior temporal” (AT) subnetwork comprised of regions in the temporopolar and dorsomedial prefrontal cortex; and a “medial prefrontal” (MP) subnetwork comprised of regions primarily in the medial prefrontal cortex (mPFC). These networks vary in their functional connectivity (FC) along the hippocampal long axis and represent different kinds of information during memory-guided decision-making. Finally, a Neurosynth meta-analysis of fMRI studies suggests new hypotheses regarding the functions of the MTN and DMN subnetworks, providing a framework to guide future research on the neural architecture of episodic memory.

Episodic memory depends on interactions between the hippocampus and interconnected neocortical regions. This study uses network analyses of intrinsic brain networks at rest to identify and characterize brain networks that interact with the hippocampus and have distinct functions during memory-guided decision making.     

Functional neuroimaging

Functional neuroimaging represents an area of brain imaging that has undergone tremendous advancements in the last decade. It is now possible to design experiments that elucidate the functional interplay between brain regions that give rise to specific human cognitive processes. Positron emission tomography (PET) and functional magnetic resonance imaging (fMRI) form the core technologies that have allowed such studies. This article reviews the basis of these techniques, their strengths and limitations, the underlying neurophysiology, and the future of functional neuroimaging.     

Brain imaging and memory systems in humans: the contribution of PET methods

The development of neuroimaging methods such as PET, has provided a new impulse to the study of the neural basis of cognitive functions, and has extended the field of inquiry from the analysis of the consequences of brain lesions to the functional investigations of brain activity, either in patients with selective neuropsychological deficits or in normal subjects engaged in cognitive tasks. Specific patterns of hypometabolism in neurological patients are associated with different profiles of memory deficits. [¹⁸F]FDG PET studies have confirmed the association of episodic memory with the structures of Papez's circuit and have shown correlations between short-term and semantic memory and the language areas. The identification of anatomo-functional networks involved in specific components of memory function in normal subjects is the aim of several PET activation studies. The results are in agreement with ‘neural network’ models of the neural basis of memory, as complex functions subserved by multiple interconnected cortical and subcortical structures.     

Origins of spatial working memory deficits in schizophrenia: an event-related FMRI and near-infrared spectroscopy study

Abnormal prefrontal functioning plays a central role in the working memory (WM) deficits of schizophrenic patients, but the nature of the relationship between WM and prefrontal activation remains undetermined. Using two functional neuroimaging methods, we investigated the neural correlates of remembering and forgetting in schizophrenic and healthy participants. We focused on the brain activation during WM maintenance phase with event-related functional magnetic resonance imaging (fMRI). We also examined oxygenated hemoglobin changes in relation to memory performance with the near-infrared spectroscopy (NIRS) using the same spatial WM task. Distinct types of correct and error trials were segregated for analysis. fMRI data indicated that prefrontal activation was increased during WM maintenance on correct trials in both schizophrenic and healthy subjects. However, a significant difference was observed in the functional asymmetry of frontal activation pattern. Healthy subjects showed increased activation in the right frontal, temporal and cingulate regions. Schizophrenic patients showed greater activation compared with control subjects in left frontal, temporal and parietal regions as well as in right frontal regions. We also observed increased 'false memory' errors in schizophrenic patients, associated with increased prefrontal activation and resembling the activation pattern observed on the correct trials. NIRS data replicated the fMRI results. Thus, increased frontal activity was correlated with the accuracy of WM in both healthy control and schizophrenic participants. The major difference between the two groups concerned functional asymmetry; healthy subjects recruited right frontal regions during spatial WM maintenance whereas schizophrenic subjects recruited a wider network in both hemispheres to achieve the same level of memory performance. Increased "false memory" errors and accompanying bilateral prefrontal activation in schizophrenia suggest that the etiology of memory errors must be considered when comparing group performances. Finally, the concordance of fMRI and NIRS data supports NIRS as an alternative functional neuroimaging method for psychiatric research.     

Biophysical mechanism of the interaction between default mode network and working memory network

Default mode network (DMN) is a functional brain network with a unique neural activity pattern that shows high activity in resting states but low activity in task states. This unique pattern has been proved to relate with higher cognitions such as learning, memory and decision-making. But neural mechanisms of interactions between the default network and the task-related network are still poorly understood. In this paper, a theoretical model of coupling the DMN and working memory network (WMN) is proposed. The WMN and DMN both consist of excitatory and inhibitory neurons connected by AMPA, NMDA, GABA synapses, and are coupled with each other only by excitatory synapses. This model is implemented to demonstrate dynamical processes in a working memory task containing encoding, maintenance and retrieval phases. Simulated results have shown that: (1) AMPA channels could produce significant synchronous oscillations in population neurons, which is beneficial to change oscillation patterns in the WMN and DMN. (2) Different NMDA conductance between the networks could generate multiple neural activity modes in the whole network, which may be an important mechanism to switch states of the networks between three different phases of working memory. (3) The number of sequentially memorized stimuli was related to the energy consumption determined by the network''s internal parameters, and the DMN contributed to a more stable working memory process. (4) Finally, this model demonstrated that, in three phases of working memory, different memory phases corresponded to different functional connections between the DMN and WMN. Coupling strengths that measured these functional connections differed in terms of phase synchronization. Phase synchronization characteristics of the contained energy were consistent with the observations of negative and positive correlations between the WMN and DMN reported in referenced fMRI experiments. The results suggested that the coupled interaction between the WMN and DMN played important roles in working memory.Supplementary InformationThe online version contains supplementary material available at 10.1007/s11571-021-09674-1.     

From cognitive to neural models of working memory

Working memory refers to the temporary retention of information that was just experienced or just retrieved from long-term memory but no longer exists in the external environment. These internal representations are short-lived, but can be stored for longer periods of time through active maintenance or rehearsal strategies, and can be subjected to various operations that manipulate the information in such a way that makes it useful for goal-directed behaviour. Empirical studies of working memory using neuroscientific techniques, such as neuronal recordings in monkeys or functional neuroimaging in humans, have advanced our knowledge of the underlying neural mechanisms of working memory. This rich dataset can be reconciled with behavioural findings derived from investigating the cognitive mechanisms underlying working memory. In this paper, I review the progress that has been made towards this effort by illustrating how investigations of the neural mechanisms underlying working memory can be influenced by cognitive models and, in turn, how cognitive models can be shaped and modified by neuroscientific data. One conclusion that arises from this research is that working memory can be viewed as neither a unitary nor a dedicated system. A network of brain regions, including the prefrontal cortex (PFC), is critical for the active maintenance of internal representations that are necessary for goal-directed behaviour. Thus, working memory is not localized to a single brain region but probably is an emergent property of the functional interactions between the PFC and the rest of the brain.     

正常人长时数字记忆信息提取的神经基础的功能磁共振研究

本文旨在通过功能磁共振成像(functional magnetic resonance imaging,fMRI)技术研究正常人进行长时数字记忆信息提取的神经基础。选取22名右利手志愿者进行长时数字记忆任务实验,采用组块设计,记忆任务与对照任务交替进行,同时利用Siemens 1.5T超导型磁共振仪进行fMRI成像,采用SPM99软件进行数据分析,脑功能区定位在Talairach坐标中显示。结果显示被试者在进行长时数字记忆提取任务时,激活最显著的皮层是左侧额中回(Brodmann分区9区,BA9区),另外左额叶内侧回、左额下回、右额下回、扣带回、左顶下小叶、左顶上小叶、右顶上小叶、右颞中回、左枕舌回、左枕中回、右中脑、小脑、右尾状核尾部等结构也有激活,各大脑皮层的激活均呈现明显的左侧半球优势。根据上述结果推论,长时数字记忆由以左侧大脑半球为优势的各脑区共同参与完成,其中左侧额叶外侧面可能是信息提取的重要结构,而其它脑叶及其之间的广泛联系可能在数字信息的加工、处理和存储中起重要作用。     

Altered small-world brain networks in schizophrenia patients during working memory performance

Impairment of working memory (WM) performance in schizophrenia patients (SZ) is well-established. Compared to healthy controls (HC), SZ patients show aberrant blood oxygen level dependent (BOLD) activations and disrupted functional connectivity during WM performance. In this study, we examined the small-world network metrics computed from functional magnetic resonance imaging (fMRI) data collected as 35 HC and 35 SZ performed a Sternberg Item Recognition Paradigm (SIRP) at three WM load levels. Functional connectivity networks were built by calculating the partial correlation on preprocessed time courses of BOLD signal between task-related brain regions of interest (ROIs) defined by group independent component analysis (ICA). The networks were then thresholded within the small-world regime, resulting in undirected binarized small-world networks at different working memory loads. Our results showed: 1) at the medium WM load level, the networks in SZ showed a lower clustering coefficient and less local efficiency compared with HC; 2) in SZ, most network measures altered significantly as the WM load level increased from low to medium and from medium to high, while the network metrics were relatively stable in HC at different WM loads; and 3) the altered structure at medium WM load in SZ was related to their performance during the task, with longer reaction time related to lower clustering coefficient and lower local efficiency. These findings suggest brain connectivity in patients with SZ was more diffuse and less strongly linked locally in functional network at intermediate level of WM when compared to HC. SZ show distinctly inefficient and variable network structures in response to WM load increase, comparing to stable highly clustered network topologies in HC.     

Recruitment of unique neural systems to support visual memory in normal aging.

The performance of many cognitive tasks changes in normal aging [1] [2] [3]. Recent behavioral work has identified some tasks that seem to be performed in an age-invariant manner [4]. To understand the brain mechanisms responsible for this, we combined psychophysical measurements of visual short-term memory with positron emission tomography (PET) in young and old individuals. Participants judged the differences between two visual stimuli, and the memory load was manipulated by interposing a delay between the two stimuli. Both age groups performed the task equally well, but the neural systems supporting performance differed between young and old individuals. Although there was some overlap in the brain regions supporting performance (for example, occipital, temporal and inferior prefrontal cortices, and caudate), the functional interconnections between these common regions were much weaker in old participants. This suggests that the regions were not operating effectively as a network in old individuals. Old participants recruited unique areas, however, including medial temporal and dorsolateral prefrontal cortices. These unique areas were strongly interactive and their activity was related to performance only in old participants. Therefore, these areas may have acted to compensate for reduced interactions between the other brain areas.     

When encoding yields remembering: insights from event-related neuroimaging.

To understand human memory, it is important to determine why some experiences are remembered whereas others are forgotten. Until recently, insights into the neural bases of human memory encoding, the processes by which information is transformed into an enduring memory trace, have primarily been derived from neuropsychological studies of humans with select brain lesions. The advent of functional neuroimaging methods, such as positron emission tomography (PET) and functional magnetic resonance imaging (fMRI), has provided a new opportunity to gain additional understanding of how the brain supports memory formation. Importantly, the recent development of event-related fMRI methods now allows for examination of trial-by-trial differences in neural activity during encoding and of the consequences of these differences for later remembering. In this review, we consider the contributions of PET and fMRI studies to the understanding of memory encoding, placing a particular emphasis on recent event-related fMRI studies of the Dm effect: that is, differences in neural activity during encoding that are related to differences in subsequent memory. We then turn our attention to the rich literature on the Dm effect that has emerged from studies using event-related potentials (ERPs). It is hoped that the integration of findings from ERP studies, which offer higher temporal resolution, with those from event-related fMRI studies, which offer higher spatial resolution, will shed new light on when and why encoding yields subsequent remembering.     

Assessing the influence of different ROI selection strategies on functional connectivity analyses of fMRI data acquired during steady-state conditions

In blood oxygen level dependent (BOLD) functional magnetic resonance imaging (fMRI), assessing functional connectivity between and within brain networks from datasets acquired during steady-state conditions has become increasingly common. However, in contrast to connectivity analyses based on task-evoked signal changes, selecting the optimal spatial location of the regions of interest (ROIs) whose timecourses will be extracted and used in subsequent analyses is not straightforward. Moreover, it is also unknown how different choices of the precise anatomical locations within given brain regions influence the estimates of functional connectivity under steady-state conditions. The objective of the present study was to assess the variability in estimates of functional connectivity induced by different anatomical choices of ROI locations for a given brain network. We here targeted the default mode network (DMN) sampled during both resting-state and a continuous verbal 2-back working memory task to compare four different methods to extract ROIs in terms of ROI features (spatial overlap, spatial functional heterogeneity), signal features (signal distribution, mean, variance, correlation) as well as strength of functional connectivity as a function of condition. We show that, while different ROI selection methods produced quantitatively different results, all tested ROI selection methods agreed on the final conclusion that functional connectivity within the DMN decreased during the continuous working memory task compared to rest.     

Pattern classification of large-scale functional brain networks: identification of informative neuroimaging markers for epilepsy

The accurate prediction of general neuropsychiatric disorders, on an individual basis, using resting-state functional magnetic resonance imaging (fMRI) is a challenging task of great clinical significance. Despite the progress to chart the differences between the healthy controls and patients at the group level, the pattern classification of functional brain networks across individuals is still less developed. In this paper we identify two novel neuroimaging measures that prove to be strongly predictive neuroimaging markers in pattern classification between healthy controls and general epileptic patients. These measures characterize two important aspects of the functional brain network in a quantitative manner: (i) coordinated operation among spatially distributed brain regions, and (ii) the asymmetry of bilaterally homologous brain regions, in terms of their global patterns of functional connectivity. This second measure offers a unique understanding of brain asymmetry at the network level, and, to the best of our knowledge, has not been previously used in pattern classification of functional brain networks. Using modern pattern-recognition approaches like sparse regression and support vector machine, we have achieved a cross-validated classification accuracy of 83.9% (specificity: 82.5%; sensitivity: 85%) across individuals from a large dataset consisting of 180 healthy controls and epileptic patients. We identified significantly changed functional pathways and subnetworks in epileptic patients that underlie the pathophysiological mechanism of the impaired cognitive functions. Specifically, we find that the asymmetry of brain operation for epileptic patients is markedly enhanced in temporal lobe and limbic system, in comparison with healthy individuals. The present study indicates that with specifically designed informative neuroimaging markers, resting-state fMRI can serve as a most promising tool for clinical diagnosis, and also shed light onto the physiology behind complex neuropsychiatric disorders. The systematic approaches we present here are expected to have wider applications in general neuropsychiatric disorders.     

Movement-related theta rhythm in humans: coordinating self-directed hippocampal learning

The hippocampus is crucial for episodic or declarative memory and the theta rhythm has been implicated in mnemonic processing, but the functional contribution of theta to memory remains the subject of intense speculation. Recent evidence suggests that the hippocampus might function as a network hub for volitional learning. In contrast to human experiments, electrophysiological recordings in the hippocampus of behaving rodents are dominated by theta oscillations reflecting volitional movement, which has been linked to spatial exploration and encoding. This literature makes the surprising cross-species prediction that the human hippocampal theta rhythm supports memory by coordinating exploratory movements in the service of self-directed learning. We examined the links between theta, spatial exploration, and memory encoding by designing an interactive human spatial navigation paradigm combined with multimodal neuroimaging. We used both non-invasive whole-head Magnetoencephalography (MEG) to look at theta oscillations and Functional Magnetic Resonance Imaging (fMRI) to look at brain regions associated with volitional movement and learning. We found that theta power increases during the self-initiation of virtual movement, additionally correlating with subsequent memory performance and environmental familiarity. Performance-related hippocampal theta increases were observed during a static pre-navigation retrieval phase, where planning for subsequent navigation occurred. Furthermore, periods of the task showing movement-related theta increases showed decreased fMRI activity in the parahippocampus and increased activity in the hippocampus and other brain regions that strikingly overlap with the previously observed volitional learning network (the reverse pattern was seen for stationary periods). These fMRI changes also correlated with participant's performance. Our findings suggest that the human hippocampal theta rhythm supports memory by coordinating exploratory movements in the service of self-directed learning. These findings directly extend the role of the hippocampus in spatial exploration in rodents to human memory and self-directed learning.     

Age-Related Changes in Task Related Functional Network Connectivity

Aging has a multi-faceted impact on brain structure, brain function and cognitive task performance, but the interaction of these different age-related changes is largely unexplored. We hypothesize that age-related structural changes alter the functional connectivity within the brain, resulting in altered task performance during cognitive challenges. In this neuroimaging study, we used independent components analysis to identify spatial patterns of coordinated functional activity involved in the performance of a verbal delayed item recognition task from 75 healthy young and 37 healthy old adults. Strength of functional connectivity between spatial components was assessed for age group differences and related to speeded task performance. We then assessed whether age-related differences in global brain volume were associated with age-related differences in functional network connectivity. Both age groups used a series of spatial components during the verbal working memory task and the strength and distribution of functional network connectivity between these components differed across the age groups. Poorer task performance, i.e. slower speed with increasing memory load, in the old adults was associated with decreases in functional network connectivity between components comprised of the supplementary motor area and the middle cingulate and between the precuneus and the middle/superior frontal cortex. Advancing age also led to decreased brain volume; however, there was no evidence to support the hypothesis that age-related alterations in functional network connectivity were the result of global brain volume changes. These results suggest that age-related differences in the coordination of neural activity between brain regions partially underlie differences in cognitive performance.     

Functional brain network modularity captures inter- and intra-individual variation in working memory capacity

Background

Cognitive abilities, such as working memory, differ among people; however, individuals also vary in their own day-to-day cognitive performance. One potential source of cognitive variability may be fluctuations in the functional organization of neural systems. The degree to which the organization of these functional networks is optimized may relate to the effective cognitive functioning of the individual. Here we specifically examine how changes in the organization of large-scale networks measured via resting state functional connectivity MRI and graph theory track changes in working memory capacity.

Methodology/Principal Findings

Twenty-two participants performed a test of working memory capacity and then underwent resting-state fMRI. Seventeen subjects repeated the protocol three weeks later. We applied graph theoretic techniques to measure network organization on 34 brain regions of interest (ROI). Network modularity, which measures the level of integration and segregation across sub-networks, and small-worldness, which measures global network connection efficiency, both predicted individual differences in memory capacity; however, only modularity predicted intra-individual variation across the two sessions. Partial correlations controlling for the component of working memory that was stable across sessions revealed that modularity was almost entirely associated with the variability of working memory at each session. Analyses of specific sub-networks and individual circuits were unable to consistently account for working memory capacity variability.

Conclusions/Significance

The results suggest that the intrinsic functional organization of an a priori defined cognitive control network measured at rest provides substantial information about actual cognitive performance. The association of network modularity to the variability in an individual''s working memory capacity suggests that the organization of this network into high connectivity within modules and sparse connections between modules may reflect effective signaling across brain regions, perhaps through the modulation of signal or the suppression of the propagation of noise.     

Dissociable Temporo-Parietal Memory Networks Revealed by Functional Connectivity during Episodic Retrieval

Episodic memory retrieval most often recruits multiple separate processes that are thought to involve different temporal regions. Previous studies suggest dissociable regions in the left lateral parietal cortex that are associated with the retrieval processes. Moreover, studies using resting-state functional connectivity (RSFC) have provided evidence for the temporo-parietal memory networks that may support the retrieval processes. In this functional MRI study, we tested functional significance of the memory networks by examining functional connectivity of brain activity during episodic retrieval in the temporal and parietal regions of the memory networks. Recency judgments, judgments of the temporal order of past events, can be achieved by at least two retrieval processes, relational and item-based. Neuroimaging results revealed several temporal and parietal activations associated with relational/item-based recency judgments. Significant RSFC was observed between one parahippocampal region and one left lateral parietal region associated with relational recency judgments, and between four lateral temporal regions and another left lateral parietal region associated with item-based recency judgments. Functional connectivity during task was found to be significant between the parahippocampal region and the parietal region in the RSFC network associated with relational recency judgments. However, out of the four tempo-parietal RSFC networks associated with item-based recency judgments, only one of them (between the left posterior lateral temporal region and the left lateral parietal region) showed significant functional connectivity during task. These results highlight the contrasting roles of the parahippocampal and the lateral temporal regions in recency judgments, and suggest that only a part of the tempo-parietal RSFC networks are recruited to support particular retrieval processes.     

Modulatory Interactions of Resting-State Brain Functional Connectivity

The functional brain connectivity studies are generally based on the synchronization of the resting-state functional magnetic resonance imaging (fMRI) signals. Functional connectivity measures usually assume a stable relationship over time; however, accumulating studies have reported time-varying properties of strength and spatial distribution of functional connectivity. The present study explored the modulation of functional connectivity between two regions by a third region using the physiophysiological interaction (PPI) technique. We first identified eight brain networks and two regions of interest (ROIs) representing each of the networks using a spatial independent component analysis. A voxel-wise analysis was conducted to identify regions that showed modulatory interactions (PPI) with the two ROIs of each network. Mostly, positive modulatory interactions were observed within regions involved in the same system. For example, the two regions of the dorsal attention network revealed modulatory interactions with the regions related to attention, while the two regions of the extrastriate network revealed modulatory interactions with the regions in the visual cortex. In contrast, the two regions of the default mode network (DMN) revealed negative modulatory interactions with the regions in the executive network, and vice versa, suggesting that the activities of one network may be associated with smaller within network connectivity of the competing network. These results validate the use of PPI analysis to study modulation of resting-state functional connectivity by a third region. The modulatory effects may provide a better understanding of complex brain functions.