Neuroanatomical and neurocognitive correlates of delusion in Alzheimer's disease and mild cognitive impairment.

BACKGROUND: Neuropsychiatric symptoms and delusions are highly prevalent among people with dementia. However, multiple roots of neurobiological bases and shared neural basis of delusion and cognitive function remain to be characterized. By utilizing a fine-grained multivariable approach, we investigated distinct neuroanatomical correlates of delusion symptoms across a large population of dementing illnesses. METHODS: In this study, 750 older adults with mild cognitive impairment and Alzheimer's disease completed brain structural imaging and neuropsychological assessment. We utilized principal component analysis followed by varimax rotation to identify the distinct multivariate correlates of cortical thinning patterns. Five of the cognitive domains were assessed whether the general cognitive abilities mediate the association between cortical thickness and delusion. RESULTS: The result showed that distributed thickness patterns of temporal and ventral insular cortex (component 2), inferior and lateral prefrontal cortex (component 1), and somatosensory-visual cortex (component 5) showed negative correlations with delusions. Subsequent mediation analysis showed that component 1 and 2, which comprises inferior frontal, anterior insula, and superior temporal regional thickness accounted for delusion largely through lower cognitive functions. Specifically, executive control function assessed with the Trail Making Test mediated the relationship between two cortical thickness patterns and delusions. DISCUSSION: Our findings suggest that multiple distinct subsets of brain regions underlie the delusions among older adults with cognitive impairment. Moreover, a neural loss may affect the occurrence of delusion in dementia largely due to impaired general cognitive abilities.

Association between Loneliness and Memory Function through White Matter Hyperintensities in Older Adults: The Moderating Role of Gender.

Loneliness has an important impact on memory function in late life. However, the neural mechanism by which loneliness detrimentally influences memory function remains elusive. Furthermore, it remains unclear whether the association between loneliness and memory function varies by gender. The current study aimed to investigate the neural mechanism underlying the association between loneliness and episodic memory function and explore whether it varies with gender among cognitively normal older adults. A total of 173 community-dwelling adults aged 60 years or older from the Korean Social Life, Health, and Aging Project (KSHAP) study (mean age = 71.87) underwent an assessment of loneliness, neuropsychological testing, and structural magnetic resonance imaging. The association between loneliness and episodic memory function was mediated by the volume of white matter hyperintensities (WMHs), but not by hippocampal or gray matter volumes. In addition, the association between loneliness and memory function through WMHs was significantly moderated by gender; specifically, the indirect effect was significant among men but not among women. The study suggests that WMHs may be a potential neurological mechanism that causes late-life memory dysfunction associated with loneliness in older men. The findings underscore the need for gender-specific interventions to mitigate memory impairment associated with late-life loneliness, with significant public health implications.

Facial Expressions Track Depressive Symptoms in Old Age.

Facial expressions play a crucial role in the diagnosis of mental illnesses characterized by mood changes. The Facial Action Coding System (FACS) is a comprehensive framework that systematically categorizes and captures even subtle changes in facial appearance, enabling the examination of emotional expressions. In this study, we investigated the association between facial expressions and depressive symptoms in a sample of 59 older adults without cognitive impairment. Utilizing the FACS and the Korean version of the Beck Depression Inventory-II, we analyzed both "posed" and "spontaneous" facial expressions across six basic emotions: happiness, sadness, fear, anger, surprise, and disgust. Through principal component analysis, we summarized 17 action units across these emotion conditions. Subsequently, multiple regression analyses were performed to identify specific facial expression features that explain depressive symptoms. Our findings revealed several distinct features of posed and spontaneous facial expressions. Specifically, among older adults with higher depressive symptoms, a posed face exhibited a downward and inward pull at the corner of the mouth, indicative of sadness. In contrast, a spontaneous face displayed raised and narrowed inner brows, which was associated with more severe depressive symptoms in older adults. These findings suggest that facial expressions can provide valuable insights into assessing depressive symptoms in older adults.

Erratum: A Comparison Between the Performances of Verbal and Nonverbal Fluency Tests in Discriminating Between Mild Cognitive Impairments and Alzheimer's Disease Patients and Their Brain Morphological Correlates.

[This corrects the article on p. 17 in vol. 21, PMID: 35154337.].

Brain connectivity during social exclusion differs depending on the closeness within a triad among older adults living in a village.

Social exclusion occurs in various types of social relationships, from anonymous others to close friends. However, the role that social relationships play in social exclusion is less well known because most paradigms investigating social exclusion have been done in laboratory contexts, without considering the features of individuals' real-world social relationships. Here, we addressed this gap by examining how pre-existing social relationships with rejecters may influence the brain response of individuals experiencing social exclusion. Eighty-eight older adults living in a rural village visited the laboratory with two other participants living in the same village and played Cyberball in an Magnetic Resonance Imaging scanner. Utilizing whole-brain connectome-based predictive modeling, we analyzed functional connectivity (FC) data obtained during the social exclusion task. First, we found that the level of self-reported distress during social exclusion was significantly related to sparsity, i.e. lack of closeness, within a triad. Furthermore, the sparsity was significantly predicted by the FC model, demonstrating that a sparse triadic relationship was associated with stronger connectivity patterns in brain regions previously implicated in social pain and mentalizing during Cyberball. These findings extend our understanding of how real-world social intimacy and relationships with excluders affect neural and emotional responses to social exclusion.

Lower cognitive function attenuates the convergence between self-ratings and observer ratings of depressive symptoms in late-life cognitive impairment.

OBJECTIVES: Assessment of depressive symptoms in older adults is challenging especially in the presence of risks in cognitive impairment. We aimed to examine whether the convergence between two measures of depressive symptoms (self-report and observer ratings) is affected by varying levels of cognitive function in older adults. METHODS: Self-reported scale of depression, informant-based rating of affective symptoms, and global cognitive function were assessed in 2533 older adults with no impairment, mild cognitive impairment, and Alzheimer's disease. The strength of rank-order correlation between the Geriatric Depression Scale (GDS) and behavioral ratings of the Neuropsychiatric Inventory (NPI) was examined as the metric of convergent validity. RESULTS: The results showed that the strength of convergence between the two measurements gradually decreased as a function of lowered cognitive function. Overall tendency showed that diagnoses of cognitive impairment and lower levels of cognitive function were associated with lower correspondence between the two depression measurements. The loss of convergent validity is especially evident in the behavioral symptom of apathy. CONCLUSIONS: Utilizing self-report scales of depression in older adults requires a cautious approach even with minimal or mild levels of cognitive impairment.

Relationship Between Adipokines, Cognition, and Brain Structures in Old Age Depending on Obesity.

BACKGROUND: Adipokines such as leptin and adiponectin are associated with cognitive function. Although adiposity crucially affects adipokine levels, it remains unclear whether the relationship between adipokines and cognition is influenced by obesity. METHODS: We enrolled 171 participants and divided them into participants with obesity and without obesity to explore the effect of obesity on the relationship between adipokines and cognition. In addition to plasma levels of leptin and adiponectin, multidomain cognitive functions and brain structures were assessed using neuropsychological testing and magnetic resonance imaging. Association between levels of these adipokines and Alzheimer's disease (AD) was then assessed by logistic regression. RESULTS: We found that cognitive function was negatively associated with leptin levels and leptin-to-adiponectin ratio (LAR). Such correlations between leptin and cognitive domains were prominent in participants with obesity but were not observed in those without obesity. Leptin levels were associated with lower hippocampal volumes in participants with obesity. A significant interaction of leptin and obesity was found mostly in the medial temporal lobe. Both leptin and LAR were positively associated with insulin resistance and inflammation markers in all participants. Of note, LAR was associated with a higher risk of AD after adjusting for demographic variables, Apolipoprotein E genotype, and body mass index. CONCLUSIONS: Obesity might be a factor that determines how adipokines affect brain structure and cognition. Leptin resistance might influence the relationship between adipokines and cognition. In addition, LAR rather than each adipokine levels alone may be a better indicator of AD risk in older adults with metabolic stress.

Cognitive control training enhances the integration of intrinsic functional networks in adolescents.

Introduction: We have demonstrated that intensive cognitive training can produce sustained improvements in cognitive performance in adolescents. Few studies, however, have investigated the neural basis of these training effects, leaving the underlying mechanism of cognitive plasticity during this period unexplained. Methods: In this study, we trained 51 typically developing adolescents on cognitive control tasks and examined how their intrinsic brain networks changed by applying graph theoretical analysis. We hypothesized that the training would accelerate the process of network integration, which is a key feature of network development throughout adolescence. Results: We found that the cognitive control training enhanced the integration of functional networks, particularly the cross-network integration of the cingulo-opercular network. Moreover, the analysis of additional data from older adolescents revealed that the cingulo-opercular network was more integrated with other networks in older adolescents than in young adolescents. Discussion: These findings are consistent with the hypothesis that cognitive control training may speed up network development, such that brain networks exhibit more mature patterns after training.

Distinct neuroanatomical correlates of interference-related verbal episodic memory test in healthy older adults.

Verbal learning test can include a trial of interference process that intrude initial learning and impose additional cognitive stress. However, it has been unclear whether the multiple memory processes underly different brain structural bases. We measured performances of word retrieval that represents distinct memory processes (initial learning, interference, and retention) and regional gray matter morphology from 230 cognitively unimpaired older adults. We identified three distinct multivariate pattern modes using canonical correlation analysis that map correspondence between memory and brain morphometry. The first mode comprised weights highly loaded on temporal lobe and overall performances. The second mode reflected subcortical volumes and initial learning performances. The third mode comprised thickness in the lateral prefrontal and parietal cortex and captured an ability to resist retroactive interference effect. While overall test performance reflected the temporal lobe and whole-gray matter volume, the interim trial of interference signifies neural correlates extending to subcortical and frontoparietal regions.

Clinical and biological subtypes of late-life depression.

BACKGROUND: Late-life depression (LDD) results from multiple psychosocial and neurobiological changes occurring in later life. The current study investigated how patterns of clinical symptoms and brain structural features are classified into LDD subtypes. METHOD: Self-report scale of depression, behavioral rating of affective symptoms, and brain structural imaging of white matter change and cortical thickness were assessed in 541 older adults with no cognitive impairment or mild cognitive impairment. Latent profile analysis was used to identify distinct subtypes of depression. RESULTS: The latent profile analysis identified four classes with mild to severe depressive symptoms and two classes with minimal symptoms. While the classes primarily differed in the overall severity, the combinatory patterns of clinical symptoms and neuropathological signature distinguished the classes with similar severity. The classes were distinguished in terms of whether or not neurodegenerative risk accompanied the corresponding depressive symptoms. The presence of the negative self-scheme and cortical thinning pattern notably characterized the subtypes of LDD. LIMITATIONS: The underlying etiologies of the biological subtypes are still speculative, and the current study lacks clinical history that differentiates late- and early-onset depression. CONCLUSIONS: Our finding provides insight in identifying heterogeneities of depressive disorder in later life and suggests that self-report and behavioral symptom profile in combination with white matter lesion and cortical thickness effectively characterizes distinct subtypes of LDD.

A Comparison Between the Performances of Verbal and Nonverbal Fluency Tests in Discriminating Between Mild Cognitive Impairments and Alzheimer's Disease Patients and Their Brain Morphological Correlates.

BACKGROUND AND PURPOSE: Verbal and nonverbal fluency tests are the conventional methods for examining executive function in the elderly population. However, differences in impairments result in fluency tests in patients with mild cognitive impairments (MCIs) and Alzheimer's disease (AD) and in neural correlates underlying the tests still necessitate concrete evidence. METHODS: We compared the test performances in 27 normal controls, 28 patients with MCI, and 20 with AD, and investigated morphological changes in association with the test performances using structural magnetic imaging. RESULTS: Patients with AD performed poorly across all the fluency tests, and a receiver operating characteristics curve analysis revealed that only category fluency test discriminated all the 3 groups. Association, category, and design fluency tests involved temporal and frontal regions, while letter fluency involved the cerebellum and caudate. CONCLUSIONS: Category fluency is a reliable measure for screening patients with AD and MCI, and this efficacy might be related to morphological correlates that underlie semantic and executive processing.

Multiple Cognitive and Behavioral Factors Link Association Between Brain Structure and Functional Impairment of Daily Instrumental Activities in Older Adults.

OBJECTIVE: Functional impairment in daily activity is a cornerstone in distinguishing the clinical progression of dementia. Multiple indicators based on neuroimaging and neuropsychological instruments are used to assess the levels of impairment and disease severity; however, it remains unclear how multivariate patterns of predictors uniquely predict the functional ability and how the relative importance of various predictors differs. METHOD: In this study, 881 older adults with subjective cognitive complaints, mild cognitive impairment (MCI), and dementia with Alzheimer's type completed brain structural magnetic resonance imaging (MRI), neuropsychological assessment, and a survey of instrumental activities of daily living (IADL). We utilized the partial least square (PLS) method to identify latent components that are predictive of IADL. RESULTS: The result showed distinct brain components (gray matter density of cerebellar, medial temporal, subcortical, limbic, and default network regions) and cognitive-behavioral components (general cognitive abilities, processing speed, and executive function, episodic memory, and neuropsychiatric symptoms) were predictive of IADL. Subsequent path analysis showed that the effect of brain structural components on IADL was largely mediated by cognitive and behavioral components. When comparing hierarchical regression models, the brain structural measures minimally added the explanatory power of cognitive and behavioral measures on IADL. CONCLUSION: Our finding suggests that cerebellar structure and orbitofrontal cortex, alongside with medial temporal lobe, play an important role in the maintenance of functional status in older adults with or without dementia. Moreover, the significance of brain structural volume affects real-life functional activities via disruptions in multiple cognitive and behavioral functions.

Social Network Characteristics Predict Loneliness in Older Adults.

INTRODUCTION: Social isolation is detrimental to late-life health outcomes. Although objective social isolation is a major source of perceived loneliness, how different layers of social disconnection systematically constitute the subjective experience of loneliness remains unclear. METHODS: This study focused on older adults who participated in the Korean Social Life, Health, and Aging Project (KSHAP) (n = 1,724; mean age = 72.91 years) and examined how the proximal and distal characteristics of social networks predict loneliness using a hierarchical linear regression model. The study also investigated whether the major loss of social roles (marital and working status) influences perceived loneliness through the proximal and distal aspects of social networks by cross-sectional mediation analysis. RESULTS: This study found that the proximal (subjective number of connections) and distal (brokerage and embeddedness) aspects of social networks additively explained the frequency of loneliness. Moreover, the loss of late-life social roles (marital and working status) was related to an increase in loneliness, where the distal characteristic of social networks mediated this relationship. DISCUSSION/CONCLUSION: The results of this study suggest that the proximal and the distal characteristic of social networks is a social determinant predicting loneliness in late life. Besides, the loss of bridging and cohesive position among community networks may be a critical pathway to psychosocial transition after marital and working status changes.

Loss of association between plasma irisin levels and cognition in Alzheimer's disease.

BACKGROUND: Irisin, an exercise-induced myokine, has been shown to have beneficial effects on cognitive and metabolic functions. However, previous studies assessing the levels of circulating irisin in patients with Alzheimer's disease (AD) or diabetes mellitus (DM) have provided inconsistent results. This suggests that the normal physiological action of irisin may be altered by disease-associated pathological conditions in target organs. OBJECTIVE: To investigate the association of plasma levels of irisin with cognition and brain structures according to the presence or absence of AD and DM. METHODS: Plasma levels of irisin, multi-domain cognition, and volumes of relevant brain regions were assessed using enzyme-linked immunoassay, neuropsychological test, and magnetic resonance imaging, respectively. We classified 107 participants by cognitive (cognitively normal [CN, n = 23], mild cognitive impairment [MCI, n = 49], and AD [n = 35]) and metabolic (non-DM [n = 75] and DM [n = 32]) states. RESULTS: Disease state-stratified multiple regression analyses showed that plasma levels of irisin were positively associated with cognition only in participants without AD (CN plus MCI). By contrast, in participants with AD, these associations lost significance, and furthermore, higher levels of irisin indicated smaller hippocampal, superior temporal, and inferior frontal volumes. The association between plasma irisin levels and cognition was not affected by the presence of DM. Consistently, moderation analysis revealed that the relationship between plasma irisin levels and cognition or brain structures was significantly modified by the presence of AD, not that of DM. CONCLUSION: Our findings suggest that the beneficial actions of circulating irisin on cognition may be attenuated by AD-induced pathological conditions in the brain.

Utility of Machine Learning Approach with Neuropsychological Tests in Predicting Functional Impairment of Alzheimer's Disease.

BACKGROUND: In assessing the levels of clinical impairment in dementia, a summary index of neuropsychological batteries has been widely used in describing the overall functional status. OBJECTIVE: It remains unexamined how complex patterns of the test performances can be utilized to have specific predictive meaning when the machine learning approach is applied. METHODS: In this study, the neuropsychological battery (CERAD-K) and assessment of functioning level (Clinical Dementia Rating scale and Instrumental Activities of Daily Living) were administered to 2,642 older adults with no impairment (n = 285), mild cognitive impairment (n = 1,057), and Alzheimer's disease (n = 1,300). Predictive accuracy on functional impairment level with the linear models of the single total score or multiple subtest scores (Model 1, 2) and support vector regression with low or high complexity (Model 3, 4) were compared across different sample sizes. RESULTS: The linear models (Model 1, 2) showed superior performance with relatively smaller sample size, while nonlinear models with low and high complexity (Model 3, 4) showed an improved accuracy with a larger dataset. Unlike linear models, the nonlinear models showed a gradual increase in the predictive accuracy with a larger sample size (n > 500), especially when the model training is allowed to exploit complex patterns of the dataset. CONCLUSION: Our finding suggests that nonlinear models can predict levels of functional impairment with a sufficient dataset. The summary index of the neuropsychological battery can be augmented for specific purposes, especially in estimating the functional status of dementia.

Genome-wide association study of occupational attainment as a proxy for cognitive reserve.

Occupational attainment, which represents middle-age cognitive activities, is a known proxy marker of cognitive reserve for Alzheimer's disease. Previous genome-wide association studies have identified numerous genetic variants and revealed the genetic architecture of educational attainment, another marker of cognitive reserve. However, the genetic architecture and heritability for occupational attainment remain elusive. We performed a large-scale genome-wide association study of occupational attainment with 248 847 European individuals from the UK Biobank using the proportional odds logistic mixed model method. In this analysis, we defined occupational attainment using the classified job levels formulated in the UK Standard Occupational Classification system considering the individual professional skill and academic level. We identified 30 significant loci (P < 5 × 10-8); 12 were novel variants, not associated with other traits. Among them, four lead variants were associated with genes expressed in brain tissues by expression quantitative trait loci mapping from 10 brain regions: rs13002946, rs3741368, rs11654986 and rs1627527. The single nucleotide polymorphism-based heritability was estimated to be 8.5% (standard error of the mean = 0.004) and partitioned heritability was enriched in the CNS and brain tissues. Genetic correlation analysis showed shared genetic backgrounds between occupational attainment and multiple traits, including education, intelligence, leisure activities, life satisfaction and neuropsychiatric disorders. In two-sample Mendelian randomization analysis, we demonstrated that high occupation levels were associated with reduced risk for Alzheimer's disease [odds ratio (OR) = 0.78, 95% confidence interval (CI) = 0.65-0.92 in inverse variance weighted method; OR = 0.73, 95% CI = 0.57-0.92 in the weighted median method]. This causal relationship between occupational attainment and Alzheimer's disease was robust in additional sensitivity analysis that excluded potentially pleiotropic single nucleotide polymorphisms (OR = 0.72, 95% CI = 0.57-0.91 in the inverse variance weighted method; OR = 0.72, 95% CI = 0.53-0.97 in the weighted median method). Multivariable Mendelian randomization confirmed that occupational attainment had an independent effect on the risk for Alzheimer's disease even after taking educational attainment into account (OR = 0.72, 95% CI = 0.54-0.95 in the inverse variance weighted method; OR = 0.68, 95% CI = 0.48-0.97 in the weighted median method). Overall, our analyses provide insights into the genetic architecture of occupational attainment and demonstrate that occupational attainment is a potential causal protective factor for Alzheimer's disease as a proxy marker of cognitive reserve.

Mild behavioral impairment linked to progression to Alzheimer's disease and cortical thinning in amnestic mild cognitive impairment.

Background: Mild behavioral impairment (MBI) is a neurobehavioral syndrome characterized by later life emergence of sustained neuropsychiatric symptoms, as an at-risk state for dementia. However, the associations between MBI and a risk of progression to Alzheimer's disease (AD) and its neuroanatomical correlates in mild cognitive impairment (MCI) are still unclear. Method: A total 1,184 older adults with amnestic MCI was followed for a mean of 3.1 ± 2.0 years. MBI was approximated using a transformation algorithm for the Neuropsychiatric Inventory at baseline. A two-step cluster analysis was used to identify subgroups of individuals with amnestic MCI based on profiles of 5 MBI domain symptoms (decreased motivation, affective dysregulation, impulse dyscontrol, social inappropriateness, abnormal perception/thought content). A Cox regression analysis was applied to investigate differences in the risk of progression to AD between subgroups. A subset of participants (n = 202) underwent 3D T1-weighted MRI scans at baseline and cortical thickness was compared between the subgroups of amnestic MCI patients. Result: The cluster analysis classified the patients into 3 groups: (1) patients without any MBI domain symptoms (47.4%, asymptomatic group); (2) those with only affective dysregulation (29.4%, affective dysregulation group); (3) those with multiple MBI domain symptoms, particularly affective dysregulation, decreased motivation and impulse dyscontrol (23.2%, complex group). Compared to the asymptomatic group, the complex group was associated with a higher risk of progression to AD (hazard ratio = 2.541 [1.904-3.392], p < 0.001), but the affective dysregulation group was not (1.214 [0.883-1.670], p = 0.232). In cortical thickness analysis, the complex group revealed cortical thinning bilaterally in the inferior parietal, lateral occipital, lateral superior temporal, and frontopolar regions compared with the affective dysregulation group. Conclusion: The multiple co-occuring MBI domains in individuals with amnestic MCI are associated with a higher risk of progression to AD and cortical thinning in temporal, parietal and frontal areas. These results suggest that evaluation of MBI could be useful for risk stratification for AD and appropriate intervention in MCI individuals.

Distributed functional connectivity predicts neuropsychological test performance among older adults.

Neuropsychological test is an essential tool in assessing cognitive and functional changes associated with late-life neurocognitive disorders. Despite the utility of the neuropsychological test, the brain-wide neural basis of the test performance remains unclear. Using the predictive modeling approach, we aimed to identify the optimal combination of functional connectivities that predicts neuropsychological test scores of novel individuals. Resting-state functional connectivity and neuropsychological tests included in the OASIS-3 dataset (n = 428) were used to train the predictive models, and the identified models were iteratively applied to the holdout internal test set (n = 216) and external test set (KSHAP, n = 151). We found that the connectivity-based predicted score tracked the actual behavioral test scores (r = 0.08-0.44). The predictive models utilizing most of the connectivity features showed better accuracy than those composed of focal connectivity features, suggesting that its neural basis is largely distributed across multiple brain systems. The discriminant and clinical validity of the predictive models were further assessed. Our results suggest that late-life neuropsychological test performance can be formally characterized with distributed connectome-based predictive models, and further translational evidence is needed when developing theoretically valid and clinically incremental predictive models.

Neural and social correlates of attitudinal brokerage: using the complete social networks of two entire villages.

To avoid polarization and maintain small-worldness in society, people who act as attitudinal brokers are critical. These people maintain social ties with people who have dissimilar and even incompatible attitudes. Based on resting-state functional magnetic resonance imaging (n = 139) and the complete social networks from two Korean villages (n = 1508), we investigated the individual-level neural capacity and social-level structural opportunity for attitudinal brokerage regarding gender role attitudes. First, using a connectome-based predictive model, we successfully identified the brain functional connectivity that predicts attitudinal diversity of respondents' social network members. Brain regions that contributed most to the prediction included mentalizing regions known to be recruited in reading and understanding others' belief states. This result was corroborated by leave-one-out cross-validation, fivefold cross-validation and external validation where the brain connectivity identified in one village was used to predict the attitudinal diversity in another independent village. Second, the association between functional connectivity and attitudinal diversity of social network members was contingent on a specific position in a social network, namely, the structural brokerage position where people have ties with two people who are not otherwise connected.

Similarity in functional brain connectivity at rest predicts interpersonal closeness in the social network of an entire village.

People often have the intuition that they are similar to their friends, yet evidence for homophily (being friends with similar others) based on self-reported personality is inconsistent. Functional connectomes-patterns of spontaneous synchronization across the brain-are stable within individuals and predict how people tend to think and behave. Thus, they may capture interindividual variability in latent traits that are particularly similar among friends but that might elude self-report. Here, we examined interpersonal similarity in functional connectivity at rest-that is, in the absence of external stimuli-and tested if functional connectome similarity is associated with proximity in a real-world social network. The social network of a remote village was reconstructed; a subset of residents underwent functional magnetic resonance imaging. Similarity in functional connectomes was positively related to social network proximity, particularly in the default mode network. Controlling for similarities in demographic and personality data (the Big Five personality traits) yielded similar results. Thus, functional connectomes may capture latent interpersonal similarities between friends that are not fully captured by commonly used demographic or personality measures. The localization of these results suggests how friends may be particularly similar to one another. Additionally, geographic proximity moderated the relationship between neural similarity and social network proximity, suggesting that such associations are particularly strong among people who live particularly close to one another. These findings suggest that social connectivity is reflected in signatures of brain functional connectivity, consistent with the common intuition that friends share similarities that go beyond, for example, demographic similarities.