Resting-state Functional Connectivity Research Articles

Brain functional connectivity, but not neuroanatomy, captures the interrelationship between sex and gender in preadolescents.

Understanding sex differences in the adolescent brain is crucial, as these differences are linked to neurological and psychiatric conditions that vary between males and females. Predicting sex from adolescent brain data may offer valuable insights into how these variations shape neurodevelopment. Recently, attention has shifted toward exploring socially-identified gender, distinct from sex assigned at birth, recognizing its additional explanatory power. This study evaluates whether resting-state functional connectivity (rsFC) or cortical thickness more effectively predicts sex and sex/gender alignment (the congruence between sex and gender) and investigates their interrelationship in preadolescents. Using data from the Adolescent Brain Cognitive Development (ABCD) Study, we employed machine learning to predict both sex (assigned at birth) and sex/gender alignment from rsFC and cortical thickness. rsFC predicted sex with significantly higher accuracy (86%) than cortical thickness (75%) and combining both did not improve the rsFC model's accuracy. Brain regions most effective in predicting sex belonged to association (default mode, dorsal attention, and parietal memory) and visual (visual and medial visual) networks. The rsFC sex classifier trained on sex/gender aligned youth was significantly more effective in classifying unseen youth with sex/gender alignment than in classifying unseen youth with sex/gender unalignment. In females, the degree to which their brains' rsFC matched a sex profile (female or male), was positively associated with the degree of sex/gender alignment. Lastly, neither rsFC nor cortical thickness predicted sex/gender alignment. These findings highlight rsFC's predictive power in capturing the relationship between sex and gender and the complexity of the interplay between sex, gender, and the brain's functional connectivity and neuroanatomy.

Food insecurity and adolescent impulsivity: The mediating role of functional connectivity in the context of family flexibility.

Adolescent food insecurity is a salient adversity hypothesized to affect neural systems associated with increased impulsive behavior. Family environments shape how adverse experiences influence development. In this study, hypotheses were tested regarding the conjoint effects of food insecurity and family flexibility on impulsivity via alterations in connectivity between regions within the salience and central executive networks. Such alterations are reflected in resting-state functional connectivity (rsFC) metrics between the anterior insula (AI) and the middle frontal gyrus (MFG). Hypotheses were tested in a longitudinal moderated mediation model with two waves of data from 142 adolescents (Time 1 [T1] Mage = 12.89, SD=0.85; Time 2 [T2] Mage = 15.01, SD=1.07). Data on past-year household food insecurity, family flexibility, and rsFC were obtained at T1. Impulsivity was self-reported by the adolescent at T1 and T2. Findings revealed that high T1 left-to-left rsFC between the AI and MFG was associated with increased impulsivity at T2. The interaction of family flexibility and food insecurity was associated with AI and MFG rsFC. In the context of low family flexibility, food insecurity was linked to high levels of AI and MFG rsFC. Conversely, in the context of optimal family flexibility, food insecurity was associated with low levels of AI and MFG rsFC. Conditional indirect analysis suggests that the links among food insecurity, rsFC, and impulsive behavior depend on family flexibility. RESEARCH HIGHLIGHTS: Adolescent food insecurity was associated with anterior insula and middle frontal gyrus connectivity only at certain levels of family flexibility. High family flexibility attenuated the link between food insecurity and neural connectivity, while low levels of family flexibility increased this risk. High left anterior insula and left middle frontal gyrus connectivity was associated with increased impulsivity 1 year later.

Altered resting-state functional connectivity in a thalamo-cortico-cerebellar network in patients with schizophrenia

The diagnosis of schizophrenia is associated with a complex psychopathology related to disrupted brain circuitry causing a failure in coordinating information across brain sites with no consensus regarding the mechanisms. Although schizophrenia is well-studied, the great majority of studies investigated pre-selected ROIs or Seed-based connectivity. Whole brain ROI-wise studies that consider all ROIs available simultaneously are lacking. This technique helps understand large- and local-scale dynamics of information exchange across the whole brain. We investigated ROI-wise whole brain networks in 35 participants diagnosed with schizophrenia and 41 control participants. To unveil dysfunctions in brain subnetworks and to characterize network topology, we applied a statistical tool specially developed for network comparison called network-based statistic and graph theory. We observed a hyperconnected thalamo-cortico-cerebellar subnetwork in participants with schizophrenia; nodal analysis revealed higher number of thalamic connections. Our results suggest disruptions at the local level of the subnetwork rather than globally spread across the brain and driven by hyperconnectivity. Importantly, this subnetwork emerged from an exploratory analysis directly comparing ROI-wise whole brain network. This fact makes it an important contribution to the field as additional evidence, demonstrating the high reliability of malfunction in the local thalamo-cortico-cerebellar network.

Enhancing prediction of human traits and behaviors through ensemble learning of traditional and novel resting-state fMRI connectivity analyses

Recent advances in cognitive neuroscience have focused on using resting-state functional connectivity (RSFC) data from fMRI scans to more accurately predict human traits and behaviors. Traditional approaches generally analyze RSFC by correlating averaged time-series data across regions of interest (ROIs) or networks, which may overlook important spatial signal patterns. To address this limitation, we introduced a novel linear regression technique that estimates RSFC by predicting spatial brain activity patterns in a target ROI from those in a seed ROI. We applied both traditional and our novel RSFC estimation methods to a large-scale dataset from the Human Connectome Project and the Brain Genomics Superstruct Project, analyzing resting-state fMRI data to predict sex, age, personality traits, and psychological task performance. To enhance prediction accuracy, we developed an ensemble learner that combines these qualitatively different methods using a weighted average approach. Our findings revealed that hierarchical clustering of RSFC patterns using our novel method displays distinct whole-brain grouping patterns compared to the traditional approach. Importantly, the ensemble model, integrating these diverse weak learners, outperformed the traditional RSFC method in predicting human traits and behaviors. Notably, the predictions from the traditional and novel methods showed relatively low similarity, indicating that our novel approach captures unique and previously undetected information about human traits and behaviors through fine-grained local spatial patterns of neural activation. These results highlight the potential of combining traditional and innovative RSFC analysis techniques to enrich our understanding of the neural basis of human traits and behaviors.

Harsh parenting, amygdala functional connectivity changes across childhood, and behavioral problems.

Harsh parenting in early childhood is related to offspring's adverse behavioral outcomes. Due to the scarcity of longitudinal neuroimaging data, few studies have explored the neurobiological underpinnings of this association, focusing on within-person variability. This study examined the temporal associations among harsh parenting, later behavioral problems, and the developmental trajectories of amygdala volume and amygdala resting-state functional connectivity (RSFC) profiles, using longitudinal neuroimaging data. The study was embedded in the Growing Up in Singapore Towards healthy Outcomes (GUSTO) cohort. T1-weighted (296 children, 642 scans) and resting-state functional scans (256 children, 509 scans) were collected at ages 4.5, 6, 7.5, and 10.5 years. Amygdala volume and RSFC between the amygdala and six brain regions that have leading roles in emotional regulation were extracted. Harsh parenting at 4.5 years and child behavioral problems at 10.5 years were assessed via parent-report questionnaires. Linear regression and linear mixed models were applied. Harsh parenting was associated with more severe externalizing problems in girls (β = 0.24, 95% CI 0.08-0.40) but not boys (pint = 0.07). In the overall sample, harsh parenting was associated with the developmental trajectories of amygdala-ACC, amygdala-OFC, and amygdala-DLPFC RSFC. In addition, the developmental trajectory of amygdala-ACC RSFC mediated the harsh parenting-externalizing problems association in girls (indirect effect = 0.06, 95% CI 0.01-0.14). Harsh parenting in early childhood was associated with amygdala neurocircuitry development and behavioral problems. The developmental trajectory of amygdala-ACC RSFC is a potential neural mechanism linking harsh parenting and externalizing problems in girls.

Longitudinal functional brain connectivity maturation in premature newborn infants: modulatory influence of early music enrichment

Abstract Premature birth affects brain maturation, illustrated by altered brain functional connectivity at term equivalent age (TEA) and alters neurobehavioral outcome. To correct early developmental differences and improve neurological outcome, music during the NICU stay has been proposed as an auditory enrichment with modulatory effects on functional and structural brain development, but longitudinal effects of such interventions have not been studied so far. We longitudinally investigated resting-state functional connectivity (RS-FC) maturation in preterm infants (n=43). Data-driven Independent Component Analyses (ICA) were performed on scans obtained at 33- and 40-weeks gestational age (GA), determining the presence of distinct RSNs. Connectome analysis ´accordance measure´ quantitively examined the RS-FC both at 33- and 40-weeks GA. Further comparing the internetwork RS-FC at 33- and 40 weeks GA provided a circuitry of interest (COI) for significant maturational changes in which the effects on the RS-FC of a music intervention was tested. The connectome analyses resulted in a COI of RS-FC connections significantly maturing from 33 to 40 weeks GA, namely between the thalamic/brainstem and prefrontal-limbic, salience, sensorimotor, auditory and prefrontal cortical networks; between the prefrontal-limbic and cerebellar, visual and left hemispheric precuneus networks; between the salience and visual, and cerebellar networks; and between the sensorimotor and auditory, and posterior cingulate/precuneus networks. The infants exposed to music exhibited significantly increased maturation in RS-FC between the thalamic/brainstem and salience networks, compared to controls. This study exemplifies that preterm infant RS-FC maturation is modulated through NICU music exposure, highlighting the importance of environmental enrichment for neurodevelopment in premature newborns.

Augmenting mindfulness training through neurofeedback: a pilot study of the pre-post changes on resting-state functional connectivity in typically developing adolescents.

Mindfulness training has been shown to promote positive mental health outcomes and related changes in neural networks such as the default mode network, which has a central node in the posterior cingulate cortex (PCC). Previous work from our group reported on the impact of a novel, neurofeedback augmented mindfulness training (NAMT) task on regulation of PCC hemodynamic activity in typically developing adolescents. The present pilot study aimed to expand on this finding by examining the pre-post changes of the NAMT task on resting-state functional connectivity of the PCC. Thirty-one typically developing adolescents (14.77 ± 1.23 years; 45% female) underwent a resting-state functional magnetic resonance imaging scan both before and after completing the NAMT task. A linear mixed effects model was used to assess for changes in functional connectivity of the PCC across the two resting-state runs. Data did not support the hypothesized decrease in connectivity between the PCC seed and other DMN regions from pre- to post-NAMT task. However, we observed a significant increase in functional connectivity between the PCC and a cluster encompassing the left hippocampus and amygdala following completion of the NAMT task (run 1 Fisher's Z = 0.16; run 2 Fisher's Z = 0.26). Although preliminary, this finding suggests NAMT has the potential to strengthen connectivity between default mode and salience regions. We speculate that such changed connectivity may facilitate enhanced self-referential and emotional processing in adolescents. https://clinicaltrials.gov/, identifier NCT04053582.

Tobacco Smoking Functional Networks: A Whole-Brain Connectome Analysis in 24,539 Individuals.

Nicotine addiction, a multifaceted neuropsychiatric disorder, profoundly impacts brain functions through interactions with neural pathways. Despite its significance, the impact of tobacco smoking on the whole-brain functional connectome remains largely unexplored. We conducted a whole-brain analysis on 24,539 adults aged 40 and above from the UK Biobank cohort. Subjects were categorized into individuals who use nicotine and who do not use nicotine based on current and chronic tobacco smoking information. Functional connectivity was assessed using resting-state functional magnetic resonance imaging (rfMRI). We employed a network analysis method to assess the systematic effects of tobacco smoking on brain connectome by identifying subnetworks that show nicotine-use-related differences. Our analyses revealed two nicotine-use-related subnetworks with distinct network structure (permutation p-value < 0.001). In the first network, there is a significant decrease in resting-state functional connectivity (rsFC) between the basal ganglia regions (e.g., nucleus accumbens) and 73% of the remaining brain regions, emphasizing the central hub role of basal ganglia in addictive smoking behaviors. Additionally, a data-driven subnetwork mainly involving regions from frontal and occipital lobes, showed reduced rsFC among individuals who use nicotine. The results suggest significant alterations in the communication and coordination among the basal ganglia and the broader network of brain regions. The observed changes in rsFC indicate a widespread disruption in the connectivity patterns associated with nicotine use. This study identifies rsFC subnetworks related to chronic nicotine use through whole-brain connectome analysis. The findings confirm that widespread alterations in rsFC are centered around hub nodes within the basal ganglia, including bilateral nucleus accumbens, putamen, caudate, and globus pallidus. In addition, our analysis found a clique-forming subnetwork vulnerable to tobacco smoking consisting of regions from the visual, dorsal/ventral attention, and frontoparietal networks.

Preventive treatment effects on brain structures and functions in patients with chronic migraine: A multimodel magnetic resonance imaging study.

Patients with chronic migraine (CM) often exhibit structural and functional alterations in pain-matrix regions, but it remains unclear how preventive treatment affects these changes. Therefore, this study aimed to investigate the structural and functional changes in pain-matrix regions in CM patients after 6-month treatment. A total of 24 patients with CM and 15 healthy controls were recruited for this study. Patients were divided into responder group (N = 9) and non-responder group (N = 15). After completing the Migraine Disability Assessment (MIDAS) questionnaire, all patients underwent whole-brain high-resolution T1-weighted images, diffusion-weighted imaging, and resting-state functional magnetic resonance imaging at baseline and 6-month follow-up. Whole brain gray matter volume and white matter diffusion indices were analyzed using voxel-based analysis. Structural and functional connectivity analyses were performed to understand brain changes in patients after 6-month preventive treatment. The responder group exhibited significantly higher MIDAS scores than the non-responder group at baseline, but no significant difference between the two groups at follow-up. No significant interval change was noted in gray matter volume, white matter diffusion indices, and structural connectivity in CM patients after 6-month treatment. Nonetheless, the functional connectivity was significantly increased between occipital, temporal lobes and cerebellum, and was significantly decreased between parietal and temporal lobes after 6-month preventive treatment. We concluded that resting-state functional connectivity was suitable for investigating the preventive treatment effect on CM patients.

Reduced resting-state functional connectivity of default mode network subsystems in patients with obsessive-compulsive disorder

ObjectivesNeuroimaging studies have reported extensive resting-state functional connectivity (rsFC) abnormalities in the default mode network (DMN) in patients with obsessive-compulsive disorder (OCD), but findings are inconsistent. DMN can be divided into three subsystems: core, dorsal medial prefrontal cortex (dMPFC), and medial temporal lobe (MTL). This study aimed to explore abnormalities in rsFC strength within and between DMN subsystems in OCD patients, and their relationship with clinical symptoms. MethodsThis study recruited 39 OCD patients and 45 healthy controls (HCs). OCD symptoms were assessed using the Yale-Brown Obsessive-Compulsive Scale (YBOCS). The seed-to-seed method was used to construct rsFC matrix. The rsFC strength within and between the three DMN subsystems were calculated. ResultsCompared to the HC group, the OCD group exhibited reduced rsFC strength within core subsystem (F = 7.799, p = 0.007, Bonferroni corrected p = 0.042). Further, this reduction was also observed in the unmedicated OCD group (n = 19), but not in the medicated OCD group (n = 18). In addition, rsFC strength within core subsystem was negatively correlated with the obsession subscale of YBOCS in the OCD group (r = −0.512, p = 0.004, Bonferroni corrected p = 0.008). Further, this correlation was also significant in the unmedicated OCD group, but not in the medicated OCD group. ConclusionsOur findings suggest that reduced rsFC strength within core subsystem is a feature of OCD patients and may serve as a potential biomarker of obsession severity. Moreover, pharmacological treatments may affect rsFC strength in DMN.

Exploring the impact of biological sex on intrinsic connectivity networks in PTSD: A data-driven approach

IntroductionSex as a biological variable (SABV) may help to account for the differential development and expression of post-traumatic stress disorder (PTSD) symptoms among trauma-exposed males and females. Here, we investigate the impact of SABV on PTSD-related neural alterations in resting-state functional connectivity (rsFC) within three core intrinsic connectivity networks (ICNs): the salience network (SN), central executive network (CEN), and default mode network (DMN). MethodsUsing an independent component analysis (ICA), we compared rsFC of the SN, CEN, and DMN between males and females, with and without PTSD (n = 47 females with PTSD, n = 34 males with PTSD, n = 36 healthy control females, n = 20 healthy control males) via full factorial ANCOVAs. Additionally, linear regression analyses were conducted with clinical variables (i.e., PTSD and depression symptoms, childhood trauma scores) in order to determine intrinsic network connectivity characteristics specific to SABV. Furthermore, we utilized machine learning classification models to predict the biological sex and PTSD diagnosis of individual participants based on intrinsic network activity patterns. ResultsOur findings revealed differential network connectivity patterns based on SABV and PTSD diagnosis. Males with PTSD exhibited increased intra-SN (i.e., SN-anterior insula) rsFC and increased DMN-right superior parietal lobule/precuneus/superior occipital gyrus rsFC as compared to females with PTSD. There were also differential network connectivity patterns for comparisons between the PTSD and healthy control groups for males and females, separately. We did not observe significant correlations between clinical measures of interest and brain region clusters which displayed significant between group differences as a function of biological sex, thus further reinforcing that SABV analyses are likely not confounded by these variables. Furthermore, machine learning classification models accurately predicted biological sex and PTSD diagnosis among novel/unseen participants based on ICN activation patterns. ConclusionThis study reveals groundbreaking insights surrounding the impact of SABV on PTSD-related ICN alterations using data-driven methods. Our discoveries contribute to further defining neurobiological markers of PTSD among females and males and may offer guidance for differential sex-related treatment needs.

Structural and functional correlates of olfactory reward processing in behavioral variant frontotemporal dementia

The behavioral variant of frontotemporal dementia (bvFTD) includes symptoms that reflect altered pursuit of rewards, including food, alcohol, and money. Little is known, however, about how these reward changes relate to atrophy and functional connectivity within reward-related regions. The goal of this study was to examine the structural and functional correlates of valence perception for olfactory rewards in 24 patients with bvFTD. Regression analysis of resting-state brain functional connectivity indicated that more positive valence ratings of olfactory stimuli were predicted by ventral pallidum connectivity to other reward circuit regions, particularly functional connectivity between ventral pallidum and bilateral anterior cingulate cortex/ventromedial prefrontal cortex. Structural analysis showed that atrophy of the anterior cingulate cortex was also significantly associated with perceiving stimuli as more rewarding. Finally, there was a significant interaction between ventral pallidum connectivity and atrophy of the anterior cingulate cortex. More specifically, the ventral pallidum connectivity had a greater effect on the positive perception of olfactory stimuli in the setting of low anterior cingulate cortex volume. These findings indicate that atrophy and functional connectivity within reward-relevant regions exert independent and interacting effects on the perception of pleasantness in bvFTD, potentially due to changes in hedonic “liking” signals.

Resting-state brain activation patterns and network topology distinguish human sign and goal trackers

The “Sign-tracker/Goal-tracker” (ST/GT) is an animal model of individual differences in learning and motivational processes attributable to distinctive conditioned responses to environmental cues. While GT rats value the reward-predictive cue as a mere predictor, ST rats attribute it with incentive salience, engaging in aberrant reward-seeking behaviors that mirror those of impulse control disorders. Given its potential clinical value, the present study aimed to map such model onto humans and investigated resting state functional magnetic resonance imaging correlates of individuals categorized as more disposed to sign-tracking or goal-tracking behavior. To do so, eye-tracking was used during a translationally informed Pavlovian paradigm to classify humans as STs (n = 36) GTs (n = 35) or as Intermediates (n = 33), depending on their eye-gaze towards the reward-predictive cue or the reward location. Using connectivity and network-based approach, measures of resting state functional connectivity and centrality (role of a node as a hub) replicated preclinical findings, suggesting a major involvement of subcortical areas in STs, and dominant cortical involvement in GTs. Overall, the study strengthens the translational value of the ST/GT model, with important implications for the early identification of vulnerable phenotypes for psychopathological conditions such as substance use disorder.

Cardiorespiratory Fitness and Sleep, but not Physical Activity, are Associated with Functional Connectivity in Older Adults.

Aging results in changes in resting state functional connectivity within key networks associated with cognition. Cardiovascular function, physical activity, sleep, and body composition may influence these age-related changes in the brain. Better understanding these associations may help clarify mechanisms related to brain aging and guide interventional strategies to reduce these changes. In a large (n = 398) sample of healthy community dwelling older adults that were part of a larger interventional trial, we conducted cross sectional analyses of baseline data to examine the relationships between several modifiable behaviors and resting state functional connectivity within networks associated with cognition and emotional regulation. Additionally, maximal aerobic capacity, physical activity, quality of sleep, and body composition were assessed. Associations were explored both through correlation and best vs. worst group comparisons. Greater cardiovascular fitness, but not larger quantity of daily physical activity, was associated with greater functional connectivity within the Default Mode (p = 0.008 r = 0.142) and Salience Networks (p = 0.005, r = 0.152). Better sleep (greater efficiency and fewer nighttime awakenings) was also associated with greater functional connectivity within multiple networks including the Default Mode, Executive Control, and Salience Networks. When the population was split into quartiles, the highest body fat group displayed higher functional connectivity in the Dorsal Attentional Network compared to the lowest body fat percentage (p = 0.011; 95% CI - 0.0172 to - 0.0023). These findings confirm and expand on previous work indicating that, in older adults, higher levels of cardiovascular fitness and better sleep quality, but not greater quantity of physical activity, total sleep time, or lower body fat percentage are associated with increased functional connectivity within key resting state networks.

Brain networks in newborns and infants with and without sensorineural hearing loss: A functional near-infrared spectroscopy study.

Understanding the impact of early sensory deficits on brain development is essential for understanding developmental processes and developing potential interventions. While previous studies have looked into the impact of prenatal experiences on language development, there is a lack of research on how these experiences affect early language and brain function development in individuals with sensorineural hearing loss (SNHL). To investigate SNHL effects on early brain development and connectivity in 4-month-olds vs healthy newborns and controls. The research involved analyzing the functional brain networks of 65 infants, categorized into three groups: 28 healthy newborns, 22 4-month-old participants with SNHL, and 15 age-matched healthy participants. The resting-state functional connectivity was measured and compared between the groups using functional near-infrared spectroscopy and graph theory to assess the brain network properties. Significant differences were found in resting-state functional connectivity between participants with SNHL and age-matched controls, indicating a developmental lag in brain connectivity for those with SNHL. Surprisingly, SNHL participants showed better connectivity development compared to healthy newborns, with connectivity strengths of 0.13 ± 0.04 for SNHL, 0.16 ± 0.08 for controls, and 0.098 ± 0.04 for newborns. Graph theory analysis revealed enhanced global brain network properties for the SNHL group, suggesting higher communication efficiency at 4 months. No significant differences were noted in network properties between 4-month-old SNHL participants and neonates. A unique pattern of central hubs was observed in the SNHL group, with 2 hubs in the left hemisphere compared to 6 in controls. 4-month-old infants with SNHL have a distinct brain network pattern with efficient long-distance information transmission but less effective local communication compared to age-matched controls.

Functional Localization of the Human Auditory and Visual Thalamus Using a Thalamic Localizer Functional Magnetic Resonance Imaging Task.

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

Enhanced ADHD classification through deep learning and dynamic resting state fMRI analysis.

Attention Deficit Hyperactivity Disorder (ADHD) is characterized by deficits in attention, hyperactivity, and/or impulsivity. Resting-state functional connectivity analysis has emerged as a promising approach for ADHD classification using resting-state functional magnetic resonance imaging (rs-fMRI), although with limited accuracy. Recent studies have highlighted dynamic changes in functional connectivity patterns among ADHD children. In this study, we introduce Skip-Vote-Net, a novel deep learning-based network designed for classifying ADHD from typically developing children (TDC) by leveraging dynamic connectivity analysis on rs-fMRI data collected from 222 participants included in the NYU dataset within the ADHD-200 database. Initially, for each subject, functional connectivity matrices were constructed from overlapping segments using Pearson's correlation between mean time series of 116 regions of interest defined by the Automated Anatomical Labeling (AAL) 116 atlas. Skip-Vote-Net was then developed, employing a majority voting mechanism to classify ADHD/TDC children, as well as distinguishing between the two main subtypes: the inattentive subtype (ADHDI) and the predominantly combined subtype (ADHDC). The proposed method was evaluated across four classification scenarios: (1) two-class classification of ADHD from TD children using balanced data, (2) two-class classification between ADHD and TD children using unbalanced data, (3) two-class classification between ADHDI and ADHDC, and (4) three-class classification among ADHDI, ADHDC, and TD children. Using Skip-Vote-Net, we achieved mean classification accuracies of 97% ± 1.87 and 97.7% ± 2.2 for the balanced and unbalanced classification cases, respectively. Furthermore, the mean classification accuracy for discriminating between ADHDI and ADHDC reached 99.4% ± 1.21. Finally, the proposed method demonstrated an average accuracy of 98.86% ± 1.03 in classifying ADHDI, ADHDC, and TD children collectively. Our findings highlight the superior performance of Skip-Vote-Net over existing methods in the classification of ADHD, showcasing its potential as an effective diagnostic tool for identifying ADHD subtypes and distinguishing ADHD from typically developing children.

The altered resting-state functional connectivity of thalamic subregions in patients with globus pharyngeus.

Globus Pharyngeus (GP) is a somatic symptom that accompanies mood distress. Although the etiology of GP remains unclear, its specific symptom of a false lump sensation in the throat without physical obstruction raises the possibility of alterations in brain networks responsible for somatosensory and emotion processing in patients with GP. To address this possibility, we investigated resting-state functional connectivity (rsFC) in 31 patients with GP and 24 healthy individuals using resting-state functional magnetic resonance imaging. Considering its significance in somatosensory and emotional functions, we focused on rsFC in the subregions of the thalamus. We found significantly decreased rsFC between the right caudal temporal thalamus (rcTtha) and the midcingulate cortex (MCC) as well as significantly decreased rsFC between the right rostral temporal thalamus (rrTtha) and the left cerebellum in the patients with GP. Additionally, within the patient group, the abnormalities in rsFC between the rcTtha and the MCC were correlated with the severity of somatization symptoms but not with depression and anxiety. These findings suggest alterations in the rsFC of thalamic subregions in patients with GP, shedding light on the pathogenesis of GP and potentially leading to improved diagnosis and treatment approaches for the condition.

Multi-omics insights into the microbiota-gut-brain axis and cognitive improvement post-bariatric surgery

BackgroundAlthough numerous studies have shown that bariatric surgery results in sustained weight loss and modifications in gut microbiota composition and cognitive function, the exact underlying mechanisms are unclear. This study aimed to investigate the effects of bariatric surgery on cognitive function through the microbiota-gut-brain axis (MGBA).MethodsDemographic data, serum samples, fecal samples, cognitive assessment scales, and resting-state functional connectivity magnetic resonance imaging (rs-fMRI) scans were obtained from 39 obese patients before and after (6 months) laparoscopic sleeve gastrectomy (LSG). PCA analysis, OPLS-DA analysis, and permutation tests were used to conduct fecal 16 S microbiota profiling, serum metabolomics, and neuroimaging analyses, and a bariatric surgery-specific rs-fMRI brain functional connectivity network was constructed. Spearman correlation analysis and Co-inertia analysis were employed to correlate significant alterations in cognitive assessment scales and resting-state functional connectivity difference networks with differential serum metabolites and 16 S microbiota data to identify key gut microbiota and serum metabolic factors.ResultsLSG significantly reduced the weight of obese patients, with reductions of up to 28%. Furthermore, cognitive assessment scale measurements revealed that LSG enhanced cognitive functions, including memory (HVLT, p = 0.000) and executive function (SCWT, p = 0.008). Also, LSG significantly altered gut microbiota composition (p = 0.001), with increased microbial abundance and diversity (p < 0.05). Moreover, serum metabolite levels were significantly altered, revealing intergroup differences in 229 metabolites mapped to 72 metabolic pathways (p < 0.05, VIP > 1). Spearman correlation analysis among cognitive assessment scales, gut microbiota species, and serum metabolites revealed correlations with 68 gut microbiota species and 138 serum metabolites (p < 0.05). Furthermore, pairwise correlations were detected between gut microbiota and serum metabolites (p < 0.05). Functional neuroimaging analysis revealed that LSG increased functional connectivity in cognitive-related frontotemporal networks (FPN, p < 0.01). Additionally, normalization of the default mode network (DMN) and salience network (SN) connectivity was observed after LSG (p < 0.001). Further canonical correlation and correlation analysis suggested that the cognitive-related brain network changes induced by LSG were associated with key gut microbiota species (Akkermansia, Blautia, Collinsella, Phascolarctobacterium, and Ruminococcus, p < 0.05) and neuroactive metabolites (Glycine, L-Serine, DL-Dopa, SM (d18:1/24:1(15Z), p < 0.05).ConclusionThese findings indicate the pathophysiological role of the microbiota-gut-brain axis in enhancing cognitive function after bariatric surgery, and the study provides a basis for clinical dietary adjustments, probiotic supplementation, and guidance for bariatric surgery, but further research is still needed.Trial registrationChinese Clinical Trial Registry, ChiCTR2100049403. Registered 02 August 2021, https://www.chictr.org.cn/.

Changes in thalamic functional connectivity in post-Covid patients with and without fatigue

BackgroundFunctional brain alterations in post-Covid-19 condition have been minimally explored to date. Here, we investigate differences in resting-state thalamic functional connectivity among post-Covid patients with and without fatigue, alongside structural brain changes and cognition. MethodsThirty-nine post-Covid patients (n = 15 fatigued, n = 24 non-fatigued) participated in our study, undergoing comprehensive cognitive assessments, as well as functional and structural neuroimaging. We conducted a seed-based functional connectivity analysis using the thalamus as a seed region, exploring its connectivity with the entire brain. To further elucidate our findings, correlation analyses were performed using the functional coupling between the thalamus and regions showing different connectivity between the two patient groups. ResultsOur results reveal that patients experiencing fatigue exhibit anti-correlated functional coupling between the thalamus and motor-associated regions, including the motor cortex (M1), supplementary motor area (SMA), and anterior cingulate cortex (ACC), compared to non-fatigued patients, who are showing positive functional coupling. Furthermore, this observed coupling was found to correlate with both the fatigue scores obtained from a fatigue questionnaire and performance on the Trail Making Test, Part A, which represents a measure of processing speed. ConclusionsOur study highlights significant differences in resting-state functional connectivity between post-Covid patients with and without fatigue, particularly within motor-associated brain regions. These findings suggest a potential neural mechanism underlying post-Covid fatigue and underscore the importance of considering both functional and structural brain changes in understanding the symptomatic sequelae of post-Covid-19 condition. Further research is warranted to provide insight into the longitudinal trajectories of these neural alterations.