Recall is an act of elicitation of emotions similar to those emotions previously experienced. Unlike the past experiences where external sensory stimuli triggered emotions, recall does not require external sensory stimuli. This difference is pertinent to the key debate in affective representation, addressing whether the representation of valence is consistent across modalities (modality-general) or dependent on modalities (modality-specific). This study aimed to verify neural representations of valence between encoding and recall. Using neuroimaging data from movie watching and recall (Chen et al., 2017) and behavioral data for valence ratings (Kim et al., 2020), a searchlight analysis was conducted with cross-participant regression-based decoding between movie watching and recall. Multidimensional scaling was employed as a validation analysis of the results from searchlight analysis. The searchlight analysis revealed the right middle temporal and inferior temporal gyrus as well as the left fusiform gyrus. The validation analysis further exhibited significant consistent neural representations of valence in the inferior temporal gyrus and the left fusiform gyrus. This study identified the brain regions where valence is consistently represented between encoding and recall about real events. These findings contribute to debate in affective representations, by comparing conditions utilized little in prior, suggesting the inferior temporal gyrus relates to representations of valence during encoding and recalling natural events.

Background/Objectives: Schizophrenia (SZ) is a complex psychiatric disorder characterized by neurodegenerative processes, but the structural brain alterations associated with its progression remain poorly understood. This study investigated structural brain changes in SZ, particularly in the fronto-temporal and limbic regions, and explored their relationship with symptom severity, with a focus on mood- and emotion-related symptoms. Methods: We analyzed structural MRI data from 74 SZ patients and 91 healthy controls (HCs) using voxel-based morphometry (VBM) to compare whole-brain grey matter volumes (GMVs). The analysis focused on the fronto-temporal and limbic regions, and correlations between GMV and symptom severity were assessed using the Positive and Negative Syndrome Scale (PANSS) and the Generalized Psychopathology (GP) scale. Results: SZ patients exhibited significant reductions in GMV in the fronto-temporal and limbic regions, including the dorsolateral prefrontal cortex (dlPFC) and the temporal pole, compared to HCs. Notably, a significant positive association was found between GMV in the right inferior temporal gyrus (ITG) and the severity of generalized psychopathology, as well as with anxiety, depression, mannerisms, and unusual thought content. Further post hoc analysis identified a specific cluster of mood-related symptoms contributing to the GP scale, which correlated with GMV changes in the right ITG. Conclusions: Our findings provide new evidence of structural brain alterations in SZ, particularly in the fronto-temporal and limbic regions, suggesting a progressive neurodegenerative pattern. The role of the right ITG in mood- and emotion-related symptoms requires further exploration, as it could offer insights into SZ pathophysiology and aid in distinguishing SZ from other mood-related disorders.

Cerebral structural changes in both cortical and subcortical regions were detected in the pathology of glutaric aciduria type 1 (GA-1) patients. Conventional magnetic resonance imaging was limited by radiologist-inter variability in evaluating its severity. This cross-sectional study aimed to identify the affected brain structures and their functional correlations within the cortical and subcortical regions of patients with GA-1.

Seventeen patients with GA-1 and 17 healthy controls (HCs) were included (mean age, 38 ± 17 months; both groups contained 6 males). Three-dimensional T1-weighted imaging data were acquired, and voxel and surface-based morphometry were used to quantitatively investigate differences in gray matter volume (GMV) and cortical thickness (CT). Two-sample t-tests were performed.

Patients with GA-1 had lower GMV in the bilateral basal ganglia, thalamus, limbic system, default mode network, and right cerebellum, as well as lower CT in the bilateral insula, lateral occipital cortex, right inferior parietal cortex, inferior temporal gyrus, and posterior cingulate cortex than HCs. Patients with GA-1 had higher CT in the bilateral lingual gyrus, parahippocampal gyrus, superior frontal gyrus, left postcentral gyrus, right precuneus, precentral gyrus, middle temporal gyrus, and inferior temporal gyrus than HCs. In patients with GA-1, urinary glutaryl-carnitine levels were significantly negatively correlated with the GMV in the left inferior temporal gyrus.

Our brain morphological analyses revealed quantitative differences in the GMV and CT of GA-1 patients compared to HCs and provided useful information about normal and abnormal neuroanatomy.

The pathophysiological mechanism of migraine is still not clear. Thus, this study aimed to evaluate the changes in effective connectivity (EC) in the brain functional network underlying migraine and its association with clinical measures of migraine.

Fifty patients with episodic migraine without aura (MwoA) and 48 healthy controls (HCs) were enrolled in this study. Spontaneous activity in the brain was evaluated using the degree centrality (DC) method, and the brain regions with obvious signal differences between the two groups were taken as seed points for whole brain Granger causality analysis (GCA) analysis. The values of the brain regions with differences in DC and GCA were extracted and correlated with clinical measures of migraine.

Compared to the HCs, the MwoA patients showed decreased DC in the left inferior temporal gyrus (ITG) and increased DC in the right precuneus and exhibited significantly decreased EC from the left ITG to the left inferior parietal gyrus and right inferior occipital gyrus (IOG) as well as significantly increased EC from the left postcentral gyrus and left cerebellum posterior lobe to the left ITG. Moreover, decreased EC from the left thalamus to the right precuneus was found in the MwoA patients compared to the HCs. The DC values in the right precuneus were significantly negatively correlated with the duration of headache. Additionally, we found a significantly positive correlation between the Migraine Disability Assessment questionnaire score and the EC from the left ITG to the right IOG, as well as between the intensity of headache and the EC from the left thalamus to the right precuneus.

This study found changes in the EC of the brain functional network underlying migraine and their associations with migraine-related parameters. These findings are helpful for understanding the pathophysiological mechanism in migraine patients.

To explore the neuroimaging characteristics of eczema-induced pruritus with resting-state functional magnetic resonance imaging (rs-fMRI).

A total of 42 patients with eczema were recruited in the PE group, and 42 healthy participants were included in the HC group. The Visual Analogue Score (VAS), 12-Item Pruritus Severity Scale (12-PSS), Pittsburgh Sleep Quality Index (PSQI), and Self-Rating Anxiety Scale (SAS) were recorded in the PE group. The different values of fraction amplitude of low-frequency fluctuation (fALFF) and functional connectivity (FC) were compared after rs-fMRI scanning.

Compared with the HC group, the fALFF values of the left precentral gyrus, left postcentral gyrus, left supplementary motor area (SMA), and left midcingulate cortex in the PE group were increased. The FC values between the left precentral gyrus, bilateral superior temporal gyrus, bilateral hippocampus, and left inferior occipital gyrus in the PE group were decreased. The FC values between left SMA and bilateral superior temporal gyrus in the PE group were decreased. The 12-PSS score was positively correlated with fALFF value of the left precentral gyrus and left postcentral gyrus.

Pruritus caused increased spontaneous activity in given cerebral regions, involving the perception of itch, control of scratching movements, and expression of itch-related emotions. Meanwhile, there is a correlation between fALFF values of given cerebral regions and clinical scales, which provided potential neurobiological markers for the future study of pruritus.

While decreasing negative attitudes against outgroups are often reported by individuals themselves, biased behaviour prevails. This gap between words and actions may stem from unobtrusive mental processes that could be uncovered by using neuroimaging in addition to self-reports. In this study we investigated whether adding neuroimaging to a traditional intergroup bias measure could detect intersubject differences in intergroup bias processes in a societal context where opposing discrimination is normative. In a sample of 43 Finnish students, implicit behavioural measures failed to indicate intergroup bias against Middle Eastern and Muslim immigrants, and explicit measures reported rather positive attitudes and sentiments towards that targeted group. Yet, while implementing a repeatedly validated method for detecting intergroup bias, an implicit association paradigm presenting stereotypical ingroup and outgroup face stimuli while undergoing magnetoencephalography, we detected a clear neural difference between two experimental conditions. The neural effect is thought to reflect intergroup bias in the valence of the associations that faces evoke. The activity cluster of the neural bias peaked in BA37 and included significant activity in the fusiform gyrus, which has been repeatedly found to be active during face perception bias. Importantly, this neural pattern was driven by participants who were explicitly favourable of immigration - but to a lesser extent than others. These findings suggest that such variations in explicit support of immigration are associated with the differential neural sensitivity to the congruency of associations between intergroup faces and valence. This research showcases the potential of neuroimaging to unravel covert perceptual bias against outgroup members and its sensitivity to small variations in explicit attitudes.

Bipolar disorder (BD) is a complex psychiatric condition marked by significant mood fluctuations that deeply affect quality of life. Understanding the neural mechanisms underlying BD is critical for improving diagnostic accuracy and developing more effective treatments. This study utilized resting-state functional magnetic resonance imaging (rs-fMRI) to investigate functional connectivity within the ventral and dorsal attention networks in 52 patients with BD and 51 healthy controls. Independent Component Analysis (ICA) was employed to establish network templates, while Network Homogeneity (NH) analysis facilitated the comparison of NH values across various brain regions. We examined the association of NH values with clinical measures, including the Hamilton Depression Scale, Perceptual Deficit Questionnaire, and Young Mania Scale. Results indicated that BD patients exhibited lower NH values in the right inferior temporal gyrus of the dorsal attention network and the right middle temporal gyrus of the ventral attention network compared to controls. Notably, NH values in the right superior marginal gyrus of the ventral network were higher in the BD group. Although no significant correlations were found between NH values and clinical symptoms, Support Vector Machine (SVM) analysis demonstrated over 60% accuracy in differentiating BD patients based on NH values. These findings highlight the potential of NH measures as biomarkers for BD, underscore the importance of advanced neuroimaging in uncovering the disorder's complex neural dynamics, and point to the challenges and need for further research to improve predictive accuracy.

Understanding brain function relies on identifying spatiotemporal patterns in brain activity. In recent years, machine learning methods have been widely used to detect connections between regions of interest (ROIs) involved in cognitive functions, as measured by the fMRI technique. However, it's essential to match the type of learning method to the problem type, and extracting the information about the most important ROI connections might be challenging. In this contribution, we used machine learning techniques to classify tasks in a working memory experiment and identify the brain areas involved in processing information. We employed classical discriminators and neural networks (convolutional and residual) to differentiate between brain responses to distinct types of visual stimuli (visuospatial and verbal) and different phases of the experiment (information encoding and retrieval). The best performance was achieved by the LGBM classifier with 1-time point input data during memory retrieval and a convolutional neural network during the encoding phase. Additionally, we developed an algorithm that took into account feature correlations to estimate the most important brain regions for the model's accuracy. Our findings suggest that from the perspective of considered models, brain signals related to the resting state have a similar degree of complexity to those related to the encoding phase, which does not improve the model's accuracy. However, during the retrieval phase, the signals were easily distinguished from the resting state, indicating their different structure. The study identified brain regions that are crucial for processing information in working memory, as well as the differences in the dynamics of encoding and retrieval processes. Furthermore, our findings indicate spatiotemporal distinctions related to these processes. The analysis confirmed the importance of the basal ganglia in processing information during the retrieval phase. The presented results reveal the benefits of applying machine learning algorithms to investigate working memory dynamics.

The literature suggests that alterations in functional connectivity (FC) of the Salience Network (SN) may contribute to the manifestation of some clinical features of Autism Spectrum Disorder (ASD). The SN plays a key role in integrating external sensory information with internal emotional and bodily information. An atypical FC of this network could explain some symptomatic features of ASD such as difficulties in self-awareness and emotion processing and provide new insights into the neurobiological basis of autism. Using the Autism Brain Imaging Data Exchange II we investigated the resting-state FC of core regions of SN and its association with autism symptomatology in 29 individuals with ASD compared with 29 typically developing (TD) individuals. In ASD compared to TD individuals, seed-based connectivity analysis showed a reduced FC between the rostral prefrontal cortex and left cerebellum and an increased FC between the right supramarginal gyrus and the regions of the middle temporal gyrus and angular gyrus. Finally, we found that the clinical features of ASD are mainly associated with an atypical FC of the anterior insula and the involvement of dysfunctional mechanisms for emotional and social information processing. These findings expand the knowledge about the differences in the FC of SN between ASD and TD, highlighting atypical FC between structures that play key roles in social cognition and complex cognitive processes. Such anomalies could explain difficulties in processing salient stimuli, especially those of a socio-affective nature, with an impact on emotional and behavioral regulation.

Anticipatory brain activity makes it possible to predict the occurrence of expected situations. However, events such as traffic accidents are statistically unpredictable and can generate catastrophic consequences. This study investigates the brain activity and effective connectivity associated with anticipating and processing such unexpected, unavoidable accidents. We asked 161 participants to ride a motorcycle simulator while recording their electroencephalographic activity. Of these, 90 participants experienced at least one accident while driving. We conducted both within-subjects and between-subjects comparisons. During the pre-accident period, the right inferior parietal lobe (IPL), left anterior cingulate cortex (ACC), and right insula showed higher activity in the accident condition. In the post-accident period, the bilateral orbitofrontal cortex, right IPL, bilateral ACC, and middle and superior frontal gyrus also showed increased activity in the accident condition. We observed greater effective connectivity within the nodes of the limbic network (LN) and between the nodes of the attentional networks in the pre-accident period. In the post-accident period, we also observed greater effective connectivity between networks, from the ventral attention network (VAN) to the somatomotor network and from nodes in the visual network, VAN, and default mode network to nodes in the frontoparietal network, LN, and attentional networks. This suggests that activating salience-related processes and emotional processing allows the anticipation of accidents. Once an accident has occurred, integration and valuation of the new information takes place, and control processes are initiated to adapt behavior to the new demands of the environment.

Previous studies suggested that structural and functional connectivity of right frontotemporal circuits associate with music perception. Emerging evidences demonstrated that structure-function coupling is important for cognition and may allow for a more sensitive investigation of brain-behavior association, while we know little about the relationship between structure-function coupling and music perception.

We collected multimodal neuroimaging data from 106 participants and measured their music perception by Montreal Battery of Evaluation of Amusia (MBEA). Then we computed structure-function coupling, amplitude of low-frequency fluctuation (ALFF), gray matter volume (GMV), and structural/functional degree centrality (DC) and utilized support vector regression algorithm to build their relationship with MBEA score.

We found structure-function coupling, rather than GMV, ALFF, or DC, contributed to predict MBEA score. Left middle frontal gyrus (L.MFG), bilateral inferior temporal gyrus, and right insula were the most predictive ROIs for MBEA score. Mediation analysis revealed structure-function coupling of L.MFG, a region that is homologous to typical music circuits, fully mediated the negative link between ALFF of L.MFG and MBEA score.

Structure-function coupling is more effective when explaining variation in music perception. Our findings provide further understanding for the neural basis of music and have implications for cognitive causes of amusia.

Very preterm infants are susceptible to neurodevelopmental impairments, necessitating early detection of prognostic biomarkers for timely intervention. The study aims to explore possible functional biomarkers for very preterm infants at born that relate to their future cognitive and motor development using resting-state fMRI. Prior studies are limited by the sample size and suffer from efficient functional connectome (FC) construction algorithms that can handle the noisy data contained in neonatal time series, leading to equivocal findings. Therefore, we first propose an enhanced functional connectome construction algorithm as a prerequisite step. We then apply the new FC construction algorithm to our large prospective very preterm cohort to explore multi-level neurodevelopmental biomarkers.

There exists an intrinsic relationship between the structural connectome (SC) and FC, with a notable coupling between the two. This observation implies a putative property of graph signal smoothness on the SC as well. Yet, this property has not been fully exploited for constructing intrinsic dFC. In this study, we proposed an advanced dynamic FC (dFC) learning model, dFC-Igloo, which leveraged SC information to iteratively refine dFC estimations by applying graph signal smoothness to both FC and SC. The model was evaluated on artificial small-world graphs and simulated graph signals.

The proposed model achieved the best and most robust recovery of the ground truth graph across different noise levels and simulated SC pairs from the simulation. The model was further applied to a cohort of very preterm infants from five Neonatal Intensive Care Units, where an enhanced dFC was obtained for each infant. Based on the improved dFC, we identified neurodevelopmental biomarkers for neonates across connectome-wide, regional, and subnetwork scales.

The identified markers correlate with cognitive and motor developmental outcomes, offering insights into early brain development and potential neurodevelopmental challenges.

The intersection of neuroscience and technology hinges on the development of wearable devices and electrodes that can augment brain networks to improve cognitive capabilities such as learning and concentration. The capacity to enhance networks associated with these functions above baseline capabilities, holds the potential to benefit numerous individuals. The purpose of this study was to determine if electromagnetic field exposure modeled from physiological data would increase instances of flow in participants playing a computer game. The flow state refers to a subjective state of optimal performance experienced by individuals during a variety of tasks. For this study, participants (n = 39, 18-65 years, nfemale = 20) played the arcade game Snake for two ten-minute periods (each with a ten-minute rest period immediately following). For one of the trials, an electromagnetic field was applied bilaterally to the temporal lobes, with the other serving as the control. Brain activity was measured using quantitative electroencephalography, flow experience was measured using the Flow Short Scale and game play scores were also recorded. Results showed deceased beta 1 (12-16 Hz) activity in the left cuneus [t = 4.650, p < 0.01] and left precuneus [t = 4.603, p < 0.01], left posterior cingulate [t = 4.521, p < 0.05], insula [t = 4.234, p < 0.05], and parahippocampal gyrus [t = 4.113, p < 0.05] for trials when the field was active, compared to controls during rest periods. Results from the Flow Short Scale showed a statistically significant difference in mean "concentration ease" scores across electromagnetic field conditions, irrespective of difficulty [t = 2.131, p < 0.05]. In the EMF exposure trials, there was no discernible experience effect; participants with prior experience in the game Snake did not exhibit significantly better performance compared to those without prior experience. This anticipated effect was observed in control conditions. The comparable performance observed between novices and experienced players in the EMF condition indicate a noteworthy learning curve for novices. In all, these results provide evidence supporting the ability of EMF patterned from amygdaloid firing (6-20 Hz) to elicit neurological correlates of flow in brain regions previously reported in the literature, facilitate concentration, and subtly improve game scores. The possibility for wearable devices to support learning, concentration, and focus are discussed.

Central nervous system symptoms, such as cognitive dysfunction, have been reported in Hereditary Transthyretin Amyloidosis (ATTRv). However, there is a lack of neuroimaging studies investigating structural alterations in the brain related to cognition in ATTRv amyloidosis. This study aimed to investigate cognition and cortical morphology in a cohort of ATTRv patients.

29 ATTRv patients and 26 healthy controls completed neuropsychological assessment. 21 of these patients underwent 3T brain MRI, and 23 healthy subjects constituted the control group for MRI. Cortical measures of volume, thickness, fractional anisotropy (FA), and mean diffusivity (MD) were obtained for both groups. Correlation analyses between brain and cognitive measurements were performed.

Patients displayed worse performance than controls in executive functions, verbal and visual memory, visuospatial domains, and language tests. Our study indicated cortical thinning in ATTRv patients in the temporal, occipital, frontal, and parietal areas. The inferior temporal gyrus correlated with verbal memory. Insula and, pars opercularis correlated with both verbal memory and executive function.

Cortical thickness in the inferior temporal gyrus, pars opercularis, and insula were linked to memory and executive function. We observed no correlations between cortical volume measures and cognition. Further investigations are imperative to confirm these findings across different populations.

Ischemic stroke is a vascular disease that may cause cognitive and behavioral abnormalities. This study aims to assess abnormal brain function in ischemic stroke patients using the percent amplitude of fluctuation (PerAF) method and further explore the feasibility of PerAF as an imaging biomarker for investigating ischemic stroke pathophysiology mechanisms. Sixteen ischemic stroke patients and 22 healthy controls (HCs) underwent resting state functional magnetic resonance imaging (rs-fMRI) scanning, and the resulting data were analyzed using PerAF. Then a correlation analysis was conducted between PerAF values and Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) scores. Finally, the abnormal PerAF values were extracted and defined as features for support vector machine (SVM) analysis. Compared with HCs, ischemic stroke patients showed decreased PerAF in the bilateral cuneus, left middle frontal gyrus, precuneus and right inferior temporal gyrus, and increased PerAF in the bilateral orbital part of middle frontal gyrus and right orbital part of superior frontal gyrus. Correlation analyses revealed that PerAF values in the left orbital part of middle frontal gyrus was negatively correlated with the MoCA scores. The SVM classification of the PerAF values achieved an area under the curve (AUC) of 0.98 and an accuracy of 94.74%. Abnormal brain function has been found among ischemic stroke patients, which may be correlated with visual impairment, attention deficits, and dysregulation of negative emotions following a stroke. Our findings may support the potential of PerAF as a sensitive biomarker for investigating the underlying mechanisms of ischemic stroke.

With the increased availability and sophistication of digital devices in the last decade, young people have become mainstream mobile phone users. Heavy mobile phone dependence causes affective problems (depression, anxiety) and loss of attention on current activities, leading to more cluttered thoughts. Problematic mobile phone use has been found to increase the occurrence of mind wandering, but the neural mechanism underlying this relationship remains unclear. The current study aims to investigate the neural mechanism between mobile phone use and mind wandering. University students from datasets (ongoing research project named Gene-Brain-Behavior project, GBB) completed psychological assessments of mobile phone addiction and mind wandering and underwent resting-state functional connectivity (FC) scanning. FC matrix was constructed to further conduct correlation and mediation analyses. Students with high mobile phone addiction scores were more likely to have high mind wandering scores. FC among the default mode, motor, frontoparietal, basal ganglia, limbic, medial frontal, visual association, and cerebellar networks formed the neural basis of mind wandering. FC between the frontoparietal and motor networks, between the default mode network and cerebellar network, and within the cerebellar network mediated the relationship between mobile phone addiction and mind wandering. The findings confirm that mobile phone addiction is a risk factor for increased mind wandering and reveal that FC in several brain networks underlies this relationship. They contribute to research on behavioral addiction, education, and mental health among young adults.

Abnormalities in large-scale neuronal networks-the frontoparietal central executive network (CEN)-are consistent findings in bipolar disorder and potential therapeutic targets for transcranial magnetic stimulation (TMS).

The present study aimed to assess the effects of CEN neurocircuit-based sequential TMS on the clinical symptoms and cognitive functions of adolescents with bipolar II disorder.

The study was a single-blinded, randomized, placebo-control trial. Participants with DSM-5-defined bipolar disorder II were recruited and randomized to receive either a sham treatment (n = 20) or an active TMS treatment (n = 22). The active group patients were taking medication, with intermittent theta burst stimulation (iTBS) treatment provided as adjunctive treatment targeting the left DLPFC, the left ITG, and the left PPC nodes consecutively. Patients completed the measurements of HAMD and the Das-Naglieri Cognition Assessment System at baseline and 3 weeks after the intervention.

A significant group-by-time interaction was observed in the HAMD, total cognition, and planning. Post-hoc analysis revealed that patients in the active group significantly improved HAMD scores following neurostimulation. Moreover, within-subject analysis indicated that the active group significantly improved in scores of total cognition and planning, while the sham group did not. No significant differences were seen in the other cognitive measures.

The neurocircuit-based sequential TMS protocol targeting three CEN nodes, in conjunction with medication, safely and effectively improved depressive symptoms and cognitive function in adolescents with bipolar II disorder.

Patients with bipolar disorder (BD) and major depressive disorder (MDD) exhibit depressive episodes with similar symptoms despite having different and poorly understood underlying neurobiology, often leading to misdiagnosis and improper treatment. This exploratory study examined whole-brain functional connectivity (FC) using FC multivariate pattern analysis (fc-MVPA) to identify the FC patterns with the greatest ability to distinguish between currently depressed patients with BD type I (BD I) and those with MDD.

In a cross-sectional design, 41 BD I, 40 MDD patients and 63 control participants completed resting state functional magnetic resonance imaging scans. Data-driven fc-MVPA, as implemented in the CONN toolbox, was used to identify clusters with differential FC patterns between BD patients and MDD patients. The identified cluster was used as a seed in a post hoc seed-based analysis (SBA) to reveal associated connectivity patterns, followed by a secondary ROI-to-ROI analysis to characterize differences in connectivity between these patterns among BD I patients, MDD patients and controls.

FC-MVPA identified one cluster located in the right frontal pole (RFP). The subsequent SBA revealed greater FC between the RFP and posterior cingulate cortex (PCC) and between the RFP and the left inferior/middle temporal gyrus (LI/MTG) and lower FC between the RFP and the left precentral gyrus (LPCG), left lingual gyrus/occipital cortex (LLG/OCC) and right occipital cortex (ROCC) in MDD patients than in BD patients. Compared with the controls, ROI-to-ROI analysis revealed lower FC between the RFP and the PCC and greater FC between the RFP and the LPCG, LLG/OCC and ROCC in BD patients; in MDD patients, the analysis revealed lower FC between the RFP and the LLG/OCC and ROCC and greater FC between the RFP and the LI/MTG.

Differences in the RFP FC patterns between currently depressed patients with BD and those with MDD suggest potential neuroimaging markers that should be further examined. Specifically, BD patients exhibit increased FC between the RFP and the motor and visual networks, which is associated with psychomotor symptoms and heightened compensatory frontoparietal FC to counter distractibility. In contrast, MDD patients exhibit increased FC between the RFP and the default mode network, corresponding to sustained self-focus and rumination.

Functional magnetic resonance imaging research employing regional homogeneity (ReHo) analysis has uncovered aberrant local brain connectivity in individuals with mild cognitive impairment (MCI) and Alzheimer's disease (AD) in comparison with healthy controls. However, the precise localization, extent, and possible overlap of these aberrations are still not fully understood. To bridge this gap, we applied a novel meta-analytic and Bayesian method (minimum Bayes Factor Activation Likelihood Estimation, mBF-ALE) for a systematic exploration of local functional connectivity alterations in MCI and AD brains. We extracted ReHo data via a standardized MEDLINE database search, which included 35 peer-reviewed experiments, 1,256 individuals with AD or MCI, 1,118 healthy controls, and 205 x-y-z coordinates of ReHo variation. We then separated the data into two distinct datasets: one for MCI and the other for AD. Two mBF-ALE analyses were conducted, thresholded at "very strong evidence" (mBF ≥ 150), with a minimum cluster size of 200 mm³. We also assessed the spatial consistency and sensitivity of our Bayesian results using the canonical version of the ALE algorithm. For MCI, we observed two clusters of ReHo decrease and one of ReHo increase. Decreased local connectivity was notable in the left precuneus (Brodmann area - BA 7) and left inferior temporal gyrus (BA 20), while increased connectivity was evident in the right parahippocampal gyrus (BA 36). The canonical ALE confirmed these locations, except for the inferior temporal gyrus. In AD, one cluster each of ReHo decrease and increase were found, with decreased connectivity in the right posterior cingulate cortex (BA 30 extending to BA 23) and increased connectivity in the left posterior cingulate cortex (BA 31). These locations were confirmed by the canonical ALE. The identification of these distinct functional connectivity patterns sheds new light on the complex pathophysiology of MCI and AD, offering promising directions for future neuroimaging-based interventions. Additionally, the use of a Bayesian framework for statistical thresholding enhances the robustness of neuroimaging meta-analyses, broadening its applicability to small datasets.

47,XXX (Triple X syndrome) is a sex chromosome aneuploidy characterized by the presence of a supernumerary X chromosome in affected females and is associated with a variable cognitive, behavioral, and psychiatric phenotype. The effect of a supernumerary X chromosome in affected females on intracortical microstructure is currently unknown. Therefore, we conducted 7 Tesla structural MRI and compared T1 (ms), as a proxy for intracortical myelin (ICM), across laminae of 21 adult women with 47,XXX and 22 age-matched typically developing females using laminar analyses. Relationships between phenotypic traits and T1 values in 47,XXX were also investigated. Adults with 47,XXX showed higher bilateral T1 across supragranular laminae in the banks of the superior temporal sulcus, and in the right inferior temporal gyrus, suggesting decreases of ICM primarily within the temporal cortex in 47,XXX. Higher social functioning in 47,XXX was related to larger inferior temporal gyrus ICM content. Our findings indicate an effect of a supernumerary X chromosome in adult-aged women on ICM across supragranular laminae within the temporal cortex. These findings provide insight into the role of X chromosome dosage on ICM across laminae. Future research is warranted to further explore the functional significance of altered ICM across laminae in 47,XXX.

Anhedonia is a critical diagnostic symptom of major depressive disorder (MDD), being associated with poor prognosis. Understanding the neural mechanisms underlying anhedonia is of great significance for individuals with MDD, and it encourages the search for objective indicators that can reliably identify anhedonia.

A predictive model used connectome-based predictive modeling (CPM) for anhedonia symptoms was developed by utilizing pre-treatment functional connectivity (FC) data from 59 patients with MDD. Node-based FC analysis was employed to compare differences in FC patterns between melancholic and non-melancholic MDD patients. The support vector machines (SVM) method was then applied for classifying these two subtypes of MDD patients.

CPM could successfully predict anhedonia symptoms in MDD patients (positive network: r = 0.4719, p < 0.0020, mean squared error = 23.5125, 5000 iterations). Compared to non-melancholic MDD patients, melancholic MDD patients showed decreased FC between the left cingulate gyrus and the right parahippocampus gyrus (p_bonferroni = 0.0303). This distinct FC pattern effectively discriminated between melancholic and non-melancholic MDD patients, achieving a sensitivity of 93.54%, specificity of 67.86%, and an overall accuracy of 81.36% using the SVM method.

This study successfully established a network model for predicting anhedonia symptoms in MDD based on FC, as well as a classification model to differentiate between melancholic and non-melancholic MDD patients. These findings provide guidance for clinical treatment.

Parkinson's disease (PD) is increasingly recognized for its non-motor symptoms, among which emotional disturbances and sleep disorders frequently co-occur. The commonality of neuroanatomical underpinnings for these symptoms is not fully understood. This study is intended to investigate the differences in gray matter volume (GMV) between PD patients with anxiety (A-PD) and those without anxiety (NA-PD). Additionally, it seeks to uncover the interplay between GMV variations and the manifestations of anxiety and sleep quality.

A total of 37 A-PD patients, 43 NA-PD patients, and 36 healthy controls (HCs) were recruited, all of whom underwent voxel-based morphometry (VBM) analysis. Group differences in GMV were assessed using analysis of covariance (ANCOVA). Partial correlation between GMV, anxiety symptom, and sleep quality were analyzed. Mediation analysis explored the mediating role of the volume of GMV-distinct brain regions on the relationship between sleep quality and anxiety within the PD patient cohort.

A-PD patients showed significantly lower GMV in the fusiform gyrus (FG) and right inferior temporal gyrus (ITG) compared to HCs and NA-PD patients. GMV in these regions correlated negatively with Hamilton Anxiety Rating Scale (HAMA) scores (right ITG: r = -0.690, p < 0.001; left FG: r = -0.509, p < 0.001; right FG: r = -0.576, p < 0.001) and positively with sleep quality in PD patients (right ITG: r = 0.592, p < 0.001; left FG: r = 0.356, p = 0.001; right FG: r = 0.470, p < 0.001). Mediation analysis revealed that GMV in the FG and right ITG mediated the relationship between sleep quality and anxiety symptoms, with substantial effect sizes accounted for by the right ITG (25.74%) and FG (left: 11.90%, right: 15.59%).

This study has shed further light on the relationship between sleep disturbances and anxiety symptoms in PD patients. Given the pivotal roles of the FG and the ITG in facial recognition and the recognition of emotion-related facial expressions, our findings indicate that compromised sleep quality, under the pathological conditions of PD, may exacerbate the reduction in GMV within these regions, impairing the recognition of emotional facial expressions and thereby intensifying anxiety symptoms.

Hemisphere functional lateralization is a prominent feature of the human brain. However, it is not known whether hemispheric lateralization features are altered in end-stage knee osteoarthritis (esKOA). In this study, we performed resting-state functional magnetic imaging on 46 esKOA patients and 31 healthy controls (HCs) and compared with the global and inter-hemisphere network to clarify the hemispheric functional network lateralization characteristics of patients. A correlation analysis was performed to explore the relationship between the inter-hemispheric network parameters and clinical features of patients. The node attributes were analyzed to explore the factors changing in the hemisphere network function lateralization in patients. We found that patients and HCs exhibited "small-world" brain network topology. Clustering coefficient increased in patients compared with that in HCs. The hemisphere difference in inter-hemispheric parameters including assortativity, global efficiency, local efficiency, clustering coefficients, small-worldness, and shortest path length. The pain course and intensity of esKOA were positively correlated with the right hemispheric lateralization in local efficiency, clustering coefficients, and the small-worldness, respectively. The significant alterations of several nodal properties were demonstrated within group in pain-cognition, pain-emotion, and pain regulation circuits. The abnormal lateralization inter-hemisphere network may be caused by the destruction of regional network properties.

Human learning varies greatly among individuals and is related to the microstructure of major white matter tracts in several learning domains, yet the impact of the existing microstructure of white matter tracts on future learning outcomes remains unclear. We employed a machine-learning model selection framework to evaluate whether existing microstructure might predict individual differences in learning a sensorimotor task, and further, if the mapping between tract microstructure and learning was selective for learning outcomes. We used diffusion tractography to measure the mean fractional anisotropy (FA) of white matter tracts in 60 adult participants who then practiced drawing a set of 40 unfamiliar symbols repeatedly using a digital writing tablet. We measured drawing learning as the slope of draw duration over the practice session and measured visual recognition learning for the symbols using an old/new 2-AFC task. Results demonstrated that tract microstructure selectively predicted learning outcomes, with left hemisphere pArc and SLF3 tracts predicting drawing learning and the left hemisphere MDLFspl predicting visual recognition learning. These results were replicated using repeat, held-out data and supported with complementary analyses. Results suggest that individual differences in the microstructure of human white matter tracts may be selectively related to future learning outcomes.

Table tennis players have adaptive visual and sensorimotor networks, which are the key brain regions to acquire environmental information and generate motor output. This study examined 20 table tennis players and 21 control subjects through ultrahigh field 7 Tesla magnetic resonance imaging. First, we measured percentage amplitude of fluctuation across five different frequency bands and found that table tennis players had significantly lower percentage amplitude of fluctuation values than control subjects in 18 brain regions, suggesting enhanced stability of spontaneous brain fluctuation amplitudes in visual and sensorimotor networks. Functional connectional analyses revealed increased static functional connectivity between two sensorimotor nodes and other frontal-parietal regions among table tennis players. Additionally, these players displayed enhanced dynamic functional connectivity coupled with reduced static connectivity between five nodes processing visual and sensory information input, and other large-scale cross-regional areas. These findings highlight that table tennis players undergo neural adaptability through a dual mechanism, characterized by global stability in spontaneous brain fluctuation amplitudes and heightened flexibility in visual sensory networks. Our study offers novel insights into the mechanisms of neural adaptability in athletes, providing a foundation for future efforts to enhance cognitive functions in diverse populations, such as athletes, older adults, and individuals with cognitive impairments.

Very preterm (VPT) infants (born at less than 32 weeks gestational age) are at high risk for various adverse neurodevelopmental deficits. Unfortunately, most of these deficits cannot be accurately diagnosed until the age of 2-5 years old. Given the benefits of early interventions, accurate diagnosis and prediction soon after birth are urgently needed for VPT infants. Previous studies have applied deep learning models to learn the brain structural connectome (SC) to predict neurodevelopmental deficits in the preterm population. However, none of these models are specifically designed for graph-structured data, and thus may potentially miss certain topological information conveyed in the brain SC. In this study, we aim to develop deep learning models to learn the SC acquired at term-equivalent age for early prediction of neurodevelopmental deficits at 2 years corrected age in VPT infants. We directly treated the brain SC as a graph, and applied graph convolutional network (GCN) models to capture complex topological information of the SC. In addition, we applied the supervised contrastive learning (SCL) technique to mitigate the effects of the data scarcity problem, and enable robust training of GCN models. We hypothesize that SCL will enhance GCN models for early prediction of neurodevelopmental deficits in VPT infants using the SC. We used a regional prospective cohort of ∼280 VPT infants who underwent MRI examinations at term-equivalent age from the Cincinnati Infant Neurodevelopment Early Prediction Study (CINEPS). These VPT infants completed neurodevelopmental assessment at 2 years corrected age to evaluate cognition, language, and motor skills. Using the SCL technique, the GCN model achieved mean areas under the receiver operating characteristic curve (AUCs) in the range of 0.72∼0.75 for predicting three neurodevelopmental deficits, outperforming several competing models. Our results support our hypothesis that the SCL technique is able to enhance the GCN model in our prediction tasks.

It has been revealed that abnormal voxel-mirrored homotopic connectivity (VMHC) is present in patients with schizophrenia, yet there are inconsistencies in the relevant findings. Moreover, little is known about their association with brain gene expression profiles. In this study, transcription-neuroimaging association analyses using gene expression data from Allen Human Brain Atlas and case-control VMHC differences from both the discovery (meta-analysis, including 9 studies with a total of 386 patients and 357 controls) and replication (separate group-level comparisons within two datasets, including a total of 258 patients and 287 controls) phases were performed to identify genes associated with VMHC alterations. Enrichment analyses were conducted to characterize the biological functions and specific expression of identified genes, and Neurosynth decoding analysis was performed to examine the correlation between cognitive-related processes and VMHC alterations in schizophrenia. In the discovery and replication phases, patients with schizophrenia exhibited consistent VMHC changes compared to controls, which were correlated with a series of cognitive-related processes; meta-regression analysis revealed that illness duration was negatively correlated with VMHC abnormalities in the cerebellum and postcentral/precentral gyrus. The abnormal VMHC patterns were stably correlated with 1287 genes enriched for fundamental biological processes like regulation of cell communication, nervous system development, and cell communication. In addition, these genes were overexpressed in astrocytes and immune cells, enriched in extensive cortical regions and wide developmental time windows. The present findings may contribute to a more comprehensive understanding of the molecular mechanisms underlying VMHC alterations in patients with schizophrenia.

This study intended to investigate the frequency specific brain oscillation activity in patients with acute basal ganglia ischemic stroke (BGIS) by using the degree centrality (DC) method. A total of 34 acute BGIS patients and 44 healthy controls (HCs) underwent resting-state functional magnetic resonance imaging (rs-fMRI) scanning. The DC values in three frequency bands (conventional band: 0.01-0.08 Hz, slow‑4 band: 0.027-0.073 Hz, slow‑5 band: 0.01-0.027 Hz) were calculated. A two-sample t-test was used to explore the between-group differences in the conventional frequency band. A two-way repeated-measures analysis of variance (ANOVA) was used to analyze the DC differences between groups (BGIS patients, HCs) and bands (slow‑4, slow‑5). Moreover, correlations between DC values and clinical indicators were performed. In conventional band, the DC value in the right middle temporal gyrus was decreased in BGIS patients compared with HCs. Significant differences of DC were observed between the two bands mainly in the bilateral cortical brain regions. Compared with the HCs, the BGIS patients showed increased DC in the right superior temporal gyrus and the left precuneus, but decreased mainly in the right inferior temporal gyrus, right inferior occipital gyrus, right precentral, and right supplementary motor area. Furthermore, the decreased DC in the right rolandic operculum in slow-4 band and the right superior temporal gyrus in slow-5 band were found by post hoc two-sample t-test of main effect of group. There was no significant correlation between DC values and clinical scales after Bonferroni correction. Our findings showed that the DC changes in BGIS patients were frequency specific. Functional abnormalities in local brain regions may help us to understand the underlying pathogenesis mechanism of brain functional reorganization of BGIS patients.

The resting-state connectivity features underlying pure generalized anxiety disorder (GAD, G1) and comorbid GAD and depressive symptoms (G2) have not been directly compared. Furthermore, it is unclear whether these features might serve as potential prognostic biomarkers and change with treatment. Degree centrality (DC) in G1 (40 subjects), G2 (58 subjects), and healthy controls (HCs, 54 subjects) was compared before treatment, and the DC of G1 or G2 at baseline was compared with that after 4 weeks of paroxetine treatment. Using support vector regression (SVR), voxel-wise DC across the entire brain and abnormal DC at baseline were employed to predict treatment response. At baseline, G1 and G2 exhibited lower DC in the left mid-cingulate cortex and vermis IV/V compared to HCs. Additionally, compared to HCs, G1 had lower DC in the left middle temporal gyrus, while G2 showed higher DC in the right inferior temporal/fusiform gyrus. However, there was no significant difference in DC between G1 and G2. The SVR based on abnormal DC at baseline could successfully predict treatment response in responders in G2 or in G1 and G2. Notably, the predictive performance based on abnormal DC at baseline surpassed that based on DC across the entire brain. After treatment, G2 responders showed lower DC in the right medial orbital frontal gyrus, while no change in DC was identified in G1 responders. The G1 and G2 showed common and distinct dysconnectivity patterns and they could potentially serve as prognostic biomarkers. Furthermore, DC in patients with GAD could change with treatment.

All experiences preserved within episodic memory contain information on the space and time of events. The hippocampus is the main brain region involved in processing spatial and temporal information for incorporation within episodic memory representations. However, the other brain regions involved in the encoding and retrieval of spatial and temporal information within episodic memory are unclear, because a systematic review of related studies is lacking and the findings are scattered. The present study was designed to integrate the results of functional magnetic resonance imaging and positron emission tomography studies by means of a systematic review and meta-analysis to provide converging evidence. In particular, we focused on identifying the brain regions involved in the retrieval of spatial and temporal information. We identified a spatial retrieval network consisting of the inferior temporal gyrus, parahippocampal gyrus, superior parietal lobule, angular gyrus, and precuneus. Temporal context retrieval was supported by the dorsolateral prefrontal cortex. Thus, the retrieval of spatial and temporal information is supported by different brain regions, highlighting their different natures within episodic memory.

This study aimed to investigate the relationship between exercise addiction and brain structure in middle-older individuals, and to examine the role of self-efficacy in mediating physiological changes associated with exercise addiction. A total of 133 patients exhibiting symptoms of exercise addiction were recruited for this study (male = 43, age 52.86 ± 11.78 years). Structural magnetic resonance imaging and behavioral assessments were administered to assess the study population. Voxel-based morphological analysis was conducted using SPM12 software. Mediation analysis was employed to explore the potential neuropsychological mechanism of self-efficacy in relation to exercise addiction. The findings revealed a positive correlation between exercise addiction and gray matter volume in the right inferior temporal region and the right hippocampus. Conversely, there was a negative correlation with gray matter volume in the left Rolandic operculum. Self-efficacy was found to indirectly influence exercise addiction by affecting right inferior temporal region gray matter volume and acted as a mediating variable in the relationship between the gray matter volume of right inferior temporal region and exercise addiction. In summary, this study elucidates the link between exercise addiction and brain structure among middle-older individuals. It uncovers the intricate interplay among exercise addiction, brain structure, and psychological factors. These findings enhance our comprehension of exercise addiction and offer valuable insights for the development of interventions and treatments.

The connectivity of the temporoparietal (TP) region has been the subject of multiple anatomical and functional studies. Its role in high cognitive functions has been primarily correlated with long association fiber connections. As a major sensory integration hub, coactivation of areas within the TP requires a stream of short association fibers running between its subregions. The latter have been the subject of a small number of recent in vivo and cadaveric studies. This has resulted in limited understanding of this network and, in certain occasions, terminology ambiguity.

To systematically study the vertical parietal and temporoparietal short association fibers.

Thirteen normal, adult cadaveric hemispheres, were treated with the Klinger's freeze-thaw process and their subcortical anatomy was studied using the microdissection technique.

Two separate fiber layers were identified. Superficially, directly beneath the cortical u-fibers, the Stratum proprium intraparietalis (SP) was seen connecting Superior Parietal lobule and Precuneal cortical areas to inferior cortical regions of the Parietal lobe, running deep to the Intraparietal sulcus. At the same dissection level, the IPL-TP fibers were identified as a bundle connecting the Inferior Parietal lobule with posterior Temporal cortical areas. At a deeper level, parallel to the Arcuate fasciculus fibers, the SPL-TP fibers were seen connecting the Superior Parietal lobule to posterior Temporal cortical areas.

To our knowledge this is the first cadaveric dissection study to comprehensively study and describe of the vertical association fibers of the temporoparietal region while proposing a universal terminology.

Repetitive transcranial magnetic stimulation (rTMS) and cognitive training for patients with Alzheimer's disease (AD) can change functional connectivity (FC) within gray matter (GM). However, the role of white matter (WM) and changes of GM-WM FC under these therapies are still unclear. To clarify this problem, we applied 40 Hz rTMS over angular gyrus (AG) concurrent with cognitive training to 15 mild-moderate AD patients and analyzed the resting-state functional magnetic resonance imaging before and after treatment. Through AG-based FC analysis, corona radiata and superior longitudinal fasciculus (SLF) were identified as activated WM tracts. Compared with the GM results with AG as seed, more GM regions were found with activated WM tracts as seeds. The averaged FC, fractional amplitude of low-frequency fluctuation (fALFF), and regional homogeneity (ReHo) of the above GM regions had stronger clinical correlations (r/P = 0.363/0.048 vs 0.299/0.108, 0.351/0.057 vs 0.267/0.153, 0.420/0.021 vs 0.408/0.025, for FC/fALFF/ReHo, respectively) and better classification performance to distinguish pre-/post-treatment groups (AUC = 0.91 vs 0.88, 0.65 vs 0.63, 0.87 vs 0.82, for FC/fALFF/ReHo, respectively). Our results indicated that rTMS concurrent with cognitive training could rewire brain network by enhancing GM-WM FC in AD, and corona radiata and SLF played an important role in this process.

Healthy sleep is important for adolescent neurodevelopment, and relationships between brain structure and sleep can vary in strength over this maturational window. Although cortical gyrification is increasingly considered a useful index for understanding cognitive and emotional outcomes in adolescence, and sleep is also a strong predictor of such outcomes, we know relatively little about associations between cortical gyrification and sleep. We aimed to identify developmentally invariant (stable across age) or developmentally specific (observed only during discrete age intervals) gyrification-sleep relationships in young people.

A total of 252 Neuroimaging and Pediatric Sleep Databank participants (9-26 years; 58.3% female) completed wrist actigraphy and a structural MRI scan. Local gyrification index (lGI) was estimated for 34 bilateral brain regions. Naturalistic sleep characteristics (duration, timing, continuity, and regularity) were estimated from wrist actigraphy. Regularized regression for feature selection was used to examine gyrification-sleep relationships.

For most brain regions, greater lGI was associated with longer sleep duration, earlier sleep timing, lower variability in sleep regularity, and shorter time awake after sleep onset. lGI in frontoparietal network regions showed associations with sleep patterns that were stable across age. However, in default mode network regions, lGI was only associated with sleep patterns from late childhood through early-to-mid adolescence, a period of vulnerability for mental health disorders.

We detected both developmentally invariant and developmentally specific ties between local gyrification and naturalistic sleep patterns. Default mode network regions may be particularly susceptible to interventions promoting more optimal sleep during childhood and adolescence.

Prior studies have discussed rapid eye movement (REM) sleep disturbance as a potential endophenotype of major depressive disorder (MDD). However, the neural substrates underlying the percentage of REM sleep duration (REM%) and its association with disease progression in MDD remain unclear.

One hundred and fourteen MDD patients and 74 healthy controls (HCs) underwent resting-state functional and perfusion magnetic resonance imaging (MRI) scans as well as overnight polysomnography examination to assess brain function and REM%, with 48 patients completing follow-up visits. Correlation and mediation analyses were conducted to investigate the associations among baseline REM%, multimodal brain imaging measures, and the improvement of depressive symptoms at follow-up in MDD.

We found voxel-wise correlations between baseline REM% and multimodal brain imaging metrics in many brain regions involved in sensorimotor, visual processing, emotion, and cognition in patients with MDD. Moreover, the baseline REM% was correlated with the improvement of depressive symptoms from acute to remitted status in patients through regulating brain activity in the left inferior temporal gyrus and cerebral blood flow in the bilateral paracentral lobule.

Our findings help to identify the neural underpinnings of REM% in depression and highlight REM% as a potential prognostic biomarker to predict disease progression. These may inform future novel interventions of MDD from the perspective of regulating REM sleep.

Memory complaints are highly prevalent among middle-aged and older adults, and they are frequently reported in individuals experiencing subjective cognitive decline (SCD). SCD has received increasing attention due to its implications for the early detection of dementia. This study aims to advance our comprehension of individuals with SCD by elucidating potential cognitive/psychologic-contributing factors and characterizing cerebral hubs within the brain network. To identify these potential contributing factors, a structural equation modeling approach was employed to investigate the relationships between various factors, such as metacognitive beliefs, personality, anxiety, depression, self-esteem, and resilience, and memory complaints. Our findings revealed that self-esteem and conscientiousness significantly influenced memory complaints. At the cerebral level, analysis of delta and theta electroencephalographic frequency bands recorded during rest was conducted to identify hub regions using a local centrality metric known as betweenness centrality. Notably, our study demonstrated that certain brain regions undergo changes in their hub roles in response to the pathology of SCD. Specifically, the inferior temporal gyrus and the left orbitofrontal area transition into hubs, while the dorsolateral prefrontal cortex and the middle temporal gyrus lose their hub function in the presence of SCD. This rewiring of the neural network may be interpreted as a compensatory response employed by the brain in response to SCD, wherein functional connectivity is maintained or restored by reallocating resources to other regions.

Numerous studies characterized how resting-state functional connectivities (rsFCs) of the amygdala were disrupted in emotional disorders and varied with emotional traits, including anxiety. With trait anxiety known to diminish with age, a critical issue concerns disambiguating the effects of age and anxiety on amygdala rsFCs in studying the neural bases of individual differences in anxiety.

Two-hundred adults (83 women) 19-85 years of age underwent fMRI and assessment for trait anxiety. Amygdala rsFC correlates were identified using multiple regression with age and anxiety in the same model for all and separately in men and women. The rsFC correlates were examined for age-anxiety interaction.

Anxiety was negatively correlated with amygdala-temporooccipital gyri rsFC in all and in men alone. In women, amgydala rsFC with the thalamus/pallidum, angular/supramarginal gyri, inferior temporal gyrus, and posterior insula correlated positively and rsFC with calcarine cortex and caudate correlated negatively with anxiety. We also observed sex differences in age correlation of amgydala-posterior cingulate cortex/precuneus and -insula/temporoparietal rsFCs, with stronger associations in women. In women alone, anxiety and age interacted to determine amygdala rsFC with the thalamus/pallidum, calcarine cortex, and caudate, with older age associated with stronger correlation between anxiety and the rsFCs.

The findings need to be validated in an independent sample and further explored using task-based data.

Highlighting anxiety- and age- specific as well as interacting correlates of amygdala rsFCs and sex differences in the correlates, the findings may shed light on the neural markers of anxiety.