The default network and self-generated thought: component processes, dynamic control, and clinical relevance

Reconstruction and application of multilayer brain network for juvenile myoclonic epilepsy based on link prediction

Juvenile myoclonic epilepsy (JME) exhibits abnormal functional connectivity of brain networks at multiple frequencies. We used the multilayer network model to address the heterogeneous features at different frequencies and assess the mechanisms of functional integration and segregation of brain networks in JME patients. To address the possibility of false edges or missing edges during network construction, we combined multilayer networks with link prediction techniques. Resting-state functional magnetic resonance imaging (rs-fMRI) data were procured from 40 JME patients and 40 healthy controls. The Multilayer Network framework is utilized to integrate information from different frequency bands and to fuse similarity metrics for link prediction. Finally, calculate the entropy of the multiplex degree and multilayer clustering coefficient of the reconfigured multilayer frequency network. The results showed that the multilayer brain network of JME patients had significantly reduced ability to integrate and separate information and significantly correlated with severity of JME symptoms. This difference was particularly evident in default mode network (DMN), motor and somatosensory network (SMN), and auditory network (AN). In addition, significant differences were found in the precuneus, suboccipital gyrus, middle temporal gyrus, thalamus, and insula. Results suggest that JME patients have abnormal brain function and reduced cross-frequency interactions. This may be due to changes in the distribution of connections within and between the DMN, SMN, and AN in multiple frequency bands, resulting in unstable connectivity patterns. The generation of these changes is related to the pathological mechanisms of JME and may exacerbate cognitive and behavioral problems in patients.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11571-024-10191-0.

EEG microstate biomarkers for schizophrenia: a novel approach using deep neural networks

Schizophrenia remains a challenging neuropsychiatric disorder with complex diagnostic processes. Current clinical approaches often rely on subjective assessments, highlighting the critical need for objective, quantitative diagnostic methods. This study aimed to develop a robust classification approach for schizophrenia using EEG microstate analysis and advanced machine learning techniques. We analyzed EEG signals from 14 healthy individuals and 14 patients with schizophrenia during a 15-min resting-state session across 19 EEG channels. A data augmentation strategy expanded the dataset to 56 subjects in each group. The signals were preprocessed and segmented into five frequency bands (delta, theta, alpha, beta, gamma) and five microstates (A, B, C, D, E) using k-means clustering. Five key features were extracted from each microstate: duration, occurrence, standard deviation, coverage, and frequency. A Deep Neural Network (DNN) model, along with other machine learning classifiers, was developed to classify the data. A comprehensive fivefold cross-validation approach evaluated model performance across various EEG channels, frequency bands, and feature combinations. Significant alterations in microstate transition probabilities were observed, particularly in higher frequency bands. The gamma band showed the most pronounced differences, with a notable disruption in D → A transitions (absolute difference = 0.100). The Random Forest classifier achieved the highest accuracy of 99.94% ± 0.12%, utilizing theta band features from the F8 frontal channel. The deep neural network model demonstrated robust performance with 98.31% ± 0.68% accuracy, primarily in the occipital region. Feature size 2 consistently provided optimal classification across most models. Our study introduces a novel, high-precision EEG microstate analysis approach for schizophrenia diagnosis, offering an objective diagnostic tool with potential applications in neuropsychiatric disorders. The findings reveal critical insights into neural dynamics associated with schizophrenia, demonstrating the potential for transforming clinical diagnostic practices through advanced machine learning and neurophysiological feature extraction.

The impact of cognitive bias modification of interpretation on hostile attribution bias, reactive aggression and neural mechanisms

Hostile Attribution Bias (HAB) has been demonstrated to exert adverse impacts on reactive aggression (RA). Consequently, it is essential to investigate its prevention strategies and the brain-level alterations induced by these strategies. Cognitive Bias Modification of Interpretation (CBMI) has exhibited potential in modifying HAB. The current objective of this research is to explore its influence on individuals' hostile attributions bias and reactive aggression, and conduct an exploratory study on the underlying brain change mechanisms associated with behavioral alterations. The college students with high HAB and aggression levels were randomly assigned to a trained group and a control group. The trained group participated in a one-month-long CBM-I program, whereas the control group completed one-month-long reading tasks. The results indicated significant HAB and reactive aggression reductions within the trained group. In the trained group, the resting-state functional connectivity (rs-FC) between the anterior medial prefrontal cortex (amPFC) and the calcarine exhibited a remarkable increase, and this augmentation was positively correlated with the decrease in HAB. In contrast, no significant changes were detected in the control group after training. Our results suggest that the CBM-I has an effect on ameliorating individuals' hostile attributions bias and reactive aggression and has exploratorily uncovered the corresponding changes at the brain level.

Where creativity meets aesthetics: The Mirror Model of Art revisited with fMRI

How meaning is conveyed from creator to observer is debated in the psychology of art. The Mirror Model of Art represents a theoretical framework for bridging the psychological processes that underpin creative production and aesthetic appreciation of art. Specifically, it postulates that creating art and having an aesthetic experience are "mirrored" processes such that the early stage of aesthetic appreciation corresponds to the late stage of creative production, and conversely, that the late stage of aesthetic appreciation corresponds to the early stage of creative production. We conducted a meta-analysis of fMRI studies in the visual domain to test this hypothesis. Our results reveal that creative production engages the prefrontal cortex, which we attribute to its role in idea generation, whereas aesthetic appreciation engages the visual cortex, anterior insula, parahippocampal gyrus, the fusiform gyrus, and the frontal lobes, regions involved primarily in sensory, perceptual, reward and mnemonic processing. Their direct comparison revealed that creative production was associated with greater activation in the prefrontal cortex, whereas aesthetic appreciation was associated with greater activation in the visual cortex. This meta-analysis largely supports predictions derived from the Mirror Model of Art, by providing a snapshot of neural activity in the relatively early stages in art creators' and observers' minds. Future studies that capture brain function across longer spans of time are needed to understand the expression of creativity and aesthetic appreciation in different stages of information processing in relation to the Mirror Model of Art.

Functional dysconnectivity of the triple network in women with premenstrual syndrome

OBJECTIVE

Premenstrual syndrome (PMS) is a risk factor for female depression, linked to neural circuit dysfunction. This study investigates PMS-related brain network patterns, focusing on the triple network's integration and segregation.

MATERIALS & METHODS

The study enrolled 56 PMS patients and 67 healthy controls (HCs), assessed via the Daily Record of Severity of Problems (DRSP). Functional MRI (fMRI) was analyzed using independent component analysis (ICA) to calculate functional connectivity (FC) and functional network connectivity (FNC) within and between brain networks. Correlation analysis examined links between imaging metrics and DRSP scores.

RESULTS

Compared with HCs, PMS patients showed increased FC in the left inferior frontal gyrus of the salience network (SN). Additionally, there was increased FNC between the dorsal default mode network (dDMN), while a decrease was observed between the right execution network (RECN) and SN. Conversely, the FNC between RECN and dDMN was enhanced. Significant correlations were found between the FC values within the SN and DRSP scores. Similarly, the abnormal FNC pattern also correlated significantly with DRSP scores.

CONCLUSION

Triple-network dysconnectivity may serve as a biomarker for PMS, offering insights into its pathophysiology and potential targets for network-based neuromodulation therapies.

CLINICAL RELEVANCE STATEMENT

Identifying network dysconnectivities in PMS offers potential biomarkers for diagnosis and targets for neuromodulation therapy, ultimately improving symptom management and patient outcomes.

Clinical response to neurofeedback in major depression relates to subtypes of whole-brain activation patterns during training

Major Depressive Disorder (MDD) poses a significant public health challenge due to its high prevalence and the substantial burden it places on individuals and healthcare systems. Real-time functional magnetic resonance imaging neurofeedback (rtfMRI-NF) shows promise as a treatment for this disorder, although its mechanisms of action remain unclear. This study investigated whole-brain response patterns during rtfMRI-NF training to explain interindividual variability in clinical efficacy in MDD. We analyzed data from 95 participants (67 active, 28 control) with MDD from previous rtfMRI-NF studies designed to increase left amygdala activation through positive autobiographical memory recall. Significant symptom reduction was observed in the active group (t = -4.404, d = -0.704, p < 0.001) but not in the control group (t = -1.609, d = -0.430, p = 0.111). However, left amygdala activation did not account for the variability in clinical efficacy. To elucidate the brain training process underlying the clinical effect, we examined whole-brain activation patterns during two critical phases of the neurofeedback procedure: activation during the self-regulation period, and transient responses to feedback signal presentations. Using a systematic process involving feature selection, manifold extraction, and clustering with cross-validation, we identified two subtypes of regulation activation and three subtypes of brain responses to feedback signals. These subtypes were significantly associated with the clinical effect (regulation subtype: F = 8.735, p = 0.005; feedback response subtype: F = 5.326, p = 0.008; subtypes' interaction: F = 3.471, p = 0.039). Subtypes associated with significant symptom reduction were characterized by selective increases in control regions, including lateral prefrontal areas, and decreases in regions associated with self-referential thinking, such as default mode areas. These findings suggest that large-scale brain activity during training is more critical for clinical efficacy than the level of activation in the neurofeedback target region itself. Tailoring neurofeedback training to incorporate these patterns could significantly enhance its therapeutic efficacy.

The functional overlap between respiration and global signal and its behavioral relevance

Resting-state fMRI studies encounter the challenge of interpreting fluctuations in the global signal (GS). The GS has been linked to arousal, vigilance states, cognition, and psychiatric disorders, suggesting its functional relevance. However, GS also partially arises from physiological factors, particularly respiration. In this study, we investigate whether respiration and GS exhibit functional topographic overlap in the brain and its impact on behavior. Using resting-state fMRI data from the Human Connectome Project (N = 770), we find strong spatial consistency between GS and respiration topography with regional specificity. Furthermore, canonical correlation analysis reveals a shared pattern between the GS-behavior and respiration-behavior relationships, demonstrated as the linking between default mode network and psychiatric problems. In contrast, only GS topography correlates with cognitive performance. The reliability of respiration-GS relationships is confirmed via 10-fold cross-validated canonical correlation analysis. Additionally, this relationship is not replicated for another physiological signal, i.e., cardiac activity. Our findings underscore the functional and cognitive relevance of respiration to GS, rather than mere physiological noise. We propose the importance of considering respiration's multifaceted roles in modulating GS dynamics that underpin brain-body integration supporting mental health and cognitive function.

The body intervenes: How active inference explains depression's clinical presentation

The low mood that characterises depression is accompanied by changes in bodily processes, manifested in symptoms such as insomnia, reduced appetite and fatigue. The active inference framework provides an explanation as to how mood-related symptoms are linked. It suggests that affective experiences arise from predictions about interoceptive states and their corresponding prediction errors, with the relative influence of each modified by precision weighting. Moods reflect long-term predictions about the state of the body, incorporating parameters related to sleep, appetite and energy levels. Depression emerges from the interplay between reduced confidence in long-term prospects and heightened expectation of shorter-term negative affect, which sees a re-weighting of the precision of interoceptive prediction errors. The ensuing bodily changes contribute to the emergence of depressed mood; and underpin disturbances in shorter-term interoceptive predictions and the experience of emotions such as anxiety and irritability. This framework details how interoceptive processes shape the phenomenological and symptomatic experience of depression, helping us to understand the disorder's multifaceted and often idiosyncratic clinical presentation, and with implications for the way we understand and treat depression and its co-morbidities.

Shared genetics and causal relationship between sociability and the brain's default mode network

BACKGROUND

The brain's default mode network (DMN) plays a role in social cognition, with altered DMN function being associated with social impairments across various neuropsychiatric disorders. However, the genetic basis linking sociability with DMN function remains underexplored. This study aimed to elucidate the shared genetics and causal relationship between sociability and DMN-related resting-state functional MRI (rs-fMRI) traits.

METHODS

We conducted a comprehensive genomic analysis using large-scale genome-wide association study (GWAS) summary statistics for sociability and 31 activity and 64 connectivity DMN-related rs-fMRI traits (N = 34,691-342,461). We performed global and local genetic correlations analyses and bi-directional Mendelian randomization (MR) to assess shared and causal effects. We prioritized genes influencing both sociability and rs-fMRI traits by combining expression quantitative trait loci MR analyses, the CELLECT framework - integrating single-nucleus RNA sequencing (snRNA-seq) data with GWAS - and network propagation within a protein-protein interaction network.

RESULTS

Significant local genetic correlations were identified between sociability and two rs-fMRI traits, one representing spontaneous activity within the temporal cortex, the other representing connectivity between the cingulate and angular/temporal cortices. MR analyses suggested potential causal effects of sociability on 12 rs-fMRI traits. Seventeen genes were highly prioritized, with LINGO1, ELAVL2, and CTNND1 emerging as top candidates. Among these, DRD2 was also identified, serving as a robust internal validation of our approach.

CONCLUSIONS

By combining genomic and transcriptomic data, our gene prioritization strategy may serve as a blueprint for future studies. Our findings can guide further research into the biological mechanisms underlying sociability and its role in the development, prognosis, and treatment of neuropsychiatric disorders.

Neuroimaging studies of acupuncture for depressive disorder: a systematic review of published papers from 2014 to 2024

Background

Several neuroimaging studies have confirmed that acupuncture can elicit alterations in brain networks and regions associated with depressive disorder (DD). This review provides an overview of the methodologies and results of neuroimaging investigations into the efficacy of acupuncture in treating DD, with the intention of guiding future research objectives.

Methods

Neuroimaging studies of acupuncture for DD being published between February 2, 2014 and February 2, 2024, were gathered from PubMed, Cochrane Library, EMBASE, Web of Science, China National Knowledge Infrastructure, Chongqing VIP Database, WanFang Database, and Chinese Biomedical Literature Database. The methodological quality of the studies was assessed utilizing the Risk of Bias 2.0 and Risk of Bias in Non-Randomized Studies of Interventions tools. Following a qualitative analysis of the studies, relevant information regarding acupuncture interventions and brain imaging data was extracted.

Results

A total of 26 studies met the inclusion criteria. These studies featured a combined sample size of 1138 participants. All studies employed magnetic resonance imaging. Our findings indicate that acupuncture can affect neural activity in the cingulate gyrus, precuneus, insula, prefrontal lobe, etc. The neuroimaging results of most DD patients were correlated with the Hamilton Rating Scale for Depression scores.

Conclusions

The results of the current study indicate that acupuncture treatment may have a regulatory effect on the abnormal functioning of neural regions and networks in individuals diagnosed with DD. These networks are predominantly localized within various brain regions, including the default mode network, limbic system, emotion regulation and cognitive network, reward network, central executive network, salience network, and sensorimotor network. It is essential to conduct additional high-quality and multimodal neuroimaging research to expand upon these findings and elucidate the mechanisms by which acupuncture impacts patients with DD.

Systematic Review Registration

https://www.crd.york.ac.uk/prospero/, identifier CRD42023400557.

How do brain regions specialised for concrete and abstract concepts align with functional brain networks? A neuroimaging meta-analysis

Identifying the brain regions that process concrete and abstract concepts is key to understanding the neural architecture of thought, memory and language. We review current theories of concreteness effects and test their neural predictions in a meta-analysis of 72 neuroimaging studies (1400 participants). Our analysis includes more than twice as many studies as previous meta-analyses, allowing for a more sensitive mapping of these effects across the brain. We also conducted a quantitative assessment of the degree to which concreteness effects aligned with a range of large-scale functional brain networks. Our results suggest that concrete and abstract concepts vary both in the information-processing modalities they engage and in the demands they place on cognitive control processes. Abstract concepts preferentially activated networks for social cognition (particularly for sentences), language and semantic control (particularly when presented as single words). Concrete concepts preferentially activated action processing regions when presented in sentences, though we found no evidence that they activated visual networks. Specialisation for both concept types was present in different parts of the default mode network (DMN), with effects dissociating along a social-spatial axis. Concrete concepts generated greater activation in a medial temporal DMN component, implicated in constructing mental models of spatial contexts and scenes. In contrast, abstract concepts showed greater activation in frontotemporal DMN regions involved in social and language processing. These results align with prior claims that generating models of situations and events is a core DMN function and indicate specialisation within DMN for different aspects of these models.

Network meta-analysis of the effects of long-term non-pharmacologic treatment on inhibitory control in children and adolescents with attention deficit hyperactivity disorder

OBJECTIVE

To conduct a network meta-analysis comparing the effects of various long-term non-pharmacological treatments on inhibitory control in children and adolescents with Attention-Deficit/Hyperactivity Disorder (ADHD) to provide theoretical support for non-pharmacological interventions in ADHD management.

METHODS

Randomized controlled trials (RCTs) on the effects of long-term non-pharmacological treatments on inhibitory control in children and adolescents with ADHD published up to November 11, 2024, were searched in databases such as CNKI, Web of Science, APA PsycInfo, Embase, PubMed and Cochrane Library.

RESULTS

A total of 42 studies, including seven non-pharmacological types, were included, involving 1981 children and adolescents with ADHD, with a mean age of 10.04 ± 1.82 years. Both traditional and network meta-analyses based on post-test data revealed that physical exercise, cognitive training, behavior therapy, and neurofeedback significantly improved inhibitory control (P < 0.05), with physical exercise showing the best improvement (SUCRA: 85.9 %). At the same time, board games, EMG feedback, and meditation had no significant effect (P > 0.05). Follow-up analysis showed that behavior therapy and cognitive training had a good maintenance effect (P < 0.05), with behavior therapy demonstrating the best sustained effect (SUCRA: 95.1 %). In contrast, physical exercise, board games, and neurofeedback showed diminishing effects over time and had no significant long-term effect (P > 0.05).

CONCLUSION

Existing evidence shows that physical exercise, cognitive training, behavior therapy, and neurofeedback all have a positive effect on improving inhibitory control in children and adolescents with ADHD, with physical exercise showing the best effect, though with poor maintenance, while cognitive training and behavior therapy had a slightly lower effect, but their maintenance was better.

Brain activity dynamics after traumatic brain injury indicate increased state transition energy and preference of lower order states

Traumatic Brain Injury (TBI) can cause structural damage to the neural tissue and white matter connections in the brain, disrupting its functional coactivation patterns. Although there are a wealth of studies investigating TBI-related changes in the brain's structural and functional connectomes, fewer studies have investigated TBI-related changes to the brain's dynamic landscape. Network control theory is a framework that integrates structural connectomes and functional time-series to quantify brain dynamics. Using this approach, we analyzed longitudinal trajectories of brain dynamics from acute to chronic injury phases in two cohorts of individuals with mild and moderate to severe TBI, and compared them to non-brain-injured, age- and sex-matched control individuals' trajectories. Our analyses suggest individuals with mild TBI initially have brain activity dynamics similar to controls but then shift in the subacute and chronic stages of the injury (1 month and 12 months post-injury) to favor lower-order visual-dominant states compared to higher-order default mode dominant states. We further find that, compared to controls, individuals with mild TBI have overall decreased entropy and increased transition energy demand in the sub-acute and chronic stages that correlates with poorer attention performance. Finally, we found that the asymmetry in top-down to bottom-up transition energies increased in subacute and chronic stages of mild TBI, possibly indicating decreased efficacy of top-down inhibition. We replicate most findings with the moderate to severe TBI dataset, indicating their robustness, with the notable exception of finding the opposite correlation between global transition energy and mean reaction time (MRT). We attribute differences to the cohorts' varied injury severity, with perhaps a stronger compensatory mechanism in moderate to severe TBI. Overall, our findings reveal shifting brain dynamics after mild to severe TBI that relate to behavioral measures of attention, shedding light on post-injury mechanisms of recovery.

Impact of pre-existing subclinical depressive symptoms on the mental health of older adults during the COVID-19-related confinements: assessment of moderating factors including meditation training

The COVID-19 pandemic significantly challenged mental health of populations worldwide. We aimed to assess changes in mental health of cognitively unimpaired (CU) older adults with pre-existing subclinical depressive symptoms during pandemic-related confinements, and the factors that could modulate these changes. CU older adults with (DepS, n = 53) and without (NoDepS, n = 47) pre-existing subclinical depressive symptoms (defined using the Geriatric Depression Scale at baseline) from the Age-Well randomized controlled trial (NCT02977819) were included - for whom data at baseline, post-intervention visits and during the two national confinements were available. The 18-month meditation or non-native language training intervention was completed before the pandemic. DepS, compared to NoDepS, had higher levels of depressive and anxiety symptoms at all assessments, including confinements. DepS had a greater increase in anxiety than NoDepS between the two confinements, and this increase was associated with greater ruminative brooding at baseline, but was not moderated by the meditation training intervention or by meditation practice during confinements. Pre-existing subclinical depressive symptoms in older adults contribute to mental health deterioration during confinements, with rumination being the main factor involved - stressing the need to treat these symptoms.

Habenula contributions to negative self-cognitions

Self-related cognitions are integral to personal identity and psychological wellbeing. Persistent engagement with negative self-cognitions can precipitate mental ill health; whereas the ability to restructure them is protective. Here, we leverage ultra-high field 7T fMRI and dynamic causal modelling to characterise a negative self-cognition network centred on the habenula - a small midbrain region linked to the encoding of punishment and negative outcomes. We model habenula effective connectivity in a discovery sample of healthy young adults (n = 45) and in a replication cohort (n = 56) using a cognitive restructuring task during which participants repeated or restructured negative self-cognitions. The restructuring of negative self-cognitions elicits an excitatory effect from the habenula to the posterior orbitofrontal cortex that is reliably observed across both samples. Furthermore, we identify an excitatory effect of the habenula on the posterior cingulate cortex during both the repeating and restructuring of self-cognitions. Our study provides evidence demonstrating the habenula's contribution to processing self-cognitions. These findings yield unique insights into habenula's function beyond processing external reward/punishment to include abstract internal experiences.

A longitudinal functional connectivity study of bipolar Disorder: from the view of default mode network and its association with gene expression

OBJECTIVE

Abnormal functions involving brain regions within the default mode network (DMN) have been reported in bipolar disorder (BD). However, most previous studies were cross-sectional. Therefore, a longitudinal study was conducted to observe the change trajectory of symptoms and functional connectivity (FC) of DMN in BD patients. Imaging transcriptomics is used for finding spatially transcriptional correlation of FC changes.

METHODS

Eighty-two BD patients (43 patients finished the follow-up after 3-month medication treatment) and matched 88 healthy controls were included to perform seed-based FC analysis. The correlation between FC alteration and clinical symptoms was explored with multiple regression analysis. Utilizing imaging transcriptomics, genes from the Allen Human Brain Atlas associated with abnormal imaging phenotypes were obtained by spatial Spearman correlation analysis.

RESULTS

BD patients exhibited increased FC between the DMN and the orbitofrontal cortex (OFC), left cerebellar Crus II/VIIb, right inferior frontal gyrus, and bilateral anterior cingulate cortex. After treatment, elevated FC of DMN-OFC tended to normalize, while decreased FC within the inferior parietal gyrus (IPG) was observed. Two FC alterations of left IPG were positively associated with the Stroop Color-Word Test (p = 0.0014, 0.0019 respectively). Enrichment analysis suggested association genes were involved in ribosomal function, membrane transport, and enzymatic activity.

CONCLUSION

The findings further suggest that FC of DMN may relate to symptomatic changes and therapeutic mechanisms of BD. Imaging transcriptomics provides a new perspective for researching genetic factors of BD. However, the small sample and single center of this study may impact the representativeness of the results.

Challenges and hopes for treatment of anxiety disorder in the autistic population

Anxiety disorders, marked by excessive fear and worry, are particularly prevalent in autism, affecting up to 45 % of individuals with the condition. Since the 1960s, advances in neuroscience, psychology, and psychopharmacology have enhanced understanding and treatment of anxiety disorders in general population. Standardized diagnostic criteria development facilitated accurate classification of anxiety disorders. Neurobiological research identified key brain regions forming the basis of the amygdala-centred fear circuit model. Pharmacological advancements introduced selective serotonin reuptake inhibitors (SSRIs) and serotonin-norepinephrine reuptake inhibitors (SNRIs) as safer, first-line treatments. However, these medications show limited efficacy and significant side effects in autistic individuals, highlighting the need for alternative treatments. Cognitive-behavioural therapy (CBT) has gained empirical support, helping to reduce avoidance behaviours, but modifications are often needed for autistic individuals. Emerging therapies, including Mindfulness-Based Stress Reduction for Autism Spectrum Disorder (MASSI) and virtual reality-based interventions, are being explored for individuals with more treatment-resistant anxiety. Ongoing clinical trials are assessing medications used for other psychiatric disorders to determine their efficacy in anxiety treatment for autism. Recent genetic and neuroimaging research has revealed altered brain connectivity and genetic susceptibility in anxiety, promoting the development of personalized treatments. Despite these advances, challenges remain in optimizing interventions and addressing treatment resistance, necessitating continued research and innovation.

Reflective Functioning and Financial Exploitation Vulnerability in Older Adults: The Importance of Significant Others

OBJECTIVES

Financial exploitation of older adults results in devastating economic, social, and psychological losses to older adults, their families, and society at large. This study examined the relationship between reflective functioning and financial exploitation vulnerability (FEV) and whether relationship status moderated the association.

METHODS

A community-based sample of 156 Israeli older adults age 60 and over responded to demographic questions and questionnaires assessing reflective functioning and FEV.

RESULTS

A hierarchical linear regression analysis covarying for age, sex, education, income, and sum of illnesses, revealed that higher reflective functioning was associated with lower FEV (p = .011). A main effect of relationship status was not found, but a significant interaction of reflective functioning × relationship status was discovered (p = .008), adding 4.2% to the total variance of the model. Probing the interaction revealed that the reflective functioning-FEV association was significant only for older adults not in a relationship.

CONCLUSIONS

Findings suggest that low reflective functioning may be associated with increased risk of financial exploitation, specifically in certain populations of older adults.

CLINICAL IMPLICATIONS

Care providers of older adults may consider assessing for, and identifying older adults with low reflective functioning, in order to prevent or intervene in the event of a potentially exploitative situation.

Loneliness and social conformity: A predictive processing perspective

For social creatures like humans, loneliness-which is characterized by a perceived lack of meaningful social relationships-can result in detrimental health outcomes, especially when experienced over an extended period of time. One potential way to pursue rewarding social connections could be social conformity, the tendency to align one's behavior and opinions to those of others. In this perspective article, we give a broad overview of common and distinct neural mechanisms underlying loneliness and social conformity, and the involvement of the oxytocinergic system therein. Additionally, we consider how loneliness can be understood within a predictive processing framework. Specifically, negative expectations could be related to altered representations of the self and others in the medial prefrontal cortex, whereas diminished bottom-up signals from the insula may contribute to reduced precision in the perception of the social environment. This negatively skewed internal model may perpetuate loneliness and lead to chronicity over time. While acute isolation and loneliness could drive people toward reconnection and increased social conformity, chronic loneliness may lead to distrust and avoidance, eventually resulting in nonconformity. We suggest different mediating mechanisms and moderating factors that warrant further investigation in future research.

General anaesthesia decreases the uniqueness of brain functional connectivity across individuals and species

The human brain is characterized by idiosyncratic patterns of spontaneous thought, rendering each brain uniquely identifiable from its neural activity. However, deep general anaesthesia suppresses subjective experience. Does it also suppress what makes each brain unique? Here we used functional MRI scans acquired under the effects of the general anaesthetics sevoflurane and propofol to determine whether anaesthetic-induced unconsciousness diminishes the uniqueness of the human brain, both with respect to the brains of other individuals and the brains of another species. Using functional connectivity, we report that under anaesthesia individual brains become less self-similar and less distinguishable from each other. Loss of distinctiveness is highly organized: it co-localizes with the archetypal sensory-association axis, correlating with genetic and morphometric markers of phylogenetic differences between humans and other primates. This effect is more evident at greater anaesthetic depths, reproducible across sevoflurane and propofol and reversed upon recovery. Providing convergent evidence, we show that anaesthesia shifts the functional connectivity of the human brain closer to the functional connectivity of the macaque brain in a low-dimensional space. Finally, anaesthesia diminishes the match between spontaneous brain activity and cognitive brain patterns aggregated from the Neurosynth meta-analytic engine. Collectively, the present results reveal that anaesthetized human brains are not only less distinguishable from each other, but also less distinguishable from the brains of other primates, with specifically human-expanded regions being the most affected by anaesthesia.

Cortical Gradients Support Mental Time Travel into the Past and Future: Evidence from Activation Likelihood Estimation Meta-analysis

A longstanding issue concerns the extent to which episodic autobiographical memory (EAM) and episodic future thinking (EFT) are the expression of the same cognitive ability and may be dissociated at the neural level. Here, we provided an updated picture of overlaps and dissociations between brain networks supporting EAM and EFT, using Activation Likelihood Estimation. Moreover, we tested the hypothesis that spatial gradients characterize the transition between activations associated with the two domains, in line with accounts positing a transition in the relative predominance of their features and process components. We showed the involvement of a core network across EAM and EFT, including midline structures, the bilateral hippocampus/parahippocampus, angular gyrus and anterior middle temporal gyrus (aMTG) and the left superior frontal gyrus (SFG). Contrast analyses highlighted a cluster in the right aMTG significantly more activated during EFT compared with EAM. Finally, gradiental transitions were found in the ventromedial prefrontal cortex, left SFG, and bilateral aMTG. Results show that differences between EAM and EFT may arise at least partially through the organization of specific regions of common activation along functional gradients, and help to advocate between different theoretical accounts.

The desegregation of neural networks during worry induction in late-life-an effective connectivity analysis

BACKGROUND

Severe worry is a core component of anxiety and depressive disorders and is independently associated with significant morbidity and mortality. However, the neural basis of worry is poorly understood. We investigated effective connectivity (EC) using functional magnetic resonance imaging (fMRI) of a naturalistic worry induction and reappraisal task in late-life.

METHODS

112 participants age >50 years with varying worry severity completed a personalized, in-scanner worry induction and reappraisal task. We calculated voxel-wise EC in neutral, worry, and reappraisal conditions with generalized psychophysiological interactions using seeds in the subgenual anterior cingulate cortex (ACC), dorsal ACC, and left and right amygdalae, and used paired t-tests to compare conditions. We assessed clusters for association with in-scanner worry severity using linear regression.

RESULTS

During the worry condition, EC increased between the subgenual ACC and the default mode network (DMN) and major hubs of the executive control and salience networks. Left amygdala EC to the posterior cingulate also increased during worry, and dorsal ACC connectivity to primary sensory and motor regions decreased. Reappraisal reduced subgenual and dorsal ACC EC observed during worry and the EC between the left amygdala and regions of the dorsal attention network. Broadly, left amygdala EC was robustly associated with in-scanner worry severity.

CONCLUSIONS

Worry induction robustly engaged the DMN and increased connectivity with other high-order associative networks, potentially subsuming cortical resources. Reappraisal reduced these connectivities and disengaged the amygdala from areas associated with top-down attention. These findings could inform targets for neuromodulatory treatment of severe worry in older adults.

Modeling functional connectivity with learning and memory in a mouse model of Alzheimer's disease

Introduction

Functional connectivity (FC) is a metric of how different brain regions interact with each other. Although there have been some studies correlating learning and memory with FC, there have not yet been, to date, studies that use machine learning (ML) to explain how FC changes can be used to explain behavior not only in healthy mice, but also in mouse models of Alzheimer's Disease (AD). Here, we investigated changes in FC and their relationship to learning and memory in a mouse model of AD across disease progression.

Methods

We assessed the APP/PS1 mouse model of AD and wild-type controls at 3-, 6-, and 10-months of age. Using resting state functional magnetic resonance imaging (rs-fMRI) in awake, unanesthetized mice, we assessed FC between 30 brain regions. ML models were then used to define interactions between neuroimaging readouts with learning and memory performance.

Results

In the APP/PS1 mice, we identified a pattern of hyperconnectivity across all three time points, with 47 hyperconnected regions at 3 months, 46 at 6 months, and 84 at 10 months. Notably, FC changes were also observed in the Default Mode Network, exhibiting a loss of hyperconnectivity over time. Modeling revealed functional connections that support learning and memory performance differ between the 6- and 10-month groups.

Discussion

These ML models show potential for early disease detection by identifying connectivity patterns associated with cognitive decline. Additionally, ML may provide a means to begin to understand how FC translates into learning and memory performance.

The association of internet use and cognition among older adults: mediating roles of social networks and depressive symptoms

Background

To explore the chain-mediated roles of social networks and depressive symptoms in the relationship between internet use and cognition among older adults.

Methods

Data were sourced from the 2018 China Longitudinal Aging Social Survey (CLASS). Structural equation modeling (SEM) was employed to analyze the potential mediating roles of social networks and depressive symptoms. Internet use, Lubben Social Network Scale (LSNS), the Center for Epidemiological Studies Depression Scale (CES-D) and Cognitive Scale were selected as indicators. Additionally, propensity score matching (PSM) was applied to further validate the robustness of the results by reducing potential selection bias and ensuring a more comparable distribution of observed covariates between internet users and non-users.

Results

Our analysis found that the use of the internet by older adults was positively correlated with their social networks (β=0.090, p<0.01). Both internet use and social networks exhibited negative associations with depressive symptoms in older adults respectively (β=-0.244, p<0.01; β=-0.136, p<0.01). Furthermore, both internet use (β=0.177, p<0.01) and social networks (β=0.032, p<0.01) positively predicted cognition in older adults, while depressive symptoms (β=-0.090, p<0.01) negatively impacted cognition. Moreover, our study showed that internet use not only directly influenced cognition of older adults but also indirectly impacted it through social networks and depressive symptoms, yielding a total mediated effect value of 0.026. Social networks and depressive symptoms accounted for 1.48% and 10.84% of the total effect respectively. Social networks and depressive symptoms served as chain mediators between internet use and cognition, constituting 0.49% of the total effect.

Conclusion

This chain-mediated model provided a clear depiction of how social networks and depressive symptoms mediate the impact of internet use on the cognition of older adults. Enhancements in internet literacy and optimization of age-appropriate product designs are recommended to improve cognitive functions in older populations.

Enhanced individual difference of functional brain network induced by volitional eyes closing

Volitional eyes closing (EC) can promote the interoceptive thoughts that vary considerably among individuals. Although this behavior is known to recruit a large-scale brain network as its neural underpinning, individual variability in such network recruitment remains unexplored. Here, we compared the intersubject variability in functional connectivity (IVFC) between the EC and eyes opening conditions. It was found that the IVFC significantly increased during the EC condition, which could be replicated in different public datasets and multi-resolution parcellations. Moreover, the EC-enhanced IVFC focused on selective subsets of FCs, with predominant impact on the default-mode, dorsal attention, and visual networks. Finally, a positive relationship was discovered between EC-enhanced IVFC and deep involvement in the task-unrelated thoughts that was measured with an additional dataset. Collectively, these results suggested that enhanced individual difference characterize the functional network of volitional EC, with widespread impact on cognitive systems and potential connection to task-unrelated thoughts.

Resting-state EEG network variability predicts individual working memory behavior

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

The Journey of the Default Mode Network: Development, Function, and Impact on Mental Health

The Default Mode Network has been extensively studied in recent decades due to its central role in higher cognitive processes and its relevance for understanding mental disorders. This neural network, characterized by synchronized and coherent activity at rest, is intrinsically linked to self-reflection, mental exploration, social interaction, and emotional processing. Our understanding of the DMN extends beyond humans to non-human animals, where it has been observed in various species, highlighting its evolutionary basis and adaptive significance throughout phylogenetic history. Additionally, the DMN plays a crucial role in brain development during childhood and adolescence, influencing fundamental cognitive and emotional processes. This literature review aims to provide a comprehensive overview of the DMN, addressing its structural, functional, and evolutionary aspects, as well as its impact from infancy to adulthood. By gaining a deeper understanding of the organization and function of the DMN, we can advance our knowledge of the neural mechanisms that underlie cognition, behavior, and mental health. This, in turn, can lead to more effective therapeutic strategies for a range of neuropsychiatric conditions.

Altered functional connectivity within and between resting-state networks in ulcerative colitis

Patients with ulcerative colitis (UC) often exhibit affective disorders, such as depression and anxiety. The underlying neurological mechanisms of these symptoms, however, remain poorly understood. This study aimed to explore alterations in functional connectivity (FC) both within and between resting-state networks (RSNs) in individuals with ulcerative colitis. Twelve meaningful RSNs were identified from 22 ulcerative colitis patients and 23 healthy controls using independent component analysis of functional magnetic resonance imaging data. Correlation analyses were performed between clinical indices, neuropsychological assessments and neuroimaging data. Compared with healthy controls, UC patients showed increased intranetwork FC, mainly located in the right temporal pole, orbitofrontal cortex, and left superior temporal and Rolandic opercular cortices within the auditory network. Increased intranetwork FC in the Rolandic opercular cortex was also observed in UC patients during remission phase, while no significant alterations were detected in patients with active-phase UC. In addition, UC patients exhibited increased connectivity between the dorsal attention and the left frontoparietal network, as well as between the anterior default mode network and the posterior default mode network, with distinct patterns of internetwork connectivity observed across different clinical phases. No significant correlations were found between altered brain regions and psychological scales in UC patients. These findings imply that UC patients may undergo functional network alterations, affecting both intranetwork connectivity within RSNs and internetwork connectivity between RSNs.

Dev-Atlas: A reference atlas of functional brain networks for typically developing adolescents

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

Video communication mitigate feelings of friendliness: A functional near-infrared spectroscopy study

Friendliness is essential for the development of stable interpersonal relationships. Video communication is growing rapidly in popularity for initiating and maintaining personal relationships, but it is unknown if video communication is as effective as face-to-face communication for promoting friendliness. This study compared the development of subjective friendliness between video and face-to-face communication sessions and investigated the associated neural correlates. Thirty healthy same-sex pairs were recruited and randomly assigned to face-to-face (Face) and video communication (Video) groups. Both groups performed three communication conditions face-to-face or via the Zoom platform in separate rooms: singing a song alone, singing a song with the other pair, and listening to the other pair sing a song. Activities of the bilateral dorsolateral prefrontal cortex (DLPFC) and medial prefrontal cortex (MPFC) were measured during the condition by functional near-infrared spectroscopy (fNIRS), and subjective friendliness was assessed before and after each condition using the Profile of Mood States 2nd Edition (POMS-F). After each condition, the change in POMS-F score (∆POMS-F) was compared between groups, and correlations were calculated with regional brain activity measures. Face group showed an increase in all conditions in ∆POMS-F, and Video group showed in average a decrease, which results in significant group difference in ∆POMS-F. The Face group showed a significant positive correlation between right DLPFC activity and increase in ∆POMS-F, whereas the Video group showed marginal significant negative correlation. Additionally, left DLPFC activity was significantly higher and MPFC activity was significantly lower in the Video group than in the Face group during all conditions. These findings suggest that subjective feelings of friendliness may be limited during video communication due to a requirement for greater cognitive effort.

Differential effects of 2 and 4 weeks repetitive transcranial magnetic stimulation inducing neuroplasticity on cognitive improvement

BackgroundRepetitive transcranial magnetic stimulation (rTMS) is an efficient intervention for alleviating cognitive symptoms in Alzheimer's disease (AD), but the optimal treatment duration for high efficacy remains unclear.ObjectiveThis study investigates the effects of 2-week and 4-week rTMS on neural network plasticity and cognitive improvement, aiming to identify the optimal treatment duration for cognitive impairment.MethodsrTMS was administered to cognitively impaired patients over 2-week and 4-week periods, exploring its effects on cognitive improvement and induced neural circuits. The study also examines the predictive value of these neural circuits for individual treatment responses.ResultsThe 4-week rTMS treatment significantly outperformed the 2-week course in improving cognitive function. Neural activity analysis identified the precuneus as a key region for episodic memory. Changes in brain regions, particularly within the default mode network (DMN), visual network (VN), and motor network (MN), were associated with cognitive improvements. Baseline functional connectivity in these regions predicted changes in general cognition (r = 0.724, p < 0.001) and episodic memory (r = 0.447, p = 0.022) after rTMS.ConclusionsExtended rTMS treatment enhances cognitive performance in cognitive impairment patients, with the 4-week course showing superior effects. Reduced connectivity in the DMN following rTMS was linked to cognitive improvements. The neural network baseline can predict patients' treatment responses.

Enhancing creativity with covert neurofeedback: causal evidence for default-executive network coupling in creative thinking

Creativity neuroscience has consistently reported increased functional connectivity between the default mode network and the executive control network supports creative cognition, potentially reflecting coordination of generative and evaluative cognitive processes. However, evidence has been purely correlational-no causal demonstrations show that default mode network-executive control network interaction specifically drives creative performance. We sought causal evidence for default mode network-executive control network coupling in creative thinking using functional near-infrared spectroscopy-brain connectivity neurofeedback, which can endogenously modify functional connectivity through reinforcement learning. Importantly, we employed covert neurofeedback, where participants were unaware of the specific brain activity being trained, allowing for unbiased evaluation of cognitive and neural impacts. In a default-executive neurofeedback condition (n = 15), we entrained coupling between the medial prefrontal cortex and the dorsolateral prefrontal cortex, hubs of the default mode network and executive control network, respectively. We compared this with a default-motor condition (n = 15), entraining coupling between the medial prefrontal cortex and the supplementary motor area. Approximately 24 h later, default-executive neurofeedback led to increased coupling between the default mode network and the executive control network during a creative thinking task (generating creative object uses), extending to broader default mode network regions. Behaviorally, we observed a double dissociation: The default-executive condition increased idea originality, while the default-motor condition improved go/no-go reaction times. We thus provide the first evidence that default mode network-executive control network coupling causally enhances creative performance.

Intrinsic brain mapping of cognitive abilities: A multiple-dataset study on intelligence and its components

This study investigates how functional brain network features contribute to general intelligence and its cognitive components by analyzing three independent cohorts of healthy participants. Cognitive scores were derived from 1) the Wechsler Adult Intelligence Scale (WAIS-IV), 2) the Raven Standard Progressive Matrices (RPM), and 3) the NIH and Penn cognitive batteries from the Human Connectome Project. Factor analysis on the NIH and Penn cognitive batteries yielded latent variables that closely resembled the content of the WAIS-IV indices and RPM. We employed graph theory and a multi-resolution network analysis by varying the modularity parameter (γ) to investigate hierarchical brain-behavior relationships across different scales of brain organization. Brain-behavior associations were quantified using multi-level robust regression analyses to accommodate variability and confounds at the subject-level, node-level, and resolution-level. Our findings reveal consistent brain-behavior relationships across the datasets. Nodal efficiency in fronto-parietal sensorimotor regions consistently played a pivotal role in fluid reasoning, whereas efficiency in visual networks was linked to executive functions and memory. A broad, low-resolution 'task-positive' network emerged as predictive of full-scale IQ scores, indicating a hierarchical brain-behavior coding. Conversely, increased cross-network connections involving default mode and subcortical-limbic networks were associated with reductions in both general and specific cognitive performance. These outcomes highlight the relevance of network efficiency and integration, as well as of the hierarchical organization in supporting specific aspects of intelligence, while recognizing the inherent complexity of these relationships. Our multi-resolution network approach offers new insights into the interplay between multilayer network properties and the structure of cognitive abilities, advancing the understanding of the neural substrates of the intelligence construct.

Time-dependent consolidation mechanisms of durable memory in spaced learning

Emerging studies suggest that time-dependent consolidation enables memory stabilization by promoting memory integration and hippocampal-cortical transfer. Compared to massed learning, how time-dependent consolidation contributes to forming durable memory and what neural signatures predict durable memory in spaced learning remain unclear. We recruited 48 participants who underwent either 3-day spaced learning or 1-day massed learning, and both resting-state and task-based fMRI data were collected in multiple delayed tests (i.e., immediate, 1-week, and 1-month). We use representational similarity analysis to assess neural integration and replay in the hippocampus and default mode network (DMN) subsystems. In contrast with massed learning, spaced learning induces higher neural pattern similarity during immediate retrieval only in DMN subsystems. Particularly, the neural pattern similarity in the dorsal-medial DMN (DMNdm) and medial-temporal DMN subsystems predicts the durable memory defined by 1-month delay. Moreover, we find increased neural replay of durable memory in the DMNdm for spaced learning and in the hippocampus for both spaced and massed learning. Our findings suggest that time-dependent consolidation promotes neural integration and replay in the cortex rather than in the hippocampus, which may underlie the formation of durable memory after spaced learning.

Worry and rumination elicit similar neural representations: neuroimaging evidence for repetitive negative thinking

Repetitive negative thinking (RNT) captures shared cognitive and emotional features of content-specific cognition, including future-focused worry and past-focused rumination. The degree to which these distinct but related processes recruit overlapping neural structures is undetermined, because most neuroscientific studies only examine worry or rumination in isolation. To address this, we developed a paradigm to elicit idiographic worries and ruminations during an fMRI scan in 39 young adults with a range of trait RNT scores. We measured concurrent emotion ratings and heart rate as a physiological metric of arousal. Multivariate representational similarity analysis revealed that regions distributed across default mode, salience, and frontoparietal control networks encode worry and rumination similarly. Moreover, heart rate did not differ between worry and rumination. Capturing the shared neural features between worry and rumination throughout networks supporting self-referential processing, memory, salience detection, and cognitive control provides novel empirical evidence to bolster cognitive and clinical models of RNT.

Multivariate Neural Markers of Individual Differences in Thought Control Difficulties

Difficulties in controlling thought, including pathological rumination, worry, and intrusive thoughts, occur in a range of mental health disorders. Here we identify specific patterns of brain activity distributed within and across canonical brain networks that are associated with self-reported difficulties in controlling one's thoughts. These activity patterns were derived using multivariate pattern analysis on fMRI data recorded while participants engaged in one of four operations on an item in working memory: maintaining it, replacing it with another, specifically suppressing it, or clearing the mind of all thought. Individuals who reported greater difficulties exhibited brain activation patterns that were more variable and less differentiated across the four operations in frontoparietal and default mode networks, and showed less distinct patterns of connectivity within the default mode network. These activity profiles were absent during rest but serve as promising task-based neural markers, explaining over 30% of the variance in thought control difficulties.

Disease-specific alterations of effective connectivity across anti-correlated networks in major depressive disorder and bipolar disorder

Major depressive disorder (MDD) and bipolar disorder (BD) share various clinical behaviors and have confounded clinical diagnoses. Converging studies have suggested MDD and BD as disorders with abnormal communication among functional brain networks involved in mental activity and redirection. However, whether MDD and BD show disease-specific alterations in network information interaction remains unclear. This study collected resting-state functional MRI data of 98 patients with MDD, 55 patients with BD, and sex-, age-, and education-matched 95 healthy controls. Spectral dynamic causal model (spDCM) was used to investigate effective connectivities among three large-scale intrinsic functional networks including the default mode network (DMN), salience network (SN), and dorsal attention network (DAN). Effective connectivities showing disease-specific changes were then used as input features of support vector models to predict clinical symptoms and classify individuals with MDD and BD. Compared with healthy controls, both the MDD and BD groups showed increased DAN → SN connectivity. However, within-network connectivities of DMN and DAN showed opposite effects on the diseases. Notably, MDD and BD also showed different alterations on a connectivity loop of SN → DAN → DMN → SN, which could be used to predict the clinical symptom severity of either MDD or BD. Individuals with MDD and BD could be further classified by using connectivities showing opposite disease effects. Our findings reveal common and unique alterations of network interactions in MDD and BD, and further suggest disease-specific neuroimaging markers for clinical diagnosis.

Whole-brain causal discovery using fMRI

Despite significant research, discovering causal relationships from fMRI remains a challenge. Popular methods such as Granger causality and dynamic causal modeling fall short in handling contemporaneous effects and latent common causes. Methods from causal structure learning literature can address these limitations but often scale poorly with network size and need acyclicity. In this study, we first provide a taxonomy of existing methods and compare their accuracy and efficiency on simulated fMRI from simple topologies. This analysis demonstrates a pressing need for more accurate and scalable methods, motivating the design of Causal discovery for Large-scale Low-resolution Time-series with Feedback (CaLLTiF). CaLLTiF is a constraint-based method that uses conditional independence between contemporaneous and lagged variables to extract causal relationships. On simulated fMRI from the macaque connectome, CaLLTiF achieves significantly higher accuracy and scalability than all tested alternatives. From resting-state human fMRI, CaLLTiF learns causal connectomes that are highly consistent across individuals, show clear top-down flow of causal effect from attention and default mode to sensorimotor networks, exhibit Euclidean distance dependence in causal interactions, and are highly dominated by contemporaneous effects. Overall, this work takes a major step in enhancing causal discovery from whole-brain fMRI and defines a new standard for future investigations.

Short-term BCI intervention enhances functional brain connectivity associated with motor performance in chronic stroke

BACKGROUND

Evidence suggests that brain-computer interface (BCI)-based rehabilitation strategies show promise in overcoming the limited recovery potential in the chronic phase of stroke. However, the specific mechanisms driving motor function improvements are not fully understood.

OBJECTIVE

We aimed at elucidating the potential functional brain connectivity changes induced by BCI training in participants with chronic stroke.

METHODS

A longitudinal crossover design was employed with two groups of participants over the span of 4 weeks to allow for within-subject (n = 21) and cross-group comparisons. Group 1 (n = 11) underwent a 6-day motor imagery-based BCI training during the second week, whereas Group 2 (n = 10) received the same training during the third week. Before and after each week, both groups underwent resting state functional MRI scans (4 for Group 1 and 5 for Group 2) to establish a baseline and monitor the effects of BCI training.

RESULTS

Following BCI training, an increased functional connectivity was observed between the medial prefrontal cortex of the default mode network (DMN) and motor-related areas, including the premotor cortex, superior parietal cortex, SMA, and precuneus. Moreover, these changes were correlated with the increased motor function as confirmed with upper-extremity Fugl-Meyer assessment scores, measured before and after the training.

CONCLUSIONS

Our findings suggest that BCI training can enhance brain connectivity, underlying the observed improvements in motor function. They provide a basis for developing novel rehabilitation approaches using non-invasive brain stimulation for targeting functionally relevant brain regions, thereby augmenting BCI-induced neuroplasticity and enhancing motor recovery.

Graded changes in local functional connectivity of the cerebral cortex in young people with depression

BACKGROUND

Major depressive disorder (MDD) is marked by significant changes to the local synchrony of spontaneous neural activity across various brain regions. However, many methods for assessing this local connectivity use fixed or arbitrary neighborhood sizes, resulting in a decreased capacity to capture smooth changes to the spatial gradient of local correlations. A newly developed method sensitive to classical anatomo-functional boundaries, Iso-Distant Average Correlation (IDAC), was therefore used to examine depression associated alterations to the local functional connectivity of the brain.

METHOD

One-hundred and forty-seven adolescents and young adults with MDD and 94 healthy controls underwent a resting-state functional magnetic resonance imaging (fMRI) scan. Whole-brain functional connectivity maps of intracortical neural activity within iso-distant local areas (5-10, 15-20, and 25-30 mm) were generated to characterize local fMRI signal similarities.

RESULTS

Across all spatial distances, MDD participants demonstrated greater local functional connectivity of the bilateral posterior hippocampus, retrosplenial cortex, dorsal insula, fusiform gyrus, and supplementary motor area. Local connectivity alterations in short and medium distances (5-10 and 15-20 mm) in the mid insula cortex were additionally associated with expressive suppression use, independent of depressive symptom severity.

CONCLUSIONS

Our study identified increased synchrony of the neural activity in several regions commonly implicated in the neurobiology of depression. These effects were relatively consistent across the three distances examined. Longitudinal investigation of this altered local connectivity will clarify whether these differences are also found in other age groups and if this relationship is modified by increased disease chronicity.

Relating scene memory and perception activity to functional properties, networks, and landmarks of posterior cerebral cortex - a probabilistic atlas

Adaptive behavior in complex environments requires integrating visual perception with memory of our spatial environment. Recent work has implicated three brain areas in posterior cerebral cortex - the place memory areas (PMAs) that are anterior to the three visual scene perception areas (SPAs) - in this function. However, PMAs' relationship to the broader cortical hierarchy remains unclear due to limited group-level characterization. Here, we examined the PMA and SPA locations across three fMRI datasets (44 participants, 29 female). SPAs were identified using a standard visual localizer where participants viewed scenes versus faces. PMAs were identified by contrasting activity when participants recalled personally familiar places versus familiar faces (Datasets 1-2) or places versus multiple categories (familiar faces, bodies, and objects, and famous faces; Dataset 3). Across datasets, the PMAs were located anterior to the SPAs on the ventral and lateral cortical surfaces. The anterior displacement between PMAs and SPAs was highly reproducible. Compared to public atlases, the PMAs fell at the boundary between externally-oriented networks (dorsal attention) and internally-oriented networks (default mode). Additionally, while SPAs overlapped with retinotopic maps, the PMAs were consistently located anterior to mapped visual cortex. These results establish the anatomical position of the PMAs at inflection points along the cortical hierarchy between unimodal sensory and transmodal, apical regions, which informs broader theories of how the brain integrates perception and memory for scenes. We have released probabilistic parcels of these regions to facilitate future research into their roles in spatial cognition.

Papillary Craniopharyngioma: An Integrative and Comprehensive Review

Papillary craniopharyngioma (PCP) is a rare type of tumor, comprising ∼20% of all craniopharyngioma (CP) cases. It is now recognized as a separate pathological entity from the adamantinomatous type. PCPs are benign tumors, classified as World Health Organization grade 1, characterized by nonkeratinizing squamous epithelium. They typically grow as solid and round papillomatous masses or as unilocular cysts with a cauliflower-like excrescence. PCPs primarily occur in adults (95%), with increased frequency in males (60%), and predominantly affect the hypothalamus. Over 80% of these tumors are located in the third ventricle, expanding either above an anatomically intact infundibulum (strictly third ventricle tumors) or within the infundibulo-tuberal region of the third ventricle floor. Clinical manifestations commonly include visual deficits and a wide range of psychiatric disturbances (45% of patients), such as memory deficits and odd behavior. Magnetic resonance imaging can identify up to 50% of PCPs by the presence of a basal duct-like recess. Surgical management is challenging, requiring complex approaches to the third ventricle and posing significant risk of hypothalamic injury. The endoscopic endonasal approach allows radical tumor resection and yields more favorable patient outcomes. Of intriguing pathogenesis, over 90% of PCPs harbor the somatic BRAFV600E mutation, which activates the mitogen-activated protein kinase signaling pathway. A phase 2 clinical trial has demonstrated that PCPs respond well to proto-oncogene B-Raf/MAPK/ERK kinase inhibitors. This comprehensive review synthesizes information from a cohort of 560 well-described PCPs and 99 large CP series including PCP cases published from 1856 to 2023 and represents the most extensive collection of knowledge on PCPs to date.

Brain Health in Sleep Disorders

Sleep is a critical determinant of brain health, influencing cognitive, emotional, and physiologic functions. The complex bidirectional relationship between sleep and brain health underscores the importance of sleep in maintaining cognitive function, regulating brain homeostasis, and facilitating the clearance of metabolic waste through the glymphatic system. Chronic sleep deprivation and sleep disorders such as insomnia and obstructive sleep apnea have been shown to negatively impact brain structures and functions. This review discusses the impact of sleep disorders on brain health. It also explores the implications of impaired sleep on cardiovascular health, immune function, and neuroplasticity.

Thoughts and thinkers: On the complementarity between objects and processes

We argue that "processes versus objects" is not a useful dichotomy. There is, instead, substantial theoretical utility in viewing "objects" and "processes" as complementary ways of describing persistence through time, and hence the possibility of observation and manipulation. This way of thinking highlights the role of memory as an essential resource for observation, and makes it clear that "memory" and "time" are also mutually inter-defined, complementary concepts. We formulate our approach in terms of the Free Energy Principle (FEP) of Friston and colleagues and the fundamental idea from quantum theory that physical interactions can be represented by linear operators. Following Levin (2024) [30], we emphasize that memory is, first and foremost, an interpretative function, from which the idea of memory as a record, at some level of accuracy, of past events is derivative. We conclude that the distinction between objects and processes is always contrived, and always misleading, and that science would be better served by abandoning it entirely.

Altered functional connectivity and hyperactivity of the caudal hippocampus in schizophrenia compared with bipolar disorder: a resting state fMRI (functional magnetic resonance imaging) study

BACKGROUND

Schizophrenia patients frequently present with structural and functional abnormalities of the hippocampus (Hipp). Further, these abnormalities are often associated with specific symptom profiles.

AIM

To determine whether schizophrenia patients show specific functional connectivity (FC) and activity abnormalities in each hippocampal subregion compared to the BD (bipolar disorder) and HC (healthy control) groups.

METHODS

Basal activation state and functional connectivity (FC) in four subregions of the bilateral Hipp were examined: left caudal (cHipp_L), right caudal (cHipp_R), left rostral (rHipp_L), and right rostral (rHipp_R). Resting-state functional magnetic resonance images were obtained from 62 schizophrenia patients, 57 bipolar disorder (BD) patients, and 45 healthy controls (HCs), and analyzed for fractional amplitude of low-frequency fluctuations (fALFF) as a measure of basal neural activity and for whole-brain FC based on the hippocampal subregions.

RESULTS

The schizophrenia group exhibited greater fALFF in bilateral cHipp (the caudal part of hippocampus) and rHipp (the rostral part of hippocampus) subregions compared to BD and HC groups as well as increased FC between the bilateral cHipp and multiple brain regions, including the thalamus, putamen, middle frontal gyrus, parietal cortex, and precuneus. Moreover, fALFF values of the bilateral cHipp were positively correlated with the severity of clinical symptoms as measured by the Positive and Negative Syndrome Scale.

CONCLUSIONS

These findings confirm an important contribution of hippocampal dysfunction, especially of the cHipp, in schizophrenia. Further, hyper-connectivity and hyperactivity of the cHipp could serve as a biomarker for therapeutic development.

Acute psilocybin and ketanserin effects on cerebral blood flow: 5-HT2AR neuromodulation in healthy humans

Psilocin, the active metabolite of psilocybin, is a psychedelic and agonist at the serotonin 2A receptor (5-HT2AR) that has shown positive therapeutic effects for brain disorders such as depression. To elucidate the brain effects of psilocybin, we directly compared the acute effects of 5-HT2AR agonist (psilocybin) and antagonist (ketanserin) on cerebral blood flow (CBF) using pseudo-continuous arterial spin labeling magnetic resonance imaging (MRI) in a single-blind, cross-over study in 28 healthy participants. We evaluated associations between plasma psilocin level (PPL) or subjective drug intensity (SDI) and CBF. We also evaluated drug effects on internal carotid artery (ICA) diameter using time-of-flight MRI angiography. PPL and SDI were significantly negatively associated with regional and global CBF (∼11.6% at peak drug effect, p < 0.0001). CBF did not significantly change following ketanserin (2.3%, p = 0.35). Psilocybin induced a significantly greater decrease in CBF compared to ketanserin in the parietal cortex (pFWER < 0.0001). ICA diameter was significantly decreased following psilocybin (10.5%, p < 0.0001) but not ketanserin (-0.02%, p = 0.99). Our data support an asymmetric 5-HT2AR modulatory effect on CBF and provide the first in vivo human evidence that psilocybin constricts the ICA, which has important implications for understanding the neurophysiological mechanisms underlying its acute effects.

Dance and the Embodied Social Cognition of Mating: Carlos Saura's Tango in the Perspective of the Tie-Up Theory

This paper analyzes Carlos Saura's film Tango through the theoretical lens of the Tie-Up Theory to explore how fictional narratives can serve as laboratories for investigating the embodied social cognition of romantic relationships. The study shows how dance, particularly tango, functions both as subject matter and cognitive metaphor in representing the complex dynamics of couple formation and maintenance. The film's meta-representational structure, combining the creation of a dance performance with the exploration of actual relationships, reveals how cultural forms serve as cognitive scaffolds for understanding complex social dynamics. The study contributes to our understanding of how artistic representation can reveal typically implicit aspects of relationship cognition by demonstrating the value of integrating multidisciplinary perspectives of cognitive theory, psychology of mating, and cultural theory.

Gait impairment associated with neuroimaging biomarkers in Alzheimer's disease

Research links gait impairment in Alzheimer's disease (AD) to cognitive abnormalities, brain atrophy, or amyloid-β (Aβ) deposition, with the exact cause unclear. This study investigated the relationship between gait, neuroimaging biomarkers, and cognition across the AD spectrum. We recruited 48 AD dementia patients, 27 with prodromal AD, and 41 cognitively unimpaired individuals, analyzing associations among gait parameters, cognitive scores, Aβ deposition, and cortical atrophy. Path and receiver operating characteristic (ROC) analyses evaluated gait impairment's interdependent interactions and diagnostic potential. Prodromal AD and AD dementia patients showed significantly slower gait pace than CU (p = 0.014 [velocity], p = 0.003 [step length]), linked to attention and executive functions, widespread Aβ deposition, and cortical atrophy, in the inferior parietal lobule, middle temporal gyrus, precuneus, and insula. Compared to CU, AD dementia patients exhibited greater gait variability and phase (p = 0.017 [step length standard deviation], p = 0.001 [double support percentage]), significantly correlated with cognition and Aβ deposition. Path analysis revealed a combined influence of Aβ deposition, cognitive impairment, and cortical atrophy on gait impairment, with > 80% observed gait impairments directly affected by Aβ deposition. ROC curves for diagnosing AD stages showed significant areas under the curve, suggesting gait characteristics as noninvasive biomarkers for early AD diagnosis and progression monitoring.

Multivariate Neural Markers of Individual Differences in Thought Control Difficulties

Difficulties in controlling thought, including pathological rumination, worry, and intrusive thoughts, occur in a range of mental health disorders. Here we identify specific patterns of brain activity distributed within and across canonical brain networks that are associated with self-reported difficulties in controlling one's thoughts. These activity patterns were derived using multivariate pattern analysis on fMRI data recorded while participants engaged in one of four operations on an item in working memory: maintaining it, replacing it with another, specifically suppressing it, or clearing the mind of all thought. Individuals who reported greater difficulties exhibited brain activation patterns that were more variable and less differentiated across the four operations in frontoparietal and default mode networks, and showed less distinct patterns of connectivity within the default mode network. These activity profiles were absent during rest but serve as promising task-based neural markers, explaining over 30% of the variance in thought control difficulties.

Differential links in 16p11.2 deletion carriers reveal aberrant connections between large-scale networks

Qualitatively different topographical patterns of connections are thought to underlie individual differences in thought and behavior, particularly at heteromodal association areas. As such, we hypothesized that connections unique to 16p11.2 deletion carriers compared to controls, rather than hyper- or hypo-connectivity, would serve as a better model to explain the cognitive and behavioral changes observed in individuals carrying this autism-risk copy number variation. Using a spatially-unbiased, data-driven approach we found that differential links clustered non-uniformly across the cortex-particularly at the superior temporal gyrus and sulcus, posterior insula, cingulate sulcus, and inferior parietal lobule bilaterally. At these hotspots, altered local connectivity that spanned across the borders of cortical large-scale networks coincided with aberrant distant interconnectivity between large-scale networks. This was most evident between the auditory and the dorsomedial default (DNb) networks-such that greater between-network interconnectivity was associated with greater communication and social impairment. Entangled connectivity between large-scale networks may preclude each network from having the necessary fidelity to operate properly, particularly when the 2 networks have opposing organization principles-namely, local specialization (segregation) versus global coherency (integration).