Network-level enrichment provides a framework for biological interpretation of machine learning results.

Machine learning algorithms are increasingly being utilized to identify brain connectivity biomarkers linked to behavioral and clinical outcomes. However, research often prioritizes prediction accuracy at the expense of biological interpretability, and inconsistent implementation of ML methods may hinder model accuracy. To address this, our paper introduces a network-level enrichment approach, which integrates brain system organization in the context of connectome-wide statistical analysis to reveal network-level links between brain connectivity and behavior. To demonstrate the efficacy of this approach, we used linear support vector regression (LSVR) models to examine the relationship between resting-state functional connectivity networks and chronological age. We compared network-level associations based on raw LSVR weights to those produced from the forward and inverse models. Results indicated that not accounting for shared family variance inflated prediction performance, the k-best feature selection via Pearson correlation reduced accuracy and reliability, and raw LSVR model weights produced network-level associations that deviated from the significant brain systems identified by forward and inverse models. Our findings offer crucial insights for applying machine learning to neuroimaging data, emphasizing the value of network enrichment for biological interpretation.

Getting stress-related disorders under control: the untapped potential of neurofeedback.

Stress-related disorders are among the biggest global health challenges. Despite significant progress in understanding their neurocognitive basis, the promise of applying insights from fundamental research to prevention and treatment remains largely unfulfilled. We argue that neurofeedback - a method for training voluntary control over brain activity - has the potential to fill this translational gap. We provide a contemporary perspective on neurofeedback as endogenous neuromodulation that can target complex brain network dynamics, is transferable to real-world scenarios outside a laboratory or treatment facility, can be trained prospectively, and is individually adaptable. This makes neurofeedback a prime candidate for a personalized preventive neuroscience-based intervention strategy that focuses on the ecological momentary neuromodulation of stress-related brain networks in response to actual stressors in real life.

Effects of connectivity hyperalignment (CHA) on estimated brain network properties: from coarse-scale to fine-scale.

Recent gains in functional magnetic resonance imaging (fMRI) studies have been driven by increasingly sophisticated statistical and computational techniques and the ability to capture brain data at finer spatial and temporal resolution. These advances allow researchers to develop population-level models of the functional brain representations underlying behavior, performance, clinical status, and prognosis. However, even following conventional preprocessing pipelines, considerable inter-individual disparities in functional localization persist, posing a hurdle to performing compelling population-level inference. Persistent misalignment in functional topography after registration and spatial normalization will reduce power in developing predictive models and biomarkers, reduce the specificity of estimated brain responses and patterns, and provide misleading results on local neural representations and individual differences. This study aims to determine how connectivity hyperalignment (CHA)-an analytic approach for handling functional misalignment-can change estimated functional brain network topologies at various spatial scales from the coarsest set of parcels down to the vertex-level scale. The findings highlight the role of CHA in improving inter-subject similarities, while retaining individual-specific information and idiosyncrasies at finer spatial granularities. This highlights the potential for fine-grained connectivity analysis using this approach to reveal previously unexplored facets of brain structure and function.

Antidepressant and anxiolytic effects of activating 5HT2A receptors in the anterior cingulate cortex and the theoretical mechanisms underlying them - A scoping review of available literature.

Psychedelic drugs that activate the 5HT2A receptor have long been the target of extensive clinical research, particularly in models of psychiatric illness. The aim of this literature review was to investigate the therapeutic effects of 5HT2A receptor activation in the anterior cingulate cortex (ACC) and the respective mechanisms that underlie them. Based on the available research, I suggest that 5HT2A receptors in the ACC exert profound changes in excitatory neurotransmission and brain network connectivity in a way that reduces anxious preoccupation and obsessional thoughts, as well as promoting cognitive flexibility and long-lasting mood improvements in anhedonia. This is possibly due to a complex interplay with glutamate and gamma-butyric acid neurotransmission, particularly 5HT2A activation enhances α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptor signalling, thus altering the ratio of AMPA to N-methyl-D-Aspartate (NMDA) activity in the ACC, which can dismantle previously established neuronal connections and aid the formation of new ones, an effect that may be beneficial for fear extinction and reversal learning. Psychedelics potentially change intra- and internetwork connectivity, strengthening connectivity from the dorsal ACC / Salience Network to the Default Mode Network (DMN) and Central Executive Network (CEN), which correlates with improvements in attentional shifting and anti-anhedonic effects. Additionally, they may decrease inhibitory influence of the DMN over the CEN which may reduce overevaluation of internal states and ameliorate cognitive deficits. Activation of ACC 5HT2A receptors also has important downstream effects on subcortical areas, including reducing amygdala reactivity to threatening stimuli and enhancing mesolimbic dopamine, respectively improving anxiety and the experience of natural rewards.

Resting-state brain networks alterations in adolescents with Internet Gaming Disorder associate with cognitive control impairments.

Objective: Research indicates that cognitive control is compromised in individuals with internet gaming disorder (IGD). However, the neural mechanisms behind it are still unclear. This study aims to investigate alterations in resting-state brain networks in adolescents with IGD and the potential neurobiological mechanisms underlying cognitive dysfunction. Materials and methods: A total of 44 adolescent IGD subjects (male/female: 38/6) and 50 healthy controls (male/female: 40/10) were enrolled. Participants underwent demographic assessments, Young's Internet Addiction Scale, Barratt Impulsiveness Scale 11 Chinese Revised Version, the Chinese Adolescents' Maladaptive Cognitions Scale, exploratory eye movement tests, and functional magnetic resonance imaging (fMRI). FMRI data were analyzed using the GIFT software for independent component analysis, focusing on functional connectivity within and between resting-state brain networks. Results: In comparison to the control group, impulsivity in adolescent IGD subjects showed a positive correlation with the severity of IGD (r=0.6350, p < 0.001), linked to impairments in the Executive Control Network (ECN) and a decrease in functional connectivity between the Salience Network (SN) and ECN (r=0.4307, p=0.0021; r=-0.5147, p=0.0034). Decreased resting state activity of the dorsal attention network (DAN) was associated with attentional dysregulation of IGD in adolescents (r=0.4071, p=0.0017), and ECN increased functional connectivity with DAN. The degree of IGD was positively correlated with enhanced functional connectivity between the ECN and DAN (r=0.4283, p=0.0037). Conclusions: This research demonstrates that changes in the ECN and DAN correlate with heightened impulsivity and attentional deficits in adolescents with IGD. The interaction between cognitive control disorders and resting-state brain networks in adolescent IGD is related.

Altered functional coupling of the bed nucleus of the stria terminalis and amygdala in spider phobic fear.

Background: Individuals with spider phobic fear (SP) show hypervigilance and amygdala hyperactivity towards fear-associated stimuli, which may promote the development of other anxiety disorders. The amygdala is a key region within the fear network, which is connected to the anxiety system, where the bed nucleus of the stria terminalis (BNST) plays a crucial role. However, the BNST's involvement in phobic fear is unknown. Therefore, this study investigated the association of phobic fear and anxiety on these regions' functional connectivity (FC) in SP compared to healthy controls (HC). Methods: 7T-fMRI resting-state FC of 30 individuals with SP and 45 HC was assessed to detect network differences between these groups. The association of phobic fear severity, trait anxiety, and social anxiety on FC was explored using linear regressions combined with seed-to-voxel analyses with amygdala and BNST as primary seeds, corrected for age and sex. Results: In SP, phobic fear was associated with reduced FC between the left amygdala and the right supramarginal gyrus. In contrast, anxiety severity was related to increased FC between the right BNST and the left inferior frontal gyrus. Moreover, social anxiety was related to decreased FC between bilateral BNST and left precuneus. Conclusions: These findings show changes in FC in SP, connecting fear with altered activity in the BNST and amygdala. The results suggest that persistent anxiety in phobic fear is associated with abnormal brain function in these regions, potentially explaining susceptibility to anxiety disorders and processes involved in phobic fear, such as threat perception, avoidance, and salience.

Resting-state EEG microstates predict mentalizing ability as assessed by the Reading the Mind in the Eyes test.

Microstates analysis of electroencephalography (EEG) has gained increasing attention among researchers and clinicians as a valid tool for investigating temporal dynamics of large-scale brain networks with a millisecond time resolution. Although microstates analysis has been widely applied to elucidate the neurophysiological basis of various cognitive functions in both clinical and non-clinical samples, its application in relation to socio-affective processing has been relatively under-researched. Therefore, the main aim of the current study was to investigate the relationship between EEG microstates and mentalizing (i.e., the ability to understand the mental states of others). Eighty-two participants (thirty-six men; mean age: 24.28 ± 7.35 years; mean years of education: 15.82 ± 1.77) underwent a resting-state EEG recording and performed the Reading the Mind in the Eyes Test (RMET). The parameters of the microstates were then calculated using Cartool v. 4.09 software. Our results showed that the occurrence of microstate map C was independently and positively associated with the RMET total score and contributed to the prediction of mentalizing performance, even when controlling for potential confounding variables (i.e., age, sex, education level, tobacco and alcohol use). Since microstate C is involved in self-related processes, our findings may reflect the link between self-awareness of one's own thoughts/feelings and the enhanced ability to recognize the mental states of others at the neurophysiological level. This finding extends the functions traditionally attributed to microstate C, i.e. mind-wandering, self-related thoughts, prosociality, and emotional and interoceptive processing, to include mentalizing ability.

Investigating changes of functional brain networks in major depressive disorder by graph theoretical analysis of resting-state fMRI.

BACKGROUND: Major Depressive Disorder (MDD), as a chronic mental disorder, causes changes in mood, thoughts, and behavior. The pathophysiology of the disorder and its treatment are still unknown. One of the most notable changes observed in patients with MDD through fMRI is abnormal functional brain connectivity. METHODS: Preprocessed data from 60 MDD patients and 60 normal controls (NCs) were selected, which has been performed using the DPARSF toolbox. The whole-brain functional networks and topologies were extracted using graph theory-based methods. A two-sample, two-tailed t-test was used to compare the topological features of functional brain networks between the MDD and NCs groups using the DPABI-Net/Statistical Analysis toolbox. RESULTS: The obtained results showed a decrease in both global and local efficiency in MDD patients compared to NCs, and specifically, MDD patients showed significantly higher path length values. Acceptable p-values were obtained with a small sample size and less computational volume compared to the other studies on large datasets. At the node level, MDD patients showed decreased and relatively decreased node degrees in the sensorimotor network (SMN) and the dorsal attention network (DAN), respectively, as well as decreased node efficiency in the SMN, default mode network (DMN), and DAN. Also, MDD patients showed slightly decreased node efficiency in the visual networks (VN) and the ventral attention network (VAN), which were reported after FDR correction with Q < 0.05. LIMITATIONS: All participants were Chinese. CONCLUSIONS: Collectively, increased path length, decreased global and local efficiency, and also decreased nodal degree and efficiency in the SMN, DAN, DAN, VN, and VAN were found in patients compared to NCs.

Addressing inconsistency in functional neuroimaging: A replicable data-driven multi-scale functional atlas for canonical brain networks.

The advent of various neuroimaging methodologies has greatly aided in the conceptualization of large-scale brain networks in the field of cognitive neuroscience. However, there is inconsistency across studies in both nomenclature and the functional entities being described. There is a need for a unifying framework which standardizes terminology across studies while also bringing analyses and results into the same reference space. Here we present a functional whole-brain atlas of canonical brain networks derived from more than 100k resting-state fMRI datasets. These data-driven networks are highly replicable across datasets as well as multiple spatial scales. We have organized, labeled, and described them with terms familiar to the fields of cognitive and affective neuroscience in order to optimize their utility in future neuroimaging analyses and enhance the accessibility of new findings. The benefits of this atlas are not limited to future template-based or reference-guided analyses, but also extend to other data-driven neuroimaging approaches across modalities, such as those using blind independent component analysis (ICA). Future studies utilizing this atlas will contribute to greater harmonization and standardization in functional neuroimaging research.

Oscillatory cortico-cortical connectivity during tactile discrimination between dynamic and static stimulation.

Fine sensory modalities play an essential role in perceiving the world. However, little is known about how the cortico-cortical distinguishes between dynamic and static tactile signals. This study investigated oscillatory connectivity during a tactile discrimination task of dynamic and static stimulation via electroencephalogram (EEG) recordings and the fast oscillatory networks across widespread cortical regions. While undergoing EEG recordings, the subject felt an electro-tactile presented by a 3-dot array. Each block consisted of 3 forms of stimulation: Spatio-temporal (dynamic), Spatial (static), and Control condition (lack of electrical stimulation). The average event-related potential for the Spatial and Spatio-temporal conditions exhibited statistically significant differences between 25 and 75, 81 and 121, 174 and 204 and 459 and 489 ms after stimulus onset. Based on those times, the sLORETA approach was used to reconstruct the inverse solutions of EEG. Source localization appeared superior parietal at around 25 to 75 ms, in the primary motor cortex at 81 to 121 ms, in the central prefrontal cortex at 174 to 204 and 459 to 489 ms. To better assess spectral brain functional connectivity, we selected frequency ranges with correspondingly significant differences: for static tactile stimulation, these are concentrated in the Theta, Alpha, and Gamma bands, whereas for dynamic stimulation, the relative energy change bands are focused on the Theta and Alpha bands. These nodes' functional connectivity analysis (phase lag index) showed 3 distinct distributed networks. A tactile information discrimination network linked the Occipital lobe, Prefrontal lobe, and Postcentral gyrus. A tactile feedback network linked the Prefrontal lobe, Postcentral gyrus, and Temporal lobe. A dominant motor feedforward loop network linked the Parietal cortex, Prefrontal lobe, Frontal lobe, and Parietal cortex. Processing dynamic and static tactile signals involves discriminating tactile information, motion planning, and cognitive decision processing.

Functional Connectivity Profiles of Ten Sub-Regions within the Premotor and Supplementary Motor Areas: Insights into Neurophysiological Integration.

Objectives: This study aimed to comprehensively investigate the functional connectivity of ten sub-regions within the premotor and supplementary motor areas (Right and Left Premotor 6d1, 6d2, 6d3, and Right and Left pre-Supplementary Motor (presma) and SMA). Using advanced magnetic resonance imaging (MRI), the objective was to understand the neurophysiological integrative characteristics of these regions by examining their connectivity with eight distinct functional brain networks. While previous studies have largely treated these areas as homogeneous entities, there is a significant gap in our understanding of the specific roles and connectivity profiles of their distinct sub-regions. The goal was to uncover the roles of these regions beyond conventional motor functions, contributing to a more holistic understanding of brain functioning. Methods: The study involved 198 healthy volunteers, with the primary methodology being functional connectivity analysis using advanced MRI techniques. Ten sub-regions within the premotor and supplementary motor areas served as seed regions, and their connectivity with eight distinct brain regional functional networks, including the Sensorimotor, Dorsal Attention, Language, Frontoparietal, Default Mode, Cerebellar, Visual, and Salience networks, was investigated. This approach allowed for the exploration of synchronized activity between these critical brain areas, shedding light on their integrated functioning and relationships with other brain networks. Results: The study revealed a nuanced landscape of functional connectivity for the premotor and supplementary motor areas with the main functional brain networks. Despite their high functional connectedness within the motor network, these regions displayed diverse functional integrations with other networks. There was moderate connectivity with the Sensorimotor and Dorsal Attention networks, highlighting their roles in motor execution and attentional processes. However, connectivity with the Language, Frontoparietal, Default Mode, Cerebellar, Visual, and Salience networks was generally low, indicating a primary focus on motor-related tasks. Conclusions: This study emphasized the multifaceted roles of the sub-regions of the premotor and supplementary motor areas. Beyond their crucial involvement in motor functions, these regions exhibited varied functional integrations with different brain networks. The observed disparities, especially in the Sensorimotor and Dorsal Attention networks, indicated a nuanced and specialized involvement of these regions in diverse cognitive functions. By delineating the specific connectivity profiles of these sub-regions, this study addresses the existing knowledge gap and suggests unique and distinct roles for each brain area in sophisticated cognitive tasks beyond their conventional motor functions. The results suggested unique and distinct roles for each brain area in sophisticated cognitive tasks beyond their conventional motor functions. This study underscores the importance of considering the broader neurophysiological landscape to comprehend the intricate roles of these brain areas, contributing to ongoing efforts in unravelling the complexities of brain function.

Minimum spanning tree analysis of unimpaired individuals at risk of Alzheimer's disease.

Identifying early and non-invasive biomarkers to detect individuals in the earliest stages of the Alzheimer's disease continuum is crucial. As a result, electrophysiology and plasma biomarkers are emerging as great candidates in this pursuit due to their low invasiveness. This is the first magnetoencephalography study to assess the relationship between minimum spanning tree parameters, an alternative to overcome the comparability and thresholding problem issues characteristic of conventional brain network analyses, and plasma phosphorylated tau231 levels in unimpaired individuals, with different risk levels of Alzheimer's disease. Seventy-six individuals with available magnetoencephalography recordings and phosphorylated tau231 plasma determination were included. The minimum spanning tree for the theta, alpha and beta bands for each subject was obtained, and the leaf fraction, tree hierarchy and diameter were calculated. To study the relationship between these topological parameters and phosphorylated tau231, we performed correlation analyses, for the whole sample and considering the two risk sub-groups separately. Increasing concentrations of phosphorylated tau231 were associated with greater leaf fraction and tree hierarchy values, along with lower diameter values, for the alpha and theta frequency bands. These results emerged for the whole sample and the higher risk group, but not for the lower risk group. Our results indicate that the network topology of cognitively unimpaired individuals with elevated plasma phosphorylated tau231 levels, a marker of Alzheimer's disease pathology and amyloid-ß accumulation, is already altered, shifting towards a more integrated network increasing its vulnerability and hub-dependency, mostly in the alpha band. This is indicated by increases in leaf fraction and tree hierarchy, along with reductions in diameter. These results match the initial trajectory proposed by theoretical models of disease progression and network disruption and suggest that changes in brain function and organization begin early on.

Why is vestibular migraine associated with many comorbidities?

Vestibular migraine (VM) is a usual trigger of episodic vertigo. Patients with VM often experience spinning, shaking, or unsteady sensations, which are usually also accompanied by photophobia, phonophobia, motor intolerance, and more. VM is often associated with a number of comorbidities. Recurrent episodes of VM can affect the patient's emotions, sleep, and cognitive functioning to varying degrees. Patients with VM may be accompanied by adverse moods such as anxiety, fear, and depression, which can gradually develop into anxiety disorders or depressive disorders. Sleep disorders are also a common concomitant symptom of VM, which significantly lower patients' quality of life. The influence of anxiety disorders and sleep disorders may reduce cognitive functions of VM, such as visuospatial ability, attention, and memory decline. Clinically, it is also common to see VM comorbid with other vestibular disorders, making the diagnosis more difficult. VM episodes are relieved but lingering, in which case VM may coexist with persistent postural-perceptual dizziness (PPPD). Anxiety may be an important bridge between recurrent VM and PPPD. The clinical manifestations of VM and Meniere's disease (MD) overlap considerably, and those who meet the diagnostic criteria for both can be said to have VM/MD comorbidity. VM can also present with positional vertigo, and some patients with VM present with typical benign paroxysmal positional vertigo (BPPV) nystagmus on positional testing. In this paper, we synthesize and analyze the pathomechanisms of VM comorbidity by reviewing the literature. The results show that it may be related to the extensive connectivity of the vestibular system with different brain regions and the close connection of the trigeminovascular system with the periphery of the vestibule. Therefore, it is necessary to pay attention to the diagnosis of comorbidities in VM, synthesize its pathogenesis, and give comprehensive treatment to patients.

David Ferrier's "complex whole": Early traces of a "brain network" concept.

Currently, the idea that the brain is a complex network of interacting brain regions is hardly controversial. The rapid development of this field is often attributed to the emergence of powerful brain-imaging techniques and, around the millennium, the merging of the neuroscience of brain networks with modern mathematical graph theory. However, little is known about the historical roots of this concept. It is interesting to know when the first traces of a concept of brain networks can be found in the work of early neuroscientists, how this concept evolved over time, and what factors may have influenced this evolution. This study aims to set a first step in addressing these questions by a detailed analysis of David Ferrier's classic study, The Functions of the Brain. From this analysis it will become clear that, in addition to a clear notion of localized functions in the brain, Ferrier speculated in several places about the need for several of these brain regions to communicate and interact in order to bring about higher brain functions. He referred to this perspective on the brain as a "complex whole," which could be interpreted as an early precursor of the modern concept of brain networks.

Resting-state functional connectivity abnormalities in subjective cognitive decline: A 7T MRI study.

Resting-state functional connectivity (FC) MRI is sensitive to brain changes in Alzheimer's disease in preclinical stages, however studies in persons with subjective cognitive decline (SCD) have reported conflicting findings, and no study is available at 7T MRI. In this study, we investigated FC alterations in sixty-six participants recruited at the Geneva Memory Center (24 controls, 14 SCD, 28 cognitively impaired [CI]). Participants were classified as SCD if they reported cognitive complaints without objective cognitive deficits, and underwent 7T fMRI to assess FC in canonical brain networks and their association with cognitive/clinical features. SCD showed normal cognition, a trend for higher depressive symptoms, and normal AD biomarkers. Compared to the other two groups, SCD showed higher FC in frontal default mode network (DMN) and insular and superior temporal nodes of ventral attention network (VAN). Higher FC in the DMN and VAN was associated with worse cognition but not depression, suggesting that hyper-connectivity in these networks may be a signature of age-related cognitive decline in SCD at low risk of developing AD.

From neurons to brain networks, pharmacodynamics of stimulant medication for ADHD.

Stimulants represent the first line pharmacological treatment for attention-deficit/hyperactivity disorder (ADHD) and are among the most prescribed psychopharmacological treatments. Their mechanism of action at synaptic level has been extensively studied. However, it is less clear how their mechanism of action determines clinically observed benefits. To help bridge this gap, we provide a comprehensive review of stimulant effects, with an emphasis on nuclear medicine and magnetic resonance imaging (MRI) findings. There is evidence that stimulant-induced modulation of dopamine and norepinephrine neurotransmission optimizes engagement of task-related brain networks, increases perceived saliency, and reduces interference from the default mode network. An acute administration of stimulants may reduce brain alterations observed in untreated individuals in fronto-striato-parieto-cerebellar networks during tasks or at rest. Potential effects of prolonged treatment remain controversial. Overall, neuroimaging has fostered understanding on stimulant mechanism of action. However, studies are often limited by small samples, short or no follow-up, and methodological heterogeneity. Future studies should address age-related and longer-term effects, potential differences among stimulants, and predictors of treatment response.

Brain dynamics supported by a hierarchy of complex correlation patterns defining a robust functional architecture.

Functional magnetic resonance imaging (fMRI) provides insights into cognitive processes with significant clinical potential. However, delays in brain region communication and dynamic variations are often overlooked in functional network studies. We demonstrate that networks extracted from fMRI cross-correlation matrices, considering time lags between signals, show remarkable reliability when focusing on statistical distributions of network properties. This reveals a robust brain functional connectivity pattern, featuring a sparse backbone of strong 0-lag correlations and weaker links capturing coordination at various time delays. This dynamic yet stable network architecture is consistent across rats, marmosets, and humans, as well as in electroencephalogram (EEG) data, indicating potential universality in brain dynamics. Second-order properties of the dynamic functional network reveal a remarkably stable hierarchy of functional correlations in both group-level comparisons and test-retest analyses. Validation using alcohol use disorder fMRI data uncovers broader shifts in network properties than previously reported, demonstrating the potential of this method for identifying disease biomarkers.

Visual contrast sensitivity is associated with community structure integrity in cognitively unimpaired older adults: the Brain Networks and Mobility (B-NET) Study.

Older adults with impairment in contrast sensitivity (CS), the ability to visually perceive differences in light and dark, are more likely to demonstrate limitations in mobility function, but the mechanisms underlying this relationship are poorly understood. We sought to determine if functional brain networks important to visual processing and mobility may help elucidate possible neural correlates of this relationship. This cross-sectional analysis utilized functional MRI both at rest and during a motor imagery (MI) task in 192 community-dwelling, cognitively-unimpaired older adults ≥ 70 years of age from the Brain Networks and Mobility study (B-NET). Brain networks were partitioned into network communities, groups of regions that are more interconnected with each other than the rest of the brain, the spatial consistency of the communities for multiple brain subnetworks was assessed. Lower baseline binocular CS was significantly associated with degraded sensorimotor network (SMN) community structure at rest. During the MI task, lower binocular CS was significantly associated with degraded community structure in both the visual (VN) and default mode network (DMN). These findings may suggest shared neural pathways for visual and mobility dysfunction that could be targeted in future studies.

Chronic hypercortisolism disrupts the principal functional gradient in Cushing's disease: A multi-scale connectomics and transcriptomics study.

Cushing's disease (CD) represents a state of cortisol excess, serving as a model to investigate the effects of prolonged hypercortisolism on functional brain. Potential alterations in the functional connectome of the brain may explain frequently reported cognitive deficits and affective disorders in CD patients. This study aims to elucidate the effects of chronic hypercortisolism on the principal functional gradient, which represents a hierarchical architecture with gradual transitions across cognitive processes, by integrating connectomics and transcriptomics approaches. Utilizing resting-state functional magnetic resonance imaging data from 140 participants (86 CD patients, 54 healthy controls) recruited at a single center, we explored the alterations in the principal gradient in CD patients. Further, we thoroughly explored the underlying associative mechanisms of the observed characteristic alterations with cognitive function domains, biological attributes, and neuropsychiatric representations, as well as gene expression profiles. Compared to healthy controls, CD patients demonstrated changes in connectome patterns in both primary and higher-order networks, exhibiting an overall converged trend along the principal gradient axis. The gradient values in CD patients' right prefrontal cortex and bilateral sensorimotor cortices exhibited a significant correlation with cortisol levels. Moreover, the cortical regions showing gradient alterations were principally associated with sensory information processing and higher-cognitive functions, as well as correlated with the gene expression patterns which involved synaptic components and function. The findings suggest that converged alterations in the principal gradient in CD patients may mediate the relationship between hypercortisolism and cognitive impairments, potentially involving genes regulating synaptic components and function.