Frequent absent mindedness and the neural mechanism trapped by mobile phone addiction.

INTRODUCTION: 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. METHOD: The current study aimed to investigate the neural mechanism between mobile phone use and mind wandering. 459 university students (averaged age, 19.26 years) 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. RESULTS: Students with high mobile phone addiction scores were more likely to have high mind wandering scores. Functional connectivity among the default mode, motor, frontoparietal, basal ganglia, limbic, medial frontal, visual association, and cerebellar networks formed the neural basis of mind wandering. Functional connectivity 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. CONCLUSION: The findings of this study 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.

Comparative analysis of rs-fMRI markers in heat and mechanical pain sensitivity.

This study investigates the comparative analysis of resting-state functional magnetic imaging (rs-fMRI) markers in heat and mechanical pain sensitivity among healthy adults. Using quantitative sensory testing (QST) in the orofacial area and rs-fMRI, we explored the relationship between pain sensitivities and resting-state functional connectivity (rsFC) across whole brain areas. Brain regions were spatially divided using group independent component analysis (gICA), and additional masked gICA was performed for brainstem regions. Our findings revealed that a significant number of rsFCs were correlated with either heat or mechanical pain sensitivity, with a substantial portion originating from the Sensorimotor Network (SMN). Furthermore, multivariable regression models incorporating rsFC features demonstrated predictive capabilities for pain sensitivities, with the inclusion of brainstem gICA components significantly enhancing model accuracy. Finally, a composite critical rsFC value was introduced to simplify and describe overall abnormal communication in the brain network, which could also be used in univariable regression models to predict heat and mechanical pain sensitivity.

Reorganization of integration and segregation networks in brain-based visual impairment.

Growing evidence suggests that cerebral connectivity changes its network organization by altering modular topology in response to developmental and environmental experience. However, changes in cerebral connectivity associated with visual impairment due to early neurological injury are still not fully understood. Cerebral visual impairment (CVI) is a brain-based visual disorder associated with damage and maldevelopment of retrochiasmal pathways and areas implicated in visual processing. In this study, we used a multimodal imaging approach and connectomic analyses based on structural (voxel-based morphometry; VBM) and resting state functional connectivity (rsfc) to investigate differences in weighted degree and link-level connectivity in individuals with CVI compared to controls with neurotypical development. We found that participants with CVI showed significantly reduced grey matter volume within the primary visual cortex and intraparietal sulcus (IPS) compared to controls. Participants with CVI also exhibited marked reorganization characterized by increased integration of visual connectivity to somatosensory and multimodal integration areas (dorsal and ventral attention regions) and lower connectivity from visual to limbic and default mode networks. Link-level functional changes in CVI were also associated with key clinical outcomes related to visual function and development. These findings provide early insight into how visual impairment related to early brain injury distinctly reorganizes the functional network architecture of the human brain.

Immunomodulatory treatment may change functional and structural brain imaging in severe mental disorders.

Neuroinflammation has been implicated in the pathophysiology of schizophrenia and obsessive-compulsive disorder (OCD) and deviations in brain structure and connectivity are seen in these disorders. Here, we explore the effects of a potent immunomodulatory treatment on neuroimaging. In a pilot study of rituximab treatment in schizophrenia and OCD, a subgroup (n = 13) underwent structural and functional magnetic resonance imaging before and 5 months after treatment, to study longitudinal changes in resting-state functional connectivity (rsFC) and voxel-based morphometry (VBM). A hypothesis-free exploratory whole-brain analysis was performed twice to assess changes in rsFC, using anterior cingulate cortex, anterior insula, posterior insula and nucleus accumbens as seed regions. There were significant interactions (diagnosis x time) in connectivity between right posterior insula and two clusters encompassing basal ganglia and anterior frontal pole, and between left anterior insula and a cluster in basal ganglia, where connectivity decreased in OCD and increased in schizophrenia. The increase of connectivity after rituximab, between left anterior insula and parts of cerebellum and lingual gyrus and between left posterior insula and parts of cerebellum, correlated with improved global psychosocial functioning according to the Personal and Social Performance Scale, especially in schizophrenia. VBM analysis identified two clusters with increased grey matter volumes (GMV) after rituximab, one in right insula overlapping one of the seed regions with significant rsFC changes. This pilot study implies that rituximab may influence both brain structure and connectivity and that GMV changes and rsFC changes are regionally associated.

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

OBJECTIVES: Neuroimaging 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. METHODS: This 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. RESULTS: Compared 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. CONCLUSIONS: Our 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.

Support vector machine classification of irritable bowel syndrome patients based on whole-brain resting-state functional connectivity features.

Background: Irritable bowel syndrome (IBS) is a disorder characterized by signaling dysregulation between the brain and gut, leading to gastrointestinal dysfunction. Symptoms such as abdominal pain and constipation can manifest periodically or persistently, and negative emotions may exacerbate the symptoms. Previous studies have shown that the pathogenesis of IBS is closely related to the brain-gut axis and brain function, but there are still difficulties in disease diagnosis. Therefore, this study applied a machine-learning approach based on resting-state functional magnetic resonance imaging (rs-fMRI) whole-brain functional connectivity (FC) to distinguish IBS patients from healthy controls (HCs). Methods: A total of 176 subjects, comprising 88 consecutive patients with IBS and 88 age-, sex- and education-matched HCs, were enrolled in this study between January 2020 and January 2024 at the First Affiliated Hospital of Shantou University Medical College. All the subjects underwent rs-fMRI and high-resolution anatomical T1-weighted imaging (T1WI) examinations. Following the preprocessing of the rs-fMRI image data, FC matrices between all regions of interest (ROIs) were extracted using automated anatomical labeling (AAL). Subsequently, supervised machine learning was performed using whole-brain FC for classification features to identify the best-performing model. Finally, weights of the optimal model's features were exported to confirm the neuroanatomical regions significantly influencing model establishment. Results: Compared with other supervised learning models, the support vector machine (SVM) model had significantly higher classification accuracy and performed significantly better than the other models (P<0.05) with a classification accuracy of 75% and an area under the curve (AUC) of 0.7788 (95% confidence interval [CI]: 0.6861-0.8715) (P<0.01). In addition, the FC features from the Rolandic operculum (ROL) to the anterior cingulate gyrus (ACG), the calcarine sulcus (CAL) to the triangular part of the inferior frontal gyrus (IFG), the gyrus rectus (REC) to the inferior occipital gyrus (IOG), the lingual gyrus (LING) to the putamen (PUT), and the IOG to the angular gyrus (ANG) were relatively important in the construction of the machine-learning models. Conclusions: The SVM was the optimal machine-learning model for effectively classifying IBS patients and HCs based on whole-brain resting-state FC matrices. The FC features between the emotion-related brain regions significantly affected the construction of the machine-learning models. As a classification feature in machine learning, whole-brain resting-state FC holds the potential to achieve precision medicine in IBS and enhance disease diagnostic efficacy.

Comparing different motion correction approaches for resting-state functional connectivity analysis with functional near-infrared spectroscopy data.

Significance: Motion artifacts are a notorious challenge in the functional near-infrared spectroscopy (fNIRS) field. However, little is known about how to deal with them in resting-state data. Aim: We assessed the impact of motion artifact correction approaches on assessing functional connectivity, using semi-simulated datasets with different percentages and types of motion artifact contamination. Approach: Thirty-five healthy adults underwent a 15-min resting-state acquisition. Semi-simulated datasets were generated by adding spike-like and/or baseline-shift motion artifacts to the real dataset. Fifteen pipelines, employing various correction approaches, were applied to each dataset, and the group correlation matrix was computed. Three metrics were used to test the performance of each approach. Results: When motion artifact contamination was low, various correction approaches were effective. However, with increased contamination, only a few pipelines were reliable. For datasets mostly free of baseline-shift artifacts, discarding contaminated frames after pre-processing was optimal. Conversely, when both spike and baseline-shift artifacts were present, discarding contaminated frames before pre-processing yielded the best results. Conclusions: This study emphasizes the need for customized motion correction approaches as the effectiveness varies with the specific type and amount of motion artifacts present.

Abnormal resting-state functional connectivity of hippocampal subregions in type 2 diabetes mellitus-associated cognitive decline.

Objective: Type 2 diabetes mellitus (T2DM) over time predisposes to inflammatory responses and abnormalities in functional brain networks that damage learning, memory, or executive function. The hippocampus is a key region often reporting connectivity abnormalities in memory disorders. Here, we investigated peripheral inflammatory responses and resting-state functional connectivity (RSFC) changes characterized of hippocampal subregions in type 2 diabetes-associated cognitive decline (T2DACD). Methods: The study included 16 patients with T2DM, 16 patients with T2DACD and 25 healthy controls (HCs). Subjects were assessed for cognitive performance, tested for the expression of inflammatory factors IL-6, IL-10 and TNF-α in peripheral serum, underwent resting-state functional magnetic resonance imaging scans, and analyzed for RSFC using the hippocampal subregions as seeds. We also calculated the correlation between cognitive performance and RSFC of hippocampal subregion, and analyzed the significantly altered RSFC values of T2DACD for Receiver Operating Characteristic (ROC) analysis. Results: T2DACD patients showed a decline in their ability to complete cognitive assessment scales and experimental paradigms, and T2DM did not show abnormal cognitive performance. IL-6 expression was increased in peripheral serum in both T2DACD and T2DM. Compared with HCs, T2DACD showed abnormalities RSFC of the left anterior hippocampus with left precentral gyrus and left angular gyrus. T2DM showed abnormalities RSFC of the left middle hippocampus with right medial frontal gyrus, right anterior and middle hippocampus with left precuneus, left anterior hippocampus with right precuneus and right posterior middle temporal gyrus. Compared with T2DM, T2DACD showed abnormalities RSFC of the left posterior hippocampus and right middle hippocampus with left precuneus. In addition, RSFC in the left posterior hippocampus with left precuneus of T2DACD was positively correlated with Flanker conflict response time (r=0.766, P=0.001). In the ROC analysis, the significantly altered RSFC values of T2DACD achieved significant performance. Conclusions: T2DACD showed a significant decrease in attentional inhibition and working memory, peripheral pro-inflammatory response increased, and abnormalities RSFC of the hippocampal subregions with default mode network and sensory-motor network. T2DM did not show a significant cognitive decline, but peripheral pro-inflammatory response increased and abnormalities RSFC of the hippocampus subregions occurred in the brain. In addition, the left precuneus may be a key brain region in the conversion of T2DM to T2DACD. The results of this study may provide a basis for the preliminary diagnosis of T2DACD.

Functional Brain Network of Trait Impulsivity: Whole-Brain Functional Connectivity Predicts Self-Reported Impulsivity.

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.

The "limbic network," comprising orbitofrontal and anterior temporal cortex, is part of an extended default network: Evidence from multi-echo fMRI.

Resting-state functional magnetic resonance imaging (fMRI) investigations have provided a view of the default network (DN) as composed of a specific set of frontal, parietal, and temporal cortical regions. This spatial topography is typically defined with reference to an influential network parcellation scheme that designated the DN as one of seven large-scale networks (Yeo et al., 2011). However, the precise functional organization of the DN is still under debate, with studies arguing for varying subnetwork configurations and the inclusion of subcortical regions. In this vein, the so-called limbic network-defined as a distinct large-scale network comprising the bilateral temporal poles, ventral anterior temporal lobes, and orbitofrontal cortex-is of particular interest. A large multi-modal and multi-species literature on the anatomical, functional, and cognitive properties of these regions suggests a close relationship to the DN. Notably, these regions have poor signal quality with conventional fMRI acquisition, likely obscuring their network affiliation in most studies. Here, we leverage a multi-echo fMRI dataset with high temporal signal-to-noise and whole-brain coverage, including orbitofrontal and anterior temporal regions, to examine the large-scale network resting-state functional connectivity of these regions and assess their associations with the DN. Consistent with our hypotheses, our results support the inclusion of the majority of the orbitofrontal and anterior temporal cortex as part of the DN and reveal significant heterogeneity in their functional connectivity. We observed that left-lateralized regions within the temporal poles and ventral anterior temporal lobes, as well as medial orbitofrontal regions, exhibited the greatest resting-state functional connectivity with the DN, with heterogeneity across DN subnetworks. Overall, our findings suggest that, rather than being a functionally distinct network, the orbitofrontal and anterior temporal regions comprise part of a larger, extended default network.

The precise functional organization of the default network is still under debate. Limitations in temporal signal-to-noise of functional MRI BOLD signal data may have restricted estimations of the topography of the default network. The "limbic network," defined as a distinct large-scale network comprising bilateral anterior temporal and orbitofrontal cortex, has been affiliated with the default network in nonhuman animal tractography and task-based fMRI studies in humans. We leverage a multi-echo fMRI dataset with high temporal signal-to-noise and whole-brain coverage to examine the large-scale network resting-state functional connectivity of these regions and assess their associations with the default network. Our results support the inclusion of anterior temporal and orbitofrontal cortex as part of the default network. Overall, our findings suggest that, rather than being a functionally distinct limbic network, the anterior temporal and orbitofrontal regions comprise part of an extended default network.

Disruptive and complementary effects of depression symptoms on spontaneous brain activity in the subcortical vascular mild cognitive impairment.

Background: Although depression symptoms are commonly reported in patients with subcortical vascular mild cognitive impairment (svMCI), their impact on brain functions remains largely unknown, with diagnoses mainly dependent on behavioral assessments. Methods: In this study, we analyzed resting-state fMRI data from a cohort of 34 svMCI patients, comprising 18 patients with depression symptoms (svMCI+D) and 16 patients without (svMCI-D), along with 34 normal controls (NC). The study used the fraction of the amplitude of low-frequency fluctuations (fALFF), resting-state functional connectivity, correlation analyses, and support vector machine (SVM) techniques. Results: The fALFF of the right cerebellum (CERE.R) differed among the svMCI+D, svMCI-D, and NC groups. Specifically, the regional mean fALFF of CERE. R was lower in svMCI-D patients compared to NC but higher in svMCI+D patients compared to svMCI-D patients. Moreover, the adjusted fALFF of CERE. R showed a significant correlation with Montreal Cognitive Assessment (MOCA) scores in svMCI-D patients. The fALFF of the right orbital part of the superior frontal gyrus was significantly correlated with Hamilton Depression Scale scores in svMCI+D patients, whereas the fALFF of the right postcingulate cortex (PCC.R) showed a significant correlation with MOCA scores in svMCI-D patients. Furthermore, RSFC between PCC. R and right precuneus, as well as between CERE. R and the right lingual gyrus (LING.R), was significantly reduced in svMCI-D patients compared to NC. In regional analyses, the adjusted RSFC between PCC. R and PreCUN. R, as well as between CERE. R and LING. R, was decreased in svMCI-D patients compared to NC but increased in svMCI+D patients compared to svMCI-D. Further SVM analyses achieved good performances, with an area under the curve (AUC) of 0.82 for classifying svMCI+D, svMCI-D, and NC; 0.96 for classifying svMCI+D and svMCI-D; 0.82 for classifying svMCI+D and NC; and 0.92 for classifying svMCI-D and NC. Conclusion: The study revealed disruptive effects of cognitive impairment, along with both disruptive and complementary effects of depression symptoms on spontaneous brain activity in svMCI. Moreover, these findings suggest that the identified features might serve as potential biomarkers for distinguishing between svMCI+D, svMCI-D, and NC, thereby guiding clinical treatments such as transcranial magnetic stimulation for svMCI.

Hypothalamic connectivities and self-evaluated aggression in young adults.

Introduction: The hypothalamus plays a pivotal role in supporting motivated behavior, including aggression. Previous work suggested differential roles of the medial hypothalamus (MH) and lateral hypothalamus (LH) in aggressive behaviors, but little is known about how their resting-state functional connectivity (rsFC) may relate to aggression in humans. Methods: We employed the data from the Human Connectome Project (HCP) and examined the rsFC's of LH and MH in 745 young adults (393 women). We also explored sex differences in the rsFC's. We processed the imaging data with published routines and evaluated the results of voxel-wise regression on aggression score, as obtained from Achenbach Adult Self Report, with a corrected threshold. Results: The analysis revealed significant rsFC between the LH and clusters in the middle temporal and occipital gyri across all subjects and in the thalamus for men, both in negative correlation with aggression score. Slope test confirmed sex differences in the correlation between LH-thalamus rsFC and aggression score. No significant rsFC was observed for MH. Conclusions: These findings suggest a role of LH rsFCs and sex differences in LH-thalamus rsFC in the manifestation of aggression in humans. The findings highlight the need for further research into sex-specific neural pathways in aggression and other related behavioral traits of importance to mental health.

The degenerate coding of psychometric profiles through functional connectivity archetypes.

Introduction: Degeneracy in the brain-behavior code refers to the brain's ability to utilize different neural configurations to support similar functions, reflecting its adaptability and robustness. This study aims to explore degeneracy by investigating the non-linear associations between psychometric profiles and resting-state functional connectivity (RSFC). Methods: The study analyzed RSFC data from 500 subjects to uncover the underlying neural configurations associated with various psychometric outcomes. Self-organized maps (SOM), a type of unsupervised machine learning algorithm, were employed to cluster the RSFC data. And identify distinct archetypal connectivity profiles characterized by unique within- and between-network connectivity patterns. Results: The clustering analysis using SOM revealed several distinct archetypal connectivity profiles within the RSFC data. Each archetype exhibited unique connectivity patterns that correlated with various cognitive, physical, and socioemotional outcomes. Notably, the interaction between different SOM dimensions was significantly associated with specific psychometric profiles. Discussion: This study underscores the complexity of brain-behavior interactions and the brain's capacity for degeneracy, where different neural configurations can lead to similar behavioral outcomes. These findings highlight the existence of multiple brain architectures capable of producing similar behavioral outcomes, illustrating the concept of neural degeneracy, and advance our understanding of neural degeneracy and its implications for cognitive and emotional health.

Transcranial direct current stimulation and neuronal functional connectivity in MCI: role of individual factors associated to AD.

Background: Alzheimer's disease (AD) encompasses a spectrum that may progress from mild cognitive impairment (MCI) to full dementia, characterized by amyloid-beta and tau accumulation. Transcranial direct current stimulation (tDCS) is being investigated as a therapeutic option, but its efficacy in relation to individual genetic and biological risk factors remains underexplored. Objective: To evaluate the effects of a two-week anodal tDCS regimen on the left dorsolateral prefrontal cortex, focusing on functional connectivity changes in neural networks in MCI patients resulting from various possible underlying disorders, considering individual factors associated to AD such as amyloid-beta deposition, APOE ϵ4 allele, BDNF Val66Met polymorphism, and sex. Methods: In a single-arm prospective study, 63 patients with MCI, including both amyloid-PET positive and negative cases, received 10 sessions of tDCS. We assessed intra- and inter-network functional connectivity (FC) using fMRI and analyzed interactions between tDCS effects and individual factors associated to AD. Results: tDCS significantly enhanced intra-network FC within the Salience Network (SN) and inter-network FC between the Central Executive Network and SN, predominantly in APOE ϵ4 carriers. We also observed significant sex*tDCS interactions that benefited inter-network FC among females. Furthermore, the effects of multiple modifiers, particularly the interaction of the BDNF Val66Met polymorphism and sex, were evident, as demonstrated by increased intra-network FC of the SN in female Met non-carriers. Lastly, the effects of tDCS on FC did not differ between the group of 26 MCI patients with cerebral amyloid-beta deposition detected by flutemetamol PET and the group of 37 MCI patients without cerebral amyloid-beta deposition. Conclusions: The study highlights the importance of precision medicine in tDCS applications for MCI, suggesting that individual genetic and biological profiles significantly influence therapeutic outcomes. Tailoring interventions based on these profiles may optimize treatment efficacy in early stages of AD.

Drinking severity mediates the relationship between hypothalamic connectivity and rule-breaking/intrusive behavior differently in young women and men: an exploratory study.

Background: The hypothalamus is a key hub of the neural circuits of motivated behavior. Alcohol misuse may lead to hypothalamic dysfunction. Here, we investigated how resting-state hypothalamic functional connectivities are altered in association with the severity of drinking and clinical comorbidities and how men and women differ in this association. Methods: We employed the data of the Human Connectome Project. A total of 870 subjects were included in data analyses. The severity of alcohol use was quantified for individual subjects with the first principal component (PC1) identified from principal component analyses of all drinking measures. Rule-breaking and intrusive scores were evaluated with the Achenbach Adult Self-Report Scale. We performed a whole-brain regression of hypothalamic connectivities on drinking PC1 in all subjects and men/women separately and evaluated the results at a corrected threshold. Results: Higher drinking PC1 was associated with greater hypothalamic connectivity with the paracentral lobule (PCL). Hypothalamic PCL connectivity was positively correlated with rule-breaking score in men (r=0.152, P=0.002) but not in women. In women but not men, hypothalamic connectivity with the left temporo-parietal junction (LTPJ) was negatively correlated with drinking PC1 (r=-0.246, P<0.001) and with intrusiveness score (r=-0.127, P=0.006). Mediation analyses showed that drinking PC1 mediated the relationship between hypothalamic PCL connectivity and rule-breaking score in men and between hypothalamic LTPJ connectivity and intrusiveness score bidirectionally in women. Conclusions: We characterized sex-specific hypothalamic connectivities in link with the severity of alcohol misuse and its comorbidities. These findings extend the literature by elucidating the potential impact of problem drinking on the motivation circuits.

Shared and distinct patterns of default mode network dysfunction in major depressive disorder and bipolar disorder: A comparative meta-analysis.

BACKGROUND: While patients with major depressive disorder (MDD) and bipolar disorder (BD) exhibited default mode network (DMN) dysfunction revealed by aberrant resting-state functional connectivity (rsFC) patterns, previous findings have been inconsistent. Little is known about the similarities and differences in DMN rsFC between MDD and BD. METHODS: A voxel-wise meta-analysis of seed-based DMN rsFC studies on MDD or BD was performed using the Seed-based d Mapping software with permutation of subject images (SDM-PSI). Aberrant DMN rsFC in both disorders was investigated separately, followed by conjunction and between-disorder comparison analyses. Functional decoding was performed to implicate the psychophysiological underpinnings of derived brain abnormalities. RESULTS: Thirty-four studies comparing 1316 MDD patients with 1327 HC, and 22 studies comparing 1059 BD patients with 1396 HC were included. Compared to HC, MDD patients exhibited DMN hyperconnectivity with frontolimbic systems, and hypoconnectivity with temporal lobe and posterior cingulate cortex. BD patients displayed increased DMN connectivity with bilateral precuneus, and reduced connectivity with prefrontal cortex and middle temporal gyrus. No common patterns of DMN rsFC abnormalities were observed between MDD and BD. Compared to BD, MDD patients showed DMN hyperconnectivity with triangular part of the left inferior frontal gyrus and left fusiform gyrus. Functional decoding found that patterns of DMN rsFC alteration between MDD and BD were primarily related to action and perception domains. CONCLUSION: Distinct DMN dysfunction patterns in MDD and BD enhance current understanding of the neural substrates of mood disorders and may provide a potential biomarker for differentiation.

Association of bacterial overgrowth in the small intestine with cortical thickness and functional connectivity in Parkinson's disease involving mild cognitive impairment.

This study explored potential associations of bacterial overgrowth in the small intestine, as detected based on levels of hydrogen and methane in breath after lactulose consumption, with cortical thickness and resting-state functional connectivity in different brain regions. Prospective comparison of 35 patients with Parkinson's disease (PD) involving mild cognitive impairment, 35 patients with PD with normal cognitive function and 17 healthy controls showed the largest level of hydrogen alone and the largest combined level of hydrogen and methane in patients with mild cognitive impairment. The comparison also revealed a significant negative correlation between those levels and thickness of the right insular cortex. Mild cognitive patients showed different functional connectivity between the right insula and cognition-related brain networks from normal cognitive patients. Our results suggest that bacterial overgrowth in the small intestine may contribute to cortical thinning and alterations in resting-state functional connectivity in PD involving mild cognitive impairment. These insights support and deepen previous observations implicating the gut-brain axis in the neurological disorder.

Morning resting hypothalamus-dorsal striatum connectivity predicts individual differences in diurnal sleepiness accumulation.

While the significance of obtaining restful sleep at night and maintaining daytime alertness is well recognized for human performance and overall well-being, substantial variations exist in the development of sleepiness during diurnal waking periods. Despite the established roles of the hypothalamus and striatum in sleep-wake regulation, the specific contributions of this neural circuit in regulating individual sleep homeostasis remain elusive. This study utilized resting-state functional magnetic resonance imaging (fMRI) and mathematical modeling to investigate the role of hypothalamus-striatum connectivity in subjective sleepiness variation in a cohort of 71 healthy adults under strictly controlled in-laboratory conditions. Mathematical modeling results revealed remarkable individual differences in subjective sleepiness accumulation patterns measured by the Karolinska Sleepiness Scale (KSS). Brain imaging data demonstrated that morning hypothalamic connectivity to the dorsal striatum significantly predicts the individual accumulation of subjective sleepiness from morning to evening, while no such correlation was observed for the hypothalamus-ventral striatum connectivity. These findings underscore the distinct roles of hypothalamic connectivity to the dorsal and ventral striatum in individual sleep homeostasis, suggesting that hypothalamus-dorsal striatum circuit may be a promising target for interventions mitigating excessive sleepiness and promoting alertness.

Neurodevelopmental subtypes of functional brain organization in the ABCD study using a rigorous analytic framework.

The current study demonstrates that an individual's resting-state functional connectivity (RSFC) is a dependable biomarker for identifying differential patterns of cognitive and emotional functioning during late childhood. Using baseline RSFC data from the Adolescent Brain Cognitive Development (ABCD) study, which includes children aged 9-11, we identified four distinct RSFC subtypes. We introduce an integrated methodological pipeline for testing the reliability and importance of these subtypes. In the Identification phase, Leiden Community Detection defined RSFC subtypes, with their reproducibility confirmed through a split-sample technique in the Validation stage. The Evaluation phase showed that distinct cognitive and mental health profiles are associated with each subtype, with the Predictive phase indicating that subtypes better predict various cognitive and mental health characteristics than individual RSFC connections. The Replication stage employed bootstrapping and down-sampling methods to substantiate the reproducibility of these subtypes further. This work allows future explorations of developmental trajectories of these RSFC subtypes.

Relationship between punishment sensitivity and risk-taking propensity.

Previous research has shown that, in both laboratory and real-world contexts, punishment sensitivity is associated with lower risk-taking propensity. The neural underpinnings of the association between punishment sensitivity and risk-taking, however, remain largely unknown. To address this issue, we implemented resting-state functional connectivity (RSFC) and voxel-based morphometry (VBM) methodologies to investigate the neural basis of their relationship in the current study (N=594). The behavioral results confirmed a negative association between punishment sensitivity and risk-taking propensity, which supports the hypothesis. The VBM results demonstrated a positive correlation between punishment sensitivity and gray matter volume in the right orbitofrontal cortex (ROFC). Furthermore, the results of the RSFC analysis revealed that the functional connectivity between ROFC and the right medial temporal gyrus (RMTG) was positively associated with punishment sensitivity. Notably, mediation analysis demonstrated that punishment sensitivity acted as a complete mediator in the influence of ROFC-RMTG functional connectivity on risk-taking. These findings suggest that ROFC-RMTG functional connectivity may be the neural basis underlying the effect of punishment sensitivity on risk-taking propensity, which provides a new perspective for understanding the relationship between punishment sensitivity and risk-taking propensity.