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Age-related changes in the brain connectivity of healthy older adults have been widely studied in recent years, with some differences in the obtained results. Most of these studies showed decreases in general functional connectivity, but they also found increases in some particular regions and areas. Frequently, these studies compared young individuals with older subjects, but few studies compared different age groups only in older populations. The purpose of this study is to analyze whole-brain functional connectivity in healthy older adult groups and its network characteristics through functional segregation. A total of 114 individuals, 48 to 89 years old, were scanned using resting-state functional magnetic resonance imaging in a resting state paradigm and were divided into six different age groups (< 60, 60-64, 65-69, 70-74, 75-79, ≥ 80 years old). A partial correlation analysis, a pooled correlation analysis and a study of 3-cycle regions with prominent connectivity were conducted. Our results showed progressive diminution in the functional connectivity among different age groups and this was particularly pronounced between 75 and 79 years old. The oldest group (≥ 80 years old) showed a slight increase in functional connectivity compared to the other groups. This occurred possibly because of compensatory mechanism in brain functioning. This study provides information on the brain functional characteristics of every age group, with more specific information on the functional progressive decline, and supplies methodological tools to study functional connectivity characteristics. Approval for the study was obtained from the ethics committee of the Comisión de Bioética de la Universidad de Barcelona (approval No. PSI2012-38257) on June 5, 2012, and from the ethics committee of the Barcelona's Hospital Clínic (approval No. 2009-5306 and 2011-6604) on October 22, 2009 and April 7, 2011 respectively.

Exploring the disruptions to intrinsic resting-state networks (RSNs) in schizophrenia-spectrum disorders yields a better understanding of the disease-specific pathophysiology. However, our knowledge of the neurobiological underpinnings of schizotypal personality disorders mostly relies on research on schizotypy or schizophrenia. This study aimed to investigate the RSN abnormalities of schizotypal personality disorder (SPD) and their clinical implications. Using resting-state data, the intra- and inter-network of the higher-order functional networks (default mode network, DMN; frontoparietal network, FPN; dorsal attention network, DAN; salience network, SN) were explored in 22 medication-free, community-dwelling, non-help seeking individuals diagnosed with SPD and 30 control individuals. Consequently, while there were no group differences in intra-network functional connectivity across DMN, FPN, DAN, and SN, the SPD participants exhibited attenuated anticorrelation between the right frontal eye field region of the DAN and the right posterior parietal cortex region of the FPN. The decreases in anticorrelation were correlated with increased cognitive–perceptual deficits and disorganization factors of the schizotypal personality questionnaire, as well as reduced independence–performance of the social functioning scale for all participants together. This study, which links SPD pathology and social functioning deficits, is the first evidence of impaired large-scale intrinsic brain networks in SPD.

Machine learning (ML) approaches are increasingly being applied to neuroimaging data. Studies in neuroscience typically have to rely on a limited set of training data which may impair the generalizability of ML models. However, it is still unclear which kind of training sample is best suited to optimize generalization performance. In the present study, we systematically investigated the generalization performance of sex classification models trained on the parcelwise connectivity profile of either single samples or compound samples of two different sizes. Generalization performance was quantified in terms of mean across‐sample classification accuracy and spatial consistency of accurately classifying parcels. Our results indicate that the generalization performance of parcelwise classifiers (pwCs) trained on single dataset samples is dependent on the specific test samples. Certain datasets seem to “match” in the sense that classifiers trained on a sample from one dataset achieved a high accuracy when tested on the respected other one and vice versa. The pwCs trained on the compound samples demonstrated overall highest generalization performance for all test samples, including one derived from a dataset not included in building the training samples. Thus, our results indicate that both a large sample size and a heterogeneous data composition of a training sample have a central role in achieving generalizable results. Machine learning is often used to study neuroimaging data. However, ML‐studies often use single datasets. Here, we investigated the generalization performance in sex classification of parcel‐wise classifiers (pwC) using RS‐fMRI data. Our results indicated that compound samples show higher accuracies and better spatial consistency than single sample pwCs.

Dance is unique in that it is a sport and an art simultaneously. Beyond improving sensorimotor functions, dance training could benefit high‐level emotional and cognitive functions. Duo dances also confer the possibility for dancers to develop the abilities to recognize, understand, and share the thoughts and feelings of their dance partners during the long‐term dance training. To test this possibility, we collected high‐resolution structural and resting‐state functional magnetic resonance imaging (MRI) data from 43 expert‐level ballroom dancers (a model of long‐term exposure to duo dance training) and 40 age‐matched and sex‐matched nondancers, and measured their empathic ability using a self‐report trait empathy scale. We found that ballroom dancers showed higher scores of empathic concern (EC) than controls. The EC scores were positively correlated with years with dance partners but negatively correlated with the number of dance partners for ballroom dancers. These behavioral results were supported by the structural and functional MRI data. Structurally, we observed that the gray matter volumes in the subgenual anterior cingulate cortex (ACC) and EC scores were positively correlated. Functionally, the connectivity between ACC and occipital gyrus was positively correlated with both EC scores and years with dance partners. In addition, the relationship between years with dance partners and EC scores was indirect‐only mediated by the ACC‐occipital gyrus functional connectivity. Therefore, our findings provided solid evidence for the close link between long‐term ballroom dance training and empathy, which deepens our understanding of the neural mechanisms underlying this phenomenon. This study provided solid behavioral and neural evidence showing that long‐term ballroom dance training with relatively fixed dance partners is associated with one's empathic concern (EC). Theoretically, this study deepens our understanding of the neural mechanisms underlying the link between ballroom dance training and EC, and highlights the crucial role of resting‐state functional connectivity between the anterior cingulate cortex and occipital gyrus in mediating the relationship between dance training and EC. Practically, this study shed new insight into the development of duo dance‐based programs to improve empathic ability, thus helping people with impaired empathy, such as individuals with schizophrenia or autism spectrum disorder.

The habenula has been implicated in the pathogenesis of pain and analgesia, while evidence concerning its function in chronic low back pain (cLBP) is sparse. This study aims to investigate the resting‐state functional connectivity (rsFC) and effective connectivity of the habenula in 52 patients with cLBP and 52 healthy controls (HCs) and assess the feasibility of distinguishing cLBP from HCs based on connectivity by machine learning methods. Our results indicated significantly enhanced rsFC of the habenula‐left superior frontal cortex (SFC), habenula‐right thalamus, and habenula‐bilateral insular pathways as well as decreased rsFC of the habenula‐pons pathway in cLBP patients compared to HCs. Dynamic causal modelling revealed significantly enhanced effective connectivity from the right thalamus to right habenula in cLBP patients compared with HCs. RsFC of the habenula‐SFC was positively correlated with pain intensities and Hamilton Depression scores in the cLBP group. RsFC of the habenula‐right insula was negatively correlated with pain duration in the cLBP group. Additionally, the combination of the rsFC of the habenula‐SFC, habenula‐thalamus, and habenula‐pons pathways could reliably distinguish cLBP patients from HCs with an accuracy of 75.9% by support vector machine, which was validated in an independent cohort (N = 68, accuracy = 68.8%, p = .001). Linear regression and random forest could also distinguish cLBP and HCs in the independent cohort (accuracy = 73.9 and 55.9%, respectively). Overall, these findings provide evidence that cLBP may be associated with abnormal rsFC and effective connectivity of the habenula, and highlight the promise of machine learning in chronic pain discrimination. Our study revealed abnormal resting‐state functional connectivity and effective connectivity of the habenula with cortical/subcortical areas in patients with chronic low back pain than healthy control subjects, and the habenula connectivity can be used to distinguish chronic low back pain and healthy controls reliably by machine learning methods.

ABSTRACT Delineating the functional organization of mesoscale cortical columnar structure is essential for understanding brain function. We have previously demonstrated a high spatial correspondence between BOLD fMRI and LFP responses to tactile stimuli in the primary somatosensory cortex area 3b of nonhuman primates. This study aims to explore how 2D spatial profiles of the functional column vary across cortical layers (defined by three cortical depths) in both tactile stimulation and resting states using fMRI. At 9.4 T, we acquired submillimeter‐resolution oblique fMRI data from cortical areas 3b and 1 of anesthetized squirrel monkeys and obtained fMRI signals from three cortical layers. In both areas 3b and 1, the tactile stimulus‐evoked fMRI activation foci were fitted with point spread functions (PSFs), from which shape parameters, including full width at half maximum (FWHM), were derived. Seed‐based resting‐state fMRI data analysis was then performed to measure the spatial profiles of resting‐state connectivity within and between areas 3b and 1. We found that the tactile‐evoked fMRI response and local resting‐state functional connectivity were elongated at the superficial layer, with the major axes oriented in lateral to medial (from digit 1 to digit 5) direction. This elongation was significantly reduced in the deeper (middle and bottom) layers. To assess the robustness of these spatial profiles in distinguishing cortical layers, shape parameters describing the spatial extents of activation and resting‐state connectivity profiles were used to classify the layers via self‐organizing maps (SOM). A minimal overall classification error (~13%) was achieved, effectively classifying the layers into two groups: the superficial layer exhibited distinct features from the two deeper layers in the rsfMRI data. Our results support distinct 2D spatial profiles for superficial versus deeper cortical layers and reveal similarities between stimulus‐evoked and resting‐state configurations. This study characterizes 2D spatial profiles of mesoscale functional columns in the primary somatosensory cortex using high‐field fMRI and AI‐based clustering. Findings reveal distinct spatial features between cortical layers and high correspondence between stimulus and resting states, offering new insights into layer‐specific functional organization and data‐driven neuroimaging applications.

ABSTRACT Removal of nuisance signals (such as motion) from the BOLD time series is an important aspect of preprocessing to obtain meaningful resting‐state functional connectivity (rs‐FC). The nuisance signals are commonly removed using denoising procedures at the finest resolution, that is the voxel time series. Typically, the voxel‐wise time series are then aggregated into predefined regions or parcels to obtain an rs‐FC matrix as the correlation between pairs of regional time series. Computational efficiency can be improved by denoising the aggregated regional time series instead of the voxel time series. However, a comprehensive comparison of the effects of denoising on these two resolutions is missing. In this study, we systematically investigate the effects of denoising at different time series resolutions (voxel‐level and region‐level) in 370 unrelated subjects from the HCP‐YA dataset. Alongside the time series resolution, we considered additional factors such as aggregation method (Mean and first eigenvariate [EV]) and parcellation granularity (100, 400, and 1000 regions). To assess the effect of those choices on the utility of the resulting whole‐brain rs‐FC, we evaluated the individual specificity (fingerprinting) and the capacity to predict age and three cognitive scores. Our findings show generally equal or better performance for region‐level denoising with notable differences depending on the aggregation method. Using Mean aggregation yielded equal individual specificity and prediction performance for voxel‐level and region‐level denoising. When EV was employed for aggregation, the individual specificity of voxel‐level denoising was reduced compared to region‐level denoising. Increasing parcellation granularity generally improved individual specificity. For the prediction of age and cognitive test scores, only fluid intelligence indicated worse performance for voxel‐level denoising in the case of aggregating with the EV. Based on these results, we recommend the adoption of region‐level denoising for brain‐behavior investigations when using Mean aggregation. This approach offers equal individual specificity and prediction capacity with reduced computational resources for the analysis of rs‐FC patterns. Region‐level denoising is computationally efficient and effective: Denoising aggregated regional time series (as opposed to voxel‐level) generally yields equal or better results in individual specificity and predictive capacity.

ABSTRACT Alcohol Use Disorder (AUD), a prevalent and potentially severe psychiatric condition, is one of the leading causes of morbidity and mortality. This systematic review investigates the relationship between AUD and resting‐state functional connectivity (rsFC) derived from functional magnetic resonance imaging data. Following the PRISMA guidelines, a comprehensive search yielded 248 papers, and a screening process identified 39 studies with 73 relevant analyses. Using the automated anatomical labeling atlas for whole‐brain parcellation, relevance maps were generated to quantify associations between brain regions and their connections with AUD. These outcomes are based on the frequency with which significant findings are reported in the literature, to deal with the challenge of methodological diversity between analyses, including sample sizes, types of independent rsFC features, and AUD measures. The analysis focuses on whole‐brain studies to mitigate selection biases associated with seed‐based approaches. The most frequently reported regions include the middle and superior frontal gyri, the anterior cingulate cortex, and the insula. The generated relevance maps can serve as a valuable tool for formulating hypotheses and advancing our understanding of AUD's neural correlates in the future. This work also provides a template on how to quantitatively summarize a diverse literature, which could be applied to more specific aspects of AUD, including craving, relapse, binge drinking, or other diseases. Investigating the interplay between brain connectivity and alcohol use disorder (AUD), this review synthesizes findings and methodologies from resting‐state fMRI studies to develop relevance maps that link brain regions and functional connectivity to disorder manifestations, serving as a foundational resource for future hypotheses and methodologies in AUD research.

ABSTRACT Given impulsivity's multidimensional nature and its implications across various aspects of human behavior, a comprehensive understanding of functional brain circuits associated with this trait is warranted. In the current study, we utilized whole‐brain resting‐state functional connectivity data of healthy males (n = 156) to identify a network of connections predictive of an individual's impulsivity, as assessed by the Barratt Impulsiveness Scale (BIS)‐11. Our participants were selected, in part, based on their self‐reported BIS‐11 impulsivity scores. Specifically, individuals who reported high or low trait impulsivity scores during screening were selected first, followed by those with intermediate impulsivity levels. This enabled us to include participants with rare, extreme scores and to cover the entire BIS‐11 impulsivity spectrum. We employed repeated K‐fold cross‐validation for feature‐selection and used stratified 10‐fold cross‐validation to train and test our models. Our findings revealed a widespread neural network associated with trait impulsivity and a notable correlation between predicted and observed scores. Feature importance and node degree were assessed to highlight specific nodes and edges within the impulsivity network, revealing previously overlooked key brain regions, such as the cerebellum, brainstem, and temporal lobe, while supporting previous findings on the basal ganglia‐thalamo‐prefrontal network and the prefrontal‐motor strip network in relation to impulsiveness. This deepened understanding establishes a foundation for identifying alterations in functional brain networks associated with dysfunctional impulsivity. This study utilized machine learning to analyze whole‐brain functional connectivity data, mapping trait impulsivity. Findings revealed previously overlooked key brain regions within the impulsivity network, such as the cerebellum, brainstem, and temporal lobe, while supporting previous research on the basal ganglia‐thalamo‐prefrontal and the prefrontal‐motor strip network.

The perception and imagery of landmarks activate similar content‐dependent brain areas, including occipital and temporo‐medial brain regions. However, how these areas interact during visual perception and imagery of scenes, especially when recollecting their spatial location, remains unknown. Here, we combined functional magnetic resonance imaging (fMRI), resting‐state functional connectivity (rs‐fc), and effective connectivity to assess spontaneous fluctuations and task‐induced modulation of signals among regions entailing scene‐processing, the primary visual area and the hippocampus (HC), responsible for the retrieval of stored information. First, we functionally defined the scene‐selective regions, that is, the occipital place area (OPA), the retrosplenial complex (RSC) and the parahippocampal place area (PPA), by using the face/scene localizer, observing that two portions of the PPA—anterior and posterior PPA—were consistently activated in all subjects. Second, the rs‐fc analysis (n = 77) revealed a connectivity pathway similar to the one described in macaques, showing separate connectivity routes linking the anterior PPA with RSC and HC, and the posterior PPA with OPA. Third, we used dynamic causal modelling to evaluate whether the dynamic couplings among these regions differ between perception and imagery of familiar landmarks during a fMRI task (n = 16). We found a positive effect of HC on RSC during the retrieval of imagined places and an effect of occipital regions on both RSC and pPPA during the perception of scenes. Overall, we propose that under similar functional architecture at rest, different neural interactions take place between regions in the occipito‐temporal higher‐level visual cortex and the HC, subserving scene perception and imagery. While recruiting common areas, visual perception and imagery obey different principles. By using effective connectivity we show that the information flow follows a postero‐anterior (i.e., from visual to high‐order areas) and an antero‐posterior progression (i.e., the opposite direction) during the visual perception and the visual imagery of familiar landmarks, respectively.