fMRI Task Research Articles

Cognitive reserve against Alzheimer's pathology is linked to brain activity during memory formation.

The cognitive reserve (CR) hypothesis posits that individuals can differ in how their brain function is disrupted by pathology associated with aging and neurodegeneration. Here, we test this hypothesis in the continuum from cognitively normal to at-risk stages for Alzheimer's Disease (AD) to AD dementia using longitudinal data from 490 participants of the DELCODE multicentric observational study. Brain function is measured using task fMRI of visual memory encoding. Using a multivariate moderation analysis, we identify a CR-related activity pattern underlying successful memory encoding that moderates the detrimental effect of AD pathological load on cognitive performance. CR is mainly represented by a more pronounced expression of the task-active network encompassing deactivation of the default mode network (DMN) and activation of inferior temporal regions including the fusiform gyrus. We devise personalized fMRI-based CR scores that moderate the impact of AD pathology on cognitive performance and are positively associated with years of education. Furthermore, higher CR scores attenuate the effect of AD pathology on cognitive decline over time. Our findings primarily provide evidence for the maintenance of core cognitive circuits including the DMN as the neural basis of CR. Individual brain activity levels of these areas during memory encoding have prognostic value for future cognitive decline.

Functional localization of the human auditory and visual thalamus using a thalamic localizer functional magnetic resonance imaging task

Abstract Functional magnetic resonance imaging (fMRI) of the auditory and visual sensory systems of the human brain is an active area of investigation in the study of human health and disease. The medial geniculate nucleus (MGN) and lateral geniculate nucleus (LGN) are key thalamic nuclei involved in the processing and relay of auditory and visual information, respectively, and are the subject of blood-oxygen-level-dependent (BOLD) fMRI studies of neural activation and functional connectivity in human participants. However, localization of BOLD fMRI signal originating from neural activity in MGN and LGN remains a technical challenge, due, in part, to the poor definition of boundaries of these thalamic nuclei in standard T1-weighted and T2-weighted magnetic resonance imaging sequences. Here, we report the development and evaluation of an auditory and visual sensory thalamic localizer (TL) fMRI task that produces participant-specific functionally-defined regions of interest (fROIs) of both MGN and LGN, using 3 Tesla multiband fMRI and a clustered-sparse temporal acquisition sequence, in less than 16 minutes of scan time. We demonstrate the use of MGN and LGN fROIs obtained from the TL fMRI task in standard resting-state functional connectivity (RSFC) fMRI analyses in the same participants. In RSFC analyses, we validated the specificity of MGN and LGN fROIs for signals obtained from primary auditory and visual cortex, respectively, and benchmarked their performance against alternative atlas- and segmentation-based localization methods. The TL fMRI task and analysis code (written in Presentation and MATLAB, respectively) have been made freely available to the wider research community.

Long-term neuroplasticity in language networks after anterior temporal lobe resection.

Anterior temporal lobe resection (ATLR) is an effective treatment for drug-resistant temporal lobe epilepsy (TLE), although language deficits may occur after both left and right ATLR. Functional reorganization of the language network has been observed in the ipsilateral and contralateral hemispheres within 12 months after ATLR, but little is known of longer-term plasticity effects. Our aim was to examine the plasticity of language functions up to a decade after ATLR, in relation to cognitive profiles. We examined 24 TLE patients (12 left [LTLE]) and 10 controls across four time points: pre-surgery, 4 months, 12 months, and ~9 years post-ATLR. Participants underwent standard neuropsychological assessments (naming, phonemic, and categorical fluency tests) and a verbal fluency functional magnetic resonance imaging (fMRI) task. Using a flexible factorial design, we analyzed longitudinal fMRI activations from 12 months to ~9 years post-ATLR, relative to controls, with separate analyses for people with hippocampal sclerosis (HS). Change in cognitive profiles was correlated with the long-term change in fMRI activations to determine the "efficiency" of reorganized networks. LTLE patients had increased long-term engagement of the left extra-temporal and contralateral temporal regions, with better language performance linked to bilateral activation. Those with HS exhibited more widespread bilateral activations. RTLE patients showed plasticity in the left extra-temporal regions, with better language outcomes associated with these areas. Both groups of patients achieved cognitive stability over 9 years, with more than 50% of LTLE patients improving. Older age, longer epilepsy duration, and lower pre-operative cognitive reserve negatively affected long-term language performance. Neuroplasticity continues for up to ~9 years post-epilepsy surgery in LTLE and RTLE, with effective language recovery linked to bilateral engagement of temporal and extra-temporal regions. This adaptive reorganization is associated with improved cognitive outcomes, challenging the traditional view of localized surgery effects. These findings emphasize the need for early intervention, tailored pre-operative counseling, and the potential for continued cognitive gains with extended post-ATLR rehabilitation.

Repetitive Transcranial Magnetic Stimulation on Individualized Spots Based on Task functional Magnetic Resonance Imaging Improves Swallowing Function in Poststroke Dysphagia.

Background: Functional magnetic resonance imaging (fMRI) has not previously been used to localize the swallowing functional area in repetitive transcranial magnetic stimulation (rTMS) treatment for poststroke dysphagia; Traditionally, the target area for rTMS is the hotspot, which is defined as the specific region of the brain identified as the optimal location for transcranial magnetic stimulation (TMS). This study aims to compare the network differences between the TMS hotspot and the saliva swallowing fMRI activation to determine the better rTMS treatment site and investigate changes in functional connectivity related to poststroke dysphagia using resting-state fMRI. Methods: Using an information-based approach, we conducted a single case study to explore neural functional connectivity in a patient with poststroke dysphagia before, immediately after rTMS, and 4 weeks after rTMS intervention. A total of 20 healthy participants underwent fMRI and TMS hotspot localization as a control group. Neural network alterations were assessed, and functional connections related to poststroke dysphagia were examined using resting-state fMRI. Results: Compared to the TMS-induced hotspots, the fMRI activation peaks were located significantly more posteriorly and exhibited stronger functional connectivity with bilateral postcentral gyri. Following rTMS treatment, this patient developed functional connection between the brainstem and the bilateral insula, caudate, anterior cingulate cortex, and cerebellum. Conclusion: The saliva swallowing fMRI activation peaks show more intense functional connectivity with bilateral postcentral gyri compared to the TMS hotspots. Activation peak-guided rTMS treatment improves swallowing function in poststroke dysphagia. This study proposes a novel and potentially more efficacious therapeutic target for rTMS, expanding its therapeutic options for treating poststroke dysphagia.

Prefrontal cortex engagement during an fMRI task of emotion regulation as a potential predictor of treatment response in borderline personality disorder.

Borderline personality disorder (BPD) is a severe mental illness, with high rates of co-morbid depression and suicidality. Despite the importance of optimizing treatment in BPD, little is known about how neural processes relate to individual treatment response. This study examines how baseline regional brain blood oxygen level dependent (BOLD) activation during a functional magnetic resonance imaging (fMRI) task of emotion regulation is related to treatment response following a six-month randomized clinical trial of Dialectical Behavior Therapy (DBT) or Selective Serotonin Reuptake Inhibitor (SSRI) treatment. Unmedicated females with BPD (N = 37), with recent suicidal behavior or self-injury, underwent an fMRI task in which negative personal memories were presented and they were asked to distance (i.e., downregulate their emotional response) or immerse (i.e., experience emotions freely). Patients were then randomized to DBT (N = 16) or SSRI (N = 21) treatment, with baseline and post-treatment depression and BPD severity assessed. BOLD activity in prefrontal cortex, anterior cingulate, and insula was associated with distancing. Baseline BOLD during distancing in dorsolateral, ventrolateral, and orbital prefrontal cortex (dlPFC, vlPFC, OFC) differentially predicted depression response across treatment groups, with higher activity predicting better response in the SSRI group, and lower activity predicting better response in the DBT group. All female samples. Findings indicate that greater prefrontal engagement during emotion regulation may predict more antidepressant benefit from SSRIs, whereas lower engagement may predict better response to DBT. These results suggest different mechanisms of action for SSRI and DBT treatment, and this may allow fMRI to guide individualized treatment selection.

Good vision without peripheries: behavioral and fMRI evidence

In healthy vision, bright slow-motion stimuli are processed primarily by the regions of the visual system that receive input from the central part of the scene, whereas processing of dark fast-motion stimuli is more dependent on peripheral visual input. We tested 31 retinitis pigmentosa (RP) patients with long-term loss of peripheral photoreceptors and healthy controls with temporarily limited peripheral vision. We measured motion-based acuity using random-dot kinematograms, establishing individual thresholds for differentiating a circle from an ellipse. Participants subsequently performed a functional magnetic resonance imaging (fMRI) task set at a constant level of difficulty. The results showed that limiting vision did not affect motion-acuity thresholds in control participants but did cause different brain activations than those in RP patients, indicating prompt implementation of the strategy that would be perceptually successful. Compared with controls with both full and limited vision, impaired motion acuity in RP patients led to decreased brain activation, particularly in the primary peripheral visual areas V1-3. Importantly, compared with controls in full vision, matched decreased activation in MT+/V5, salience-processing cortices and the superior temporal cortex were detected in RP patients and in controls with limited peripheral vision, revealing brain networks that compensate for the loss of peripheral vision.

Comparison of whole-brain task-modulated functional connectivity methods for fMRI task connectomics

Higher brain functions require flexible integration of information across widely distributed brain regions depending on the task context. Resting-state functional magnetic resonance imaging (fMRI) has provided substantial insight into large-scale intrinsic brain network organisation, yet the principles of rapid context-dependent reconfiguration of that intrinsic network organisation are much less understood. A major challenge for task connectome mapping is the absence of a gold standard for deriving whole-brain task-modulated functional connectivity matrices. Here, we perform biophysically realistic simulations to control the ground-truth task-modulated functional connectivity over a wide range of experimental settings. We reveal the best-performing methods for different types of task designs and their fundamental limitations. Importantly, we demonstrate that rapid (100 ms) modulations of oscillatory neuronal synchronisation can be recovered from sluggish haemodynamic fluctuations even at typically low fMRI temporal resolution (2 s). Finally, we provide practical recommendations on task design and statistical analysis to foster task connectome mapping.

Repulsion of CA3 / dentate gyrus representations is driven by distinct internal beliefs in the face of ambiguous sensory input.

Recent human neuroimaging studies of episodic memory have revealed a counterintuitive phenomenon in the hippocampus: when events are highly similar, corresponding hippocampal activity patterns are sometimes less correlated than activity patterns associated with unrelated events. This phenomenon- repulsion- is not accounted for by most theories of the hippocampus, and the conditions that trigger repulsion remain poorly understood. Here, we used a spatial route-learning task and high-resolution fMRI in humans to test whether hippocampal repulsion is fundamentally driven by internal beliefs about the environment. By precisely measuring participants' internal beliefs and actively manipulating them, we show that repulsion selectively occurred in hippocampal subfields CA3 and dentate gyrus when visual input was ambiguous-or even identical -but internal beliefs were distinct. These findings firmly establish conditions that elicit repulsion and have broad relevance to theories of hippocampal function and to the fields of human episodic memory and rodent spatial navigation.

Functional Magnetic Resonance Imaging in Alzheimer's Disease Drug Trials: A Mini-Review.

Alzheimer's disease (AD) is a progressive neurodegenerative pathology that leads to cognitive decline and dementia, particularly in older adults. It disrupts brain structure and function, with neurotoxic amyloid-β (Aβ) plaques being a primary pathological hallmark. Pharmacotherapeutic trials targeting Aβ and other AD pathological features aim to slow disease progression. Functional magnetic resonance imaging (fMRI) is a non-invasive tool that visualizes brain functional activity, aiding in evaluating the efficacy of AD drugs in clinical trials. This mini-review explores the role of fMRI in evaluating the impact of AD pharmacotherapeutic clinical trials conducted in the past seven years. Literature was systematically searched using two databases. The risk of bias was assessed with the Revised Cochrane risk-of-bias tool (RoB-2) for randomized clinical trials (RCTs). Four studies using fMRI to investigate AD drug efficacy were included. Cholinesterase, glutamatergic, and serotonergic drugs showed significant positive effects on brain functional activity, especially within the default mode network. Functional connectivity (FC) changes due to drug intake were linked to cerebellar and cholinergic decline in AD, correlating with improved global cognition and fMRI task performance. Recent RCTs demonstrate fMRI's ability to reveal longitudinal FC pattern changes in response to AD drug treatments across disease stages. Positive FC changes in distinct brain regions suggest potential compensatory mechanisms from drug intake. However, these drugs have limited efficacy, necessitating further research to enhance specific pharmacological interventions for clinical application.

Association of neuroimaging measures with facial emotional processing in healthy adults: a task fMRI study.

Investigating the neural processing of emotion-related neural circuits underlying emotional facial processing may help in understanding mental disorders. We used two subscales of the Toronto Alexithymia Scale to assess the emotional cognitive of 25 healthy participants. A higher score indicates greater difficulty in emotional perception. In addition, participants completed a n-back task during functional magnetic resonance imaging. Psychophysiological interaction analysis was used to explore the functional connectivity (FC) of neural circuits. Next, we used elastic-net regression analysis for feature selection and conducted correlation analysis between the neuroimaging measures and questionnaire scores. Following a 3-fold cross-validation, 5 neuroimaging measures emerged as significant features. Results of correlation analysis demonstrated that participants with higher TAS scores exhibited increased FC between the amygdala and occipital face area (OFA) during facial stimulus processing, but decreased connectivity during emotional processing. These findings suggested that individuals with poor emotional recognition exhibited increased connectivity among face-related brain regions during facial processing. However, during emotional processing, decreasing neural synchronization among neural circuits involved in emotional processing affects facial expression processing. These findings suggest potential neural marker related to subjective emotional perception, which may contribute to the diagnosis and treatment of emotional dysregulation in individuals with psychiatric conditions.

Diverse social media experiences and adolescents' depressive symptoms: the moderating role of neurobiological responsivity to rejected peers.

Adolescents' experiences with social media are complex and can impact their mental well-being differently. Our study aimed to understand how neurobiological sensitivities may moderate the association between different social media experiences and depressive symptoms. In a multiwave study, 80 adolescents (Mage = 13.06, SD = 0.58) took part in an functional magnetic resonance imaging task designed to gauge the neural responses when viewing accepted and rejected peers within their own social networks (Wave 1). We also collected self-reported measures of positive (digital social connection) and negative (digital pressure) experiences on social media and depressive symptoms (Waves 2 and 3). Our findings revealed that there were no significant associations between digital social connection, digital pressure, and depressive symptoms 1 year later. However, the association between digital social connection and depressive symptoms was moderated by neural responsivity. Specifically, for adolescents with reduced sensitivity to their rejected peers in the ventral striatum, right temporoparietal junction, and ventromedial prefrontal cortex, digital social connection was associated with reduced depressive symptoms 1 year later. These results emphasize the importance of individual differences in how adolescents' brains respond to rejected peers in shaping the impact of online experiences on their mental well-being.

A chronometric relationship between circuits underlying learning and error monitoring in the basal ganglia and salience network

Abstract Healthy individuals readily adjust their behaviour in response to errors using learning mechanisms. This raises the question of how error-related neural mechanisms underlie the learning process and its progress. In this study, 21 healthy participants performed a challenging functional magnetic resonance imaging (fMRI) task to answer this question. We assessed the evolution of error-related neural response as a function of learning progress. We tested the hypothesis that the dorsal anterior cingulate cortex (dACC) and anterior insula, key regions of the error monitoring neural circuitry, reflect both the performance of an action and its improvement. Given the nature of trial-and-error learning, we also expected an involvement of the striatum, particularly the putamen. We found that error-related neural activity (in the dACC and anterior insula) was similar following correct responses and errors in an initial learning period. However, as learning progressed, the activity continuously decreased in response to correct events and increased after errors. In opposition, during the initial learning phase, the putamen activity was modulated by errors, but, as it progressed, this region became unaffected by response outcomes. In sum, our study provides neural evidence for an interaction between the mechanisms underlying error monitoring and learning, contributing to clarifying how error-related neural responses evolve with learning.

Patterns of abnormal activations in severe mental disorders a transdiagnostic data-driven meta-analysis of task-based fMRI studies.

Studies suggest severe mental disorders (SMDs), such as schizophrenia, major depressive disorder and bipolar disorder, are associated with common alterations in brain activity, albeit with a graded level of impairment. However, discrepancies between study findings likely to results from both small sample sizes and the use of different functional magnetic resonance imaging (fMRI) tasks. To address these issues, data-driven meta-analytic approach designed to identify homogeneous brain co-activity patterns across tasks was conducted to better characterize the common and distinct alterations between these disorders. A hierarchical clustering analysis was conducted to identify groups of studies reporting similar neuroimaging results, independent of task type and psychiatric diagnosis. A traditional meta-analysis (activation likelihood estimation) was then performed within each of these groups of studies to extract their aberrant activation maps. A total of 762 fMRI study contrasts were targeted, comprising 13 991 patients with SMDs. Hierarchical clustering analysis identified 5 groups of studies (meta-analytic groupings; MAGs) being characterized by distinct aberrant activation patterns across SMDs: (1) emotion processing; (2) cognitive processing; (3) motor processes, (4) reward processing, and (5) visual processing. While MAG1 was mostly commonly impaired, MAG2 was more impaired in schizophrenia, while MAG3 and MAG5 revealed no differences between disorder. MAG4 showed the strongest between-diagnoses differences, particularly in the striatum, posterior cingulate cortex, and ventromedial prefrontal cortex. SMDs are characterized mostly by common deficits in brain networks, although differences between disorders are also present. This study highlights the importance of studying SMDs simultaneously rather than independently.

Comparison of whole-brain task-modulated functional connectivity methods for fMRI task connectomics.

Higher brain functions require flexible integration of information across widely distributed brain regions depending on the task context. Resting-state functional magnetic resonance imaging (fMRI) has provided substantial insight into large-scale intrinsic brain network organisation, yet the principles of rapid context-dependent reconfiguration of that intrinsic network organisation are much less understood. A major challenge for task connectome mapping is the absence of a gold standard for deriving whole-brain task-modulated functional connectivity matrices. Here, we perform biophysically realistic simulations to control the ground-truth task-modulated functional connectivity over a wide range of experimental settings. We reveal the best-performing methods for different types of task designs and their fundamental limitations. Importantly, we demonstrate that rapid (100 ms) modulations of oscillatory neuronal synchronisation can be recovered from sluggish haemodynamic fluctuations even at typically low fMRI temporal resolution (2 s). Finally, we provide practical recommendations on task design and statistical analysis to foster task connectome mapping.

Oxytocin moderates fMRI connectivity and response to implicit threat processing in cocaine use disorder

Stressful social experiences play an important role in increasing vulnerability to substance use, including cocaine. Oxytocin (OXT), known for its anxiolytic properties and involvement in social functioning, has been suggested as a potential therapeutic for cocaine use disorder (CUD). However, limited research has explored OXT’s influence on social stress in CUD, and no study has examined its effects on neural response to subconscious (implicit) social threat cues in this population. To address this gap, the present study administered intranasal OXT (24 IU) or placebo (PBO) to participants with CUD (CUD+, N = 76) or without CUD (CUD-, N = 61) in a randomized parallel design. Participants then completed a functional magnetic resonance imaging (fMRI) task involving briefly presented facial fear and anger (i.e., threat) cues, followed by neutral face stimuli. Whole-brain activation and amygdala functional connectivity (using psychophysiological interaction modeling) were examined in response to the facial threat cues. OXT reduced activation in the thalamus and pontine reticular formation in response to fear cues, and in the supplementary motor area for both fear and anger cues, regardless of CUD status. Additionally, under PBO, amygdala-medial prefrontal cortex connectivity to fear stimuli was negative for the CUD+ group, but under OXT, this coupling was positive, similar to the positive coupling observed for the CUD- group under both PBO and OXT administration. The finding of OXT-mediated reversal of amygdala-prefrontal coupling was specific to CUD+ and suggests that OXT alters circuitry related to threat surveillance and implicit emotion regulation in CUD. However, additional research is needed to determine whether these alterations due to OXT have clinical significance in CUD.

Brain activity associated with emotion regulation predicts individual differences in working memory ability.

Previous behavioral research has found that working memory is associated with emotion regulation efficacy. However, there has been mixed evidence as to whether the neural mechanisms between emotion regulation and working memory overlap. The present study tested the prediction that individual differences on the working memory subtest of the Weschler Adult Intelligence Scale (WAIS-IV) could be predicted from the pattern of brain activity produced during emotion regulation in regions typically associated with working memory, such as the dorsal lateral prefrontal cortex (dlPFC). A total of 101 participants completed an emotion regulation fMRI task in which they either viewed or reappraised negative images. Participants also completed working memory test outside the scanner. A whole brain covariate analysis contrasting the reappraise negative and view negative BOLD response found that activity in the right dlPFC positively related to working memory ability. Moreover, a multivoxel pattern analysis approach using tenfold cross-validated support vector regression in regions-of-interest associated with working memory, including bilateral dlPFC, demonstrated that we could predict individual differences in working memory ability from the pattern of activity associated with emotion regulation. These findings support the idea that emotion regulation shares underlying cognitive processes and neural mechanisms with working memory, particularly in the dlPFC.

Heterogenous brain activations across individuals localize to a common network.

Task functional magnetic resonance imaging research has generally shielded away from studying individuals due to the low reproducibility. Here, we propose that heterogeneous brain activations across individuals localize to a common network. To test this hypothesis, we use working memory (WM) as our example. First, we showed that discrete-brain-based reproducibility of brain activation during WM across individuals was low. Then, we used activation network mapping (ANM) technique to identify each individual's brain network of WM and found that network-based reproducibility was rather high. Prediction analyses using machine learning algorithms indicated that individual WM networks identified via ANM can predict WM behavioral performance. This predictive ability even outperformed that of brain activations. Our study provides a new explanation on the low reproducibility of brain activations across individuals. The results suggest that ANM can be used to identify individual brain networks of cognitive processes, thus promising broad potential applications.

Individual cognitive traits can be predicted from task-based dynamic functional connectivity with a deep convolutional-recurrent model.

There has been increased interest in understanding the neural substrates of intelligence and several human traits from neuroimaging data. Deep learning can be used to predict different cognitive measures, such as general and fluid intelligence, from different functional magnetic resonance imaging experiments providing information about the main brain areas involved in these predictions. Using neuroimaging and behavioral data from 874 subjects provided by the Human Connectome Project, we predicted various cognitive scores using dynamic functional connectivity derived from language and working memory functional magnetic resonance imaging task states, using a 360-region multimodal atlas. The deep model joins multiscale convolutional and long short-term memory layers and was trained under a 10-fold stratified cross-validation. We removed the confounding effects of gender, age, total brain volume, motion and the multiband reconstruction algorithm using multiple linear regression. We can explain 17.1% and 16% of general intelligence variance for working memory and language tasks, respectively. We showed that task-based dynamic functional connectivity has more predictive power than resting-state dynamic functional connectivity when compared to the literature and that removing confounders significantly reduces the prediction performance. No specific cortical network showed significant relevance in the prediction of general and fluid intelligence, suggesting a spatial homogeneous distribution of the intelligence construct in the brain.

Bayesian inference for group-level cortical surface image-on-scalar regression with Gaussian process priors.

In regression-based analyses of group-level neuroimage data, researchers typically fit a series of marginal general linear models to image outcomes at each spatially referenced pixel. Spatial regularization of effects of interest is usually induced indirectly by applying spatial smoothing to the data during preprocessing. While this procedure often works well, the resulting inference can be poorly calibrated. Spatial modeling of effects of interest leads to more powerful analyses; however, the number of locations in a typical neuroimage can preclude standard computing methods in this setting. Here, we contribute a Bayesian spatial regression model for group-level neuroimaging analyses. We induce regularization of spatially varying regression coefficient functions through Gaussian process priors. When combined with a simple non-stationary model for the error process, our prior hierarchy can lead to more data-adaptive smoothing than standard methods. We achieve computational tractability through a Vecchia-type approximation of our prior that retains full spatial rank and can be constructed for a wide class of spatial correlation functions. We outline several ways to work with our model in practice and compare performance against standard vertex-wise analyses and several alternatives. Finally, we illustrate our methods in an analysis of cortical surface functional magnetic resonance imaging task contrast data from a large cohort of children enrolled in the adolescent brain cognitive development study.

Task-dependent functional connectivity changes in response to varying levels of social support.

Having social support improves one's health outcomes and self-esteem, and buffers the negative impact of stressors. Previous studies have explored the association between social support and brain activity, but evidence from task-dependent functional connectivity is still limited. We aimed to explore how gradually decreasing levels of social support influence task-dependent functional connectivity across several major neural networks. We designed a social support task and recruited 72 young adults from real-life social groups. Of the four members in each group, one healthy participant (18 participants in total) completed the functional magnetic resonance imaging (fMRI) scan. The fMRI task included three phases with varying levels of social support: high-support phase, fair phase and low-support phase. Functional connectivity changes according to three phases were examined by generalised psychophysiological interaction analysis. The results of the analysis demonstrated that participants losing expected support showed increased connectivity among salience network, default mood network and frontoparietal network nodes during the fair phase compared with the high-support phase. During the low-support phase, participants showed increased connectivity among only salience network nodes compared with the high-support phase. The results indicate that the loss of support was perceived as a threat signal and induced widespread increased functional connectivity within brain networks. The observation of significant functional connectivity changes between fair and high-support phases suggests that even a small loss of social support from close ones leads to major changes in brain function.