Most recent paper

Med Image Comput Comput Assist Interv. 2024 Oct;15003:519-529. doi: 10.1007/978-3-031-72384-1_49. Epub 2024 Oct 3.

ABSTRACT

Higher-order properties of functional magnetic resonance imaging (fMRI) induced connectivity have been shown to unravel many exclusive topological and dynamical insights beyond pairwise interactions. Nonetheless, whether these fMRI-induced higher-order properties play a role in disentangling other neuroimaging modalities' insights remains largely unexplored and poorly understood. In this work, by analyzing fMRI data from the Human Connectome Project Young Adult dataset using persistent homology, we discovered that the volume-optimal persistence homological scaffolds of fMRI-based functional connectomes exhibited conservative topological reconfigurations from the resting state to attentional task-positive state. Specifically, while reflecting the extent to which each cortical region contributed to functional cycles following different cognitive demands, these reconfigurations were constrained such that the spatial distribution of cavities in the connectome is relatively conserved. Most importantly, such level of contributions covaried with powers of aperiodic activities mostly within the theta-alpha (4-12 Hz) band measured by magnetoencephalography (MEG). This comprehensive result suggests that fMRI-induced hemodynamics and MEG theta-alpha aperiodic activities are governed by the same functional constraints specific to each cortical morpho-structure. Methodologically, our work paves the way toward an innovative computing paradigm in multimodal neuroimaging topological learning. The code for our analyses is provided in https://github.com/ngcaonghi/scaffold_noise.

PMID:39949393 | PMC:PMC11816146 | DOI:10.1007/978-3-031-72384-1_49

Ageing Res Rev. 2025 Feb 11:102688. doi: 10.1016/j.arr.2025.102688. Online ahead of print.

ABSTRACT

INTRODUCTION: Despite normal cognitive abilities, subjective memory complaints (SMC) are common in older adults and are linked to mild memory impairment. SMC may be a sign of subtle cognitive decline and underlying pathological changes, according to research; however, there is not enough data to support the use of resting-state functional connectivity to identify early changes in the brain network before cognitive symptoms manifest.

MATERIALS AND METHODS: In this study, the topological structure and regional connectivity of the brain functional network in SMC individuals were analyzed using graph theoretical analysis in both weighted and binarized network models, alongside healthy controls. Resting-state functional magnetic resonance imaging data was collected from 24 SMCs and 39 cognitively normal people. Analysis of both binary and weighted graph theory was done using the Dosenbach Atlas as a basis based on area under curves (AUCs) for the graph network parameters, which comprised of six node metrics and nine global measures. We then performed group comparisons using statistical analyses based on Network-Based Statistics functional connectomes. Finally, the relationship between global graph measures and cognition was examined using neuropsychological tests such as the Mini-Mental State Examination (MMSE) and the Alzheimer Disease Assessment Scale (ADAS score).

RESULTS: The topologic properties of brain functional connectomes at both global and nodal levels were tested. The SMC patients showed increased functional connectivity in clustering coefficient global (P < 0.00001), global efficiency (P < 0.00001), and normalized characteristic path length or Lambda (P < 0.00001), while there was decreased functional connectivity in Modularity (P < 0.04542), characteristic path length (0.00001), and small-worldness or Sigma (P < 0.00001) in binary networks model. In contrast, SMC patients only exhibited decreased functional connectivity in Assortativity identified by weighted networks model. Furthermore, some brain regions located in the default mode network, sensorimotor, occipital, and cingulo-opercular network in SMC patients showed altered nodal centralities. No significant correlation was found between global metrics and MMSE scores in both groups using binary metrics. However, in cognitively normal individuals, negative correlation was observed with weighted metrics in global and local efficiency and Lambda. While In SMC patients, a significant positive correlation was found between ADAS scores and local efficiency in both binary and weighted metrics.

CONCLUSION: The findings suggest that functional impairments in SMC patients might be associated with disruptions in the global and regional topological organization of the brain's functional connectome, offering new and significant insights into the pathophysiological mechanisms underlying SMC.

PMID:39947486 | DOI:10.1016/j.arr.2025.102688

Front Neurol. 2025 Jan 29;16:1467175. doi: 10.3389/fneur.2025.1467175. eCollection 2025.

ABSTRACT

Currently, HIV-associated neurocognitive disorders (HAND) remains one of the major challenges faced by people living with HIV (PLWH). HAND involves the vulnerability of neural circuits caused by synaptic degeneration and abnormal synaptic pruning. In recent years, connectomics has been gradually applied to HAND research as a cutting-edge method for describing the structural and functional connectivity patterns of the brain, to further elucidate the specific mechanisms underlying these neural circuit vulnerabilities. Using multimodal neuroimaging techniques such as diffusion tensor imaging (DTI), structural magnetic resonance imaging (sMRI), and resting-state functional magnetic resonance imaging (rs-fMRI), researchers can detail the connectome network changes in the brains of PLWH. These technologies offer potential biomarkers for the early diagnosis, prognosis, and treatment monitoring of HAND, while also providing new avenues for personalized prediction of cognitive status. Here, we start with the pathogenesis and risk factors of HAND, providing a comprehensive review of the basic concepts of unimodal and multimodal macro connectomics and related graph theory methods, and we review the latest progress in HAND connectomics research. We emphasize the use of connectomics to identify specific disease patterns of HIV-associated neurodegeneration and discuss the potential research directions and challenges in understanding these diseases from a connectomics perspective.

PMID:39944538 | PMC:PMC11813760 | DOI:10.3389/fneur.2025.1467175

Int J Clin Health Psychol. 2025 Jan-Mar;25(1):100547. doi: 10.1016/j.ijchp.2025.100547. Epub 2025 Jan 29.

ABSTRACT

Despite both internet gaming disorder (IGD) and gambling disorder (GD) being officially recognized as medical conditions by the World Health Organization, controversies persist. A transdiagnostic study may help inform classification and intervention approaches. IGD and GD may share or have distinct neural and behavioral features. To investigate, resting-state functional magnetic resonance imaging (fMRI) and self-reported behavioral data were collected from 58 individuals with GD, 31 with IGD, and 83 healthy control (HC) participants. After controlling for demographics, both GD and IGD groups scored lower on measures of gambling-related positive play. Neural data revealed reduced brain connectivity in the right rectus/orbital frontal gyrus in GD and IGD groups compared to HC participants. IGD participants displayed increased dynamic brain activity in the left triangular inferior frontal gyrus compared with GD and HC participants. Relatively decreased modular flexibility was also observed in GD but not IGD participants, relative to HC participants. Multiclass classification results showed that the indicators of gambling-related positive play, as well as dynamic brain activity and connectivity patterns, were useful for classifying GD, IGD, and HC participants, outperforming the use of either neural signals or self-report indicators alone. The shared phenotypes of GD and IGD groups provide insight into common features of behavioral addictions, and the combination of self-report and neural measures may provide the most robust approach for classification of diagnostic groups.

PMID:39944189 | PMC:PMC11815891 | DOI:10.1016/j.ijchp.2025.100547

Transl Psychiatry. 2025 Feb 12;15(1):49. doi: 10.1038/s41398-025-03249-y.

ABSTRACT

Suicide is a complex behavior strongly associated with depression. Despite extensive research, an objective biomarker for evaluating suicide risk precisely and timely is still lacking. Using the precision resting-state fMRI method, we studied 61 depressive patients with suicide ideation (SI) or suicide attempt (SA), and 35 patients without SI to explore functional biomarkers of suicide risk. Among them, 21 participants also completed electroconvulsive therapy (ECT) treatment, allowing the examination of functional changes across different risk states within the same individual. Functional networks were localized in each subject using resting-state fMRI and then an individualized connectome was constructed to represent the subject's functional brain organization. We identified a set of connections that track suicide risk (r = 0.41, p = 0.001) and found that these risk-associated connections were hyper-connected in the frontoparietal network (FPN, p = 0.008, Cohen's d = 0.58) in patients with suicide risk compared to those without. Moreover, ECT treatment significantly reduced (p = 0.001, Cohen's d = 0.56) and normalized these FPN hyper-connections. These findings suggest that connections involving FPN may constitute an important biomarker for evaluating suicide risk and may provide potential targets for interventions such as non-invasive brain stimulation.

PMID:39939611 | DOI:10.1038/s41398-025-03249-y

J Imaging Inform Med. 2025 Feb 12. doi: 10.1007/s10278-025-01434-5. Online ahead of print.

ABSTRACT

Functional magnetic resonance imaging (fMRI) is extensively used in clinical and preclinical settings to study brain function; however, fMRI data is inherently noisy due to physiological processes, hardware, and external noise. Denoising is one of the main preprocessing steps in any fMRI analysis pipeline. This process is challenging in preclinical data in comparison to clinical data due to variations in brain geometry, image resolution, and low signal-to-noise ratios. In this paper, we propose a structure-preserved algorithm based on a 3D Wasserstein generative adversarial network with a 3D dense U-net-based discriminator called 3D U-WGAN. We apply a 4D data configuration to effectively denoise temporal and spatial information in analyzing preclinical fMRI data. GAN-based denoising methods often utilize a discriminator to identify significant differences between denoised and noise-free images, focusing on global or local features. To refine the fMRI denoising model, our method employs a 3D dense U-Net discriminator to learn both global and local distinctions. To tackle potential oversmoothing, we introduce an adversarial loss and enhance perceptual similarity by measuring feature space distances. Experiments illustrate that 3D U-WGAN significantly improves image quality in resting-state and task preclinical fMRI data, enhancing signal-to-noise ratio without introducing excessive structural changes in existing methods. The proposed method outperforms state-of-the-art methods when applied to simulated and real data in a fMRI analysis pipeline.

PMID:39939477 | DOI:10.1007/s10278-025-01434-5

Brain Res Bull. 2025 Feb 10:111250. doi: 10.1016/j.brainresbull.2025.111250. Online ahead of print.

ABSTRACT

OBJECTIVE: To compare the brain functional changes in tinnitus patients of varying severities, in order to elucidate the complex relationship between tinnitus symptoms and neural mechanisms, providing a basis for personalized treatment for tinnitus patients with varying severity levels.

METHOD: 62 patients with chronic tinnitus were divided into severe and mild tinnitus group. 31 healthy controls (HC) matched for age, gender and education level were included. Resting-state functional magnetic resonance imaging was performed for all subjects, and the values of regional homogeneity (ReHo), amplitude of low-frequency fluctuation (ALFF), fractional ALFF (fALFF) and functional connectivity (FC) were calculated. One-way analysis of variance (ANOVA) was used to compare the differences among the three groups. Correlational analysis was conducted between imaging metrics and clinical information.

RESULTS: Compared to the mild tinnitus, the severe tinnitus shows increased ReHo and ALFF values in the left superior temporal gyrus (STG), middle temporal gyrus (MTG), supramarginal gyrus (SMG), angular gyrus (ANG), and middle occipital gyrus (MOG), as well as increased ReHo values in the left superior frontal gyrus (SFG) and ALFF values in the right ANG. In the severe tinnitus group, the FC between the bilateral ANG and the left MTG, the right ANG and the right medial SFG, the right ANG and the right anterior cingulate gyrus (ACG), as well as between the left SFG and the left rectus gyrus, was increased compared to the mild tinnitus group. In mild tinnitus group, the ReHo of left STG is correlated with tinnitus severity by Tinnitus Handicap Inventory.

CONCLUSION: Patients with different severity of tinnitus exhibit different compensatory mechanisms in brain function, highlighting the need for stratified analysis based on severity when investigating the underlying neural mechanisms.

PMID:39938755 | DOI:10.1016/j.brainresbull.2025.111250

Physiol Behav. 2025 Feb 10:114842. doi: 10.1016/j.physbeh.2025.114842. Online ahead of print.

ABSTRACT

Accumulating evidence suggests that the menstrual cycle and its endogenous ovarian hormones, including progesterone (PROG) and estradiol (E2), affect cognitive performance in women, particularly by modulating the prefrontal regions. In this study, we investigated whether differences in PROG and E2 levels modulate attentional control by affecting the prefrontal cognitive control areas. An fMRI scan was conducted on 53 naturally cycling healthy women in their late follicular phase (FP, n = 28) or mid-luteal phase (LP, n = 25) to examine the resting and task states during the completion of a face‒gender Stroop task. PROG was found to be positively correlated with the nodal efficiency of the inferior frontal gyrus (IFG) in the resting-state executive control network. At the behavioral level, while accuracy in categorizing male faces remained similar, participants in the mid-LP were significantly more accurate in categorizing female faces than those in the late FP. At the neural level, both the univariate and multivariate results indicated that higher levels of PROG enhance the detection and resolution of female incongruent faces through the activation of the bilateral IFG. These findings expand evidence of the effects of ovarian hormones on prefrontal-based attentional control in the social context.

PMID:39938608 | DOI:10.1016/j.physbeh.2025.114842

J Psychiatr Res. 2025 Jan 30;183:61-71. doi: 10.1016/j.jpsychires.2025.01.048. Online ahead of print.

ABSTRACT

Fibromyalgia (FM) is a complex medical condition. The nested hierarchical model of self and its extension to the pain matrix could represent an integrated theoretical framework that might comprehensively captures FM clinical feautres. A multi-level meta-analysis was conducted. Resting-state functional connectivity (RS-FC) studies that compared patients with FM and healthy controls (HCs) were included. The association between RS-FC among self-related brain regions and pain intensity was also explored in the FM group. Eleven studies were eligible for meta-analytic procedures. Patients with FM, compared to HCs, were characterized by an increased RS-FC between the default mode network (DMN) and areas ascribed to interoceptive (e.g., insula) and exteroceptive (e.g., premotor, visual/auditory cortices) self layers. The clinical group also showed a reduced RS-FC among regions of the pain matrix (i.e., periaqueductal gray matter, somatosensory areas) involved in pain modulation. An increased RS-FC within DMN together with a heightened RS-FC between DMN and interoceptive self areas were positively associated to pain intensity reported by patients with FM. The nested hierarchical model of self and its extension to the pain matrix might represent comprehensive neurobiological backgrounds for clarifying core mind-body clinical features of FM.

PMID:39938202 | DOI:10.1016/j.jpsychires.2025.01.048

Dev Cogn Neurosci. 2025 Feb 7;72:101523. doi: 10.1016/j.dcn.2025.101523. Online ahead of print.

ABSTRACT

It is well accepted that the brain is functionally organized into multiple networks and extensive literature has demonstrated that the organization of these networks shows major changes during adolescence. Yet, there is limited option for a reference functional brain atlas derived from typically-developing adolescents, which is problematic as the reliable identification of functional brain networks crucially depends on the use of such reference functional atlases. In this context, we utilized resting-state functional MRI data from 1391 typically-developing youth aged 8-17 years to create an adolescent-specific reference atlas of functional brain networks. We further investigated the impact of age and sex on these networks. Using a multiscale individual component clustering algorithm, we identified 24 reliable functional brain networks, classified within six domains: Default-Mode (5 networks), Control (4 networks), Salience (3 networks), Attention (4 networks), Somatomotor (5 networks), and Visual (3 networks). We identified reliable and large effects of age on the spatial topography of these majority of networks, as well as on the functional network connectivity. Sex effects were not as widespread. We created a novel brain atlas, named Dev-Atlas, focused on a typically-developing sample, with the hope that this atlas can be used in future developmental neuroscience studies.

PMID:39938145 | DOI:10.1016/j.dcn.2025.101523

PLoS One. 2025 Feb 12;20(2):e0318505. doi: 10.1371/journal.pone.0318505. eCollection 2025.

ABSTRACT

BACKGROUND: Insecure adult attachment has previously been linked to more severe psychopathology and to alterations within neuronal connectivity on a structural as well as functional level. Little is known about the resting state functional connectivity (rs-FC) of the attachment system in patients suffering from poly-drug use disorder (PUD).

METHODS: The present study investigated rs-FC at two measuring points (t1: ROI-to-ROI; t2: seed-to-voxel) in a sample of PUD patients (n = 33; Age: M = 30y; SD = 8y; Female = 15%). Adult attachment was measured with the German version of the Experiences in Close Relationships Scale (ECR-RD8). Furthermore, insecure attachment was correlated with depressive symptoms (ADS), trait anxiety (STAI) and general psychopathology (BSI-53).

RESULTS: More insecure attachment was associated with increased trait anxiety, depressive and general psychiatric symptom burden in patients. Furthermore, we observed time-stable links between insecure adult attachment and increased rs-FC between the left lateral parietal default mode network (DMN LP) and bilateral parts of the salience network, as well as decreased rs-FC between DMN LP and medial parts of the DMN.

DISCUSSION: Implications of the present study are highlighting the association between attachment security and brain areas related to affect regulation.

PMID:39937782 | DOI:10.1371/journal.pone.0318505

Front Netw Physiol. 2025 Jan 28;4:1491967. doi: 10.3389/fnetp.2024.1491967. eCollection 2024.

ABSTRACT

INTRODUCTION: Accurate localization of the seizure onset zone (SOZ) is critical for successful epilepsy surgery but remains challenging with current techniques. We developed a novel seizure onset network characterization tool that combines dynamic biomarkers of resting-state intracranial stereoelectroencephalography (rs-iEEG) and resting-state functional magnetic resonance imaging (rs-fMRI), vetted against surgical outcomes. This approach aims to reduce reliance on capturing seizures during invasive monitoring to pinpoint the SOZ.

METHODS: We computed the source-sink index (SSI) from rs-iEEG for all implanted regions and from rs-fMRI for regions identified as potential SOZs by noninvasive modalities. The SSI scores were evaluated in 17 pediatric drug-resistant epilepsy (DRE) patients (ages 3-15 years) by comparing outcomes classified as successful (Engel I or II) versus unsuccessful (Engel III or IV) at 1 year post-surgery.

RESULTS: Of 30 reviewed patients, 17 met the inclusion criteria. The combined dynamic index (im-DNM) integrating rs-iEEG and rs-fMRI significantly differentiated good (Engel I-II) from poor (Engel III-IV) surgical outcomes, outperforming the predictive accuracy of individual biomarkers from either modality alone.

CONCLUSION: The combined dynamic network model demonstrated superior predictive performance than standalone rs-fMRI or rs-iEEG indices.

SIGNIFICANCE: By leveraging interictal data from two complementary modalities, this combined approach has the potential to improve epilepsy surgical outcomes, increase surgical candidacy, and reduce the duration of invasive monitoring.

PMID:39936165 | PMC:PMC11811083 | DOI:10.3389/fnetp.2024.1491967

Front Neurosci. 2025 Jan 28;19:1484954. doi: 10.3389/fnins.2025.1484954. eCollection 2025.

ABSTRACT

INTRODUCTION: The Integration of functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) has allowed for a novel exploration of the brain's spatial-temporal resolution. While functional brain networks show variations in both spatial and temporal dimensions, most studies focus on fixed spatial networks that change together over time.

METHODS: In this study, for the first time, we link spatially dynamic brain networks with EEG spectral properties recorded simultaneously, which allows us to concurrently capture high spatial and temporal resolutions offered by these complementary imaging modalities. We estimated time-resolved brain networks using sliding window-based spatially constrained independent component analysis (scICA), producing resting brain networks that evolved over time at the voxel level. Next, we assessed their coupling with four time-varying EEG spectral power (delta, theta, alpha, and beta).

RESULTS: Our analysis demonstrated how the networks' volumes and their voxel-level activities vary over time and revealed significant correlations with time-varying EEG spectral power. For instance, we found a strong association between increasing volume of the primary visual network and alpha band power, consistent with our hypothesis for eyes open resting state scan. Similarly, the alpha, theta, and delta power of the Pz electrode were localized to voxel-level activities of primary visual, cerebellum, and temporal networks, respectively. We also identified a strong correlation between the primary motor network and alpha (mu rhythm) and beta activity. This is consistent with motor tasks during rest, though this remains to be tested directly.

DISCUSSION: These association between space and frequency observed during rest offer insights into the brain's spatial-temporal characteristics and enhance our understanding of both spatially varying fMRI networks and EEG band power.

PMID:39935841 | PMC:PMC11810936 | DOI:10.3389/fnins.2025.1484954

Front Neurosci. 2025 Jan 28;19:1472010. doi: 10.3389/fnins.2025.1472010. eCollection 2025.

ABSTRACT

INTRODUCTION: Type 2 diabetes mellitus (T2DM) accelerates brain aging and disrupts brain functional network connectivity, though the specific mechanisms remain unclear. This study aimed to investigate T2DM-driven alterations in brain functional network connectivity and topology.

METHODS: Eighty-five T2DM patients and 67 healthy controls (HCs) were included. All participants underwent clinical, neuropsychological, and laboratory tests, followed by MRI examinations, including resting-state functional magnetic resonance imaging (rs-fMRI) and three-dimensional high-resolution T1-weighted imaging (3D-T1WI) on a 3.0 T MRI scanner. Post-image preprocessing, brain functional networks were constructed using the Dosenbach atlas and analyzed with the DPABI-NET toolkit through graph theory.

RESULTS: In T2DM patients, functional connectivity within and between the default mode network (DMN), frontal parietal network (FPN), subcortical network (SCN), ventral attention network (VAN), somatosensory network (SMN), and visual network (VN) was significantly reduced compared to HCs. Conversely, two functional connections within the VN and between the DMN and SMN were significantly increased. Global network topology analysis showed an increased shortest path length and decreased clustering coefficient, global efficiency, and local efficiency in the T2DM group. MoCA scores were negatively correlated with the shortest path length and positively correlated with global and local efficiency in the T2DM group. Node network topology analysis indicated reduced clustering coefficient, degree centrality, eigenvector centrality, and nodal efficiency in multiple nodes in the T2DM group. MoCA scores positively correlated with clustering coefficient and nodal efficiency in the bilateral precentral gyrus in the T2DM group.

DISCUSSION: This study demonstrated significant abnormalities in connectivity and topology of large-scale brain functional networks in T2DM patients. These findings suggest that brain functional network connectivity and topology could serve as imaging biomarkers, providing insights into the underlying neuropathological processes associated with T2DM-related cognitive impairment.

PMID:39935840 | PMC:PMC11811103 | DOI:10.3389/fnins.2025.1472010

Brain Behav. 2025 Feb;15(2):e70321. doi: 10.1002/brb3.70321.

ABSTRACT

BACKGROUND: The thalamus plays a pivotal role in functional brain networks, yet its contribution to motor function recovery following stroke remains elusive. We aim to explore changes in thalamocortical functional connectivity poststroke and its correlation with motor function.

METHODS: Thirty-nine subacute ischemic stroke patients and 32 healthy individuals underwent resting-state functional magnetic resonance imaging (MRI). The Fugl-Meyer Assessment (FMA) was employed to evaluate upper and lower extremity motor function before and 1 year after stroke rehabilitation. The ipsilesional thalamus and contralesional thalamus were parceled into functional regions of interest (ROIs) based on connectivity with six cortical ROIs: prefrontal, motor, temporal, posterior parietal, somatosensory, and occipital cortex. Functional connectivity between each cortical ROI and its corresponding thalamic ROI was calculated and compared between groups. Differences identified in the ROI-to-ROI analysis were further investigated through seed-to-voxel whole-brain connectivity analyses to pinpoint thalamic dysconnectivity. Correlations with upper and lower extremity motor function were also analyzed.

RESULTS: Significant changes in thalamocortical functional connectivity were observed after stroke in ROI-to-ROI analysis, with bilateral somatosensory-thalamic connectivity decreased and ipsilesional temporal-thalamic and bilateral occipital-thalamic connectivity increased. Seed-to-voxel analysis localized ipsilesional thalamic hypoconnectivity to the ipsilesional rolandic operculum and ipsilesional precentral gyrus. Ipsilesional somatosensory-thalamic connectivity was positively correlated with baseline upper extremity FMA scores and negatively correlated with upper extremity motor function change rate at 1-year postdischarge.

CONCLUSIONS: This study provides new insights into the role of the thalamus in motor function recovery after stroke, offering preliminary evidence for its potential as a therapeutic target in poststroke rehabilitation.

PMID:39935146 | DOI:10.1002/brb3.70321

Brain Behav. 2025 Feb;15(2):e70323. doi: 10.1002/brb3.70323.

ABSTRACT

BACKGROUND AND AIMS: Adolescent major depressive disorder (MDD) is prevalent globally but often goes unnoticed due to differences in symptoms compared to adult criteria. Analyzing the brain from a network perspective provides new insights into higher-level brain functions and its pathophysiology. This study aimed to investigate changes in the topological organization of functional networks in adolescents with first-episode, treatment-naïve MDD.

METHOD: The study included 23 adolescents with depression and 27 matched healthy controls (HCs). Resting-state functional MRI (rs-fMRI) was conducted, and whole-brain functional networks were constructed. Graph theory analysis was used to evaluate network topological properties. A machine-learning multivariate diagnostic model was developed using network metrics associated with depression severity.

RESULTS: Both the MDD and HC groups displayed small-world topology, with male MDD patients showing reduced global clustering efficiency (Cp). The nodal Cp (NCp) and local efficiency (NLE) in the bilateral pallidum were significantly positively correlated with depression severity. In contrast, nodal efficiency (NE) in the left medial orbital superior frontal gyri (ORBsupmed) showed a negative correlation with disease severity. A machine-learning multivariate model using regional network topological features produced an AUROC of 0.71 (95% CI: 0.54-0.92) and an F1 score of 0.65, successfully differentiating adolescent MDD from HCs.

CONCLUSION: Our findings suggest disruptions in small-world topology in both global and local brain networks in adolescent depression. These abnormal nodal properties may serve as novel neural markers of the disorder.

PMID:39935140 | DOI:10.1002/brb3.70323

Brain Behav. 2025 Feb;15(2):e70314. doi: 10.1002/brb3.70314.

ABSTRACT

OBJECTIVE: We aimed to explore the alteration in topology and network properties in self-referential-related brain regions of individuals with depersonalization-derealization disorders (DPD), using evidence from resting-state functional magnetic resonance imaging (rs-fMRI).

METHODS: We first determined the regions of interest (ROIs) using Neurosynth, based on which we conducted an ROI-wise functional connectivity search to create a self-referential-related network and performed a topographical analysis. We then compared the analyzed properties from the rs-fMRI of disordered individuals to those of healthy controls to generate differential properties, based on which we conducted a machine learning-based disease diagnostic model.

RESULTS: The study found significant changes in connectivity between brain regions associated with self-referential processing in individuals with DPD compared to healthy controls. Correlation analysis showed negative correlations between "unreality of surroundings" and connectivity between the left inferior frontal gyrus (IFG) pars orbitalis and left insula and between "perceptual alterations" and connectivity between the left pregenual and subgenual anterior cingulate cortex (ACC). Graph theoretical analysis revealed increased local and global efficiency but decreased characteristic path length. The accuracy of the classification model was 0.885, and the area under the curve was 0.928.

CONCLUSIONS: Individuals with DPD showed alterations in brain topography and changes in network properties within self-referential-related brain regions; specifically, the changes in cortical midline structures and insula could be related to the underlying mechanism of DPD, highlighting potential targets for future research and therapeutic strategies.

PMID:39935045 | DOI:10.1002/brb3.70314

Sci Rep. 2025 Feb 11;15(1):5063. doi: 10.1038/s41598-024-82695-y.

ABSTRACT

Persistent genital arousal disorder (PGAD) is a rare disease causing high emotional distress eminently impacting the individual's quality of life. Experts in this field assume that the disease is caused by a multifaceted interplay of different etiologies which may share a common neurobiological basis. However, only one functional neuroimaging investigation exist, and a more in-depth comprehension of the neurobiological foundation is required. Therefore, this study aims to provide new insights into how the functional integration of brain regions may relate to PGAD. By using the functional magnetic resonance imaging (fMRI) technique, functional connectivity at rest (rs-FC) was compared between patients suffering PGAD (n = 26) and healthy controls (n = 26). Patients with PGAD showed different pattern in connectivity within brain structures putatively associated with the psychological and somatic dimensions of the disease including the right amygdala, left anterior cingulate cortex, right insula cortex, thalamic nuclei and prefrontal regions as seeds. The majority of these showed differences in brain connectivity pattern to the precuneus and prefrontal regions. The study offers preliminary insights into the characteristics and relevant neural mechanisms of PGAD. Nevertheless, since this study did not identify any peripheral correlates that would corroborate the interpretation of these findings, they were interpreted from a more theoretical perspective, thereby offering potential areas of focus for future research.

PMID:39934180 | DOI:10.1038/s41598-024-82695-y

Br J Anaesth. 2025 Feb 10:S0007-0912(25)00049-2. doi: 10.1016/j.bja.2024.12.036. Online ahead of print.

ABSTRACT

BACKGROUND: Ketamine, in doses suitable to induce anaesthesia in humans, gives rise to a unique state of unresponsiveness accompanied by vivid experiences and sensations, making it possible to disentangle the correlated but distinct concepts of conscious awareness and behavioural responsiveness. This distinction is often overlooked in the study of consciousness.

METHODS: The mathematical framework of connectome harmonic decomposition (CHD) was used to view functional magnetic resonance imaging (fMRI) signals during ketamine-induced unresponsiveness as distributed patterns across spatial scales. The connectome harmonic signature of this particular state was mapped onto signatures of other states of consciousness for comparison.

RESULTS: An increased prevalence of fine-grained connectome harmonics was found in fMRI signals obtained during ketamine-induced unresponsiveness, indicating higher granularity. After statistical assessment, the ketamine sedation harmonic signature showed alignment with signatures of LSD-induced (fixed effect =0.0113 [0.0099, 0.0127], P<0.001) or ketamine-induced (fixed effect =0.0087 [0.0071, 0.0103], P<0.001) psychedelic states, and misalignment with signatures seen in unconscious individuals owing to propofol sedation (fixed effect =-0.0213 [-0.0245, -0.0181], P<0.001) or brain injury (fixed effect =-0.0205 [-0.0234, -0.0178], P<0.001).

CONCLUSIONS: The CHD framework, which only requires resting-state fMRI data and can be applied retrospectively, has the ability to track alterations in conscious awareness in the absence of behavioural responsiveness on a group level. This is possible because of ketamine's unique property of decoupling these two facets, and is important for consciousness and anaesthesia research.

PMID:39933965 | DOI:10.1016/j.bja.2024.12.036

Phys Life Rev. 2025 Feb 7;52:278-297. doi: 10.1016/j.plrev.2025.02.001. Online ahead of print.

ABSTRACT

Neural oscillations facilitate the functioning of the human brain in spatial and temporal dimensions at various frequencies. These oscillations feature a universal frequency architecture that is governed by brain anatomy, ensuring frequency specificity remains invariant across different measurement techniques. Initial magnetic resonance imaging (MRI) methodology constrained functional MRI (fMRI) investigations to a singular frequency range, thereby neglecting the frequency characteristics inherent in blood oxygen level-dependent oscillations. With advancements in MRI technology, it has become feasible to decode intricate brain activities via multi-band frequency analysis (MBFA). During the past decade, the utilization of MBFA in fMRI studies has surged, unveiling frequency-dependent characteristics of spontaneous slow oscillations (SSOs) believed to base dark energy in the brain. There remains a dearth of conclusive insights and hypotheses pertaining to the properties and functionalities of SSOs in distinct bands. We surveyed the SSO MBFA studies during the past 15 years to delineate the attributes of SSOs and enlighten their correlated functions. We further proposed a model to elucidate the hierarchical organization of multi-band SSOs by integrating their function, aimed at bridging theoretical gaps and guiding future MBFA research endeavors.

PMID:39933322 | DOI:10.1016/j.plrev.2025.02.001