Most recent paper

Sci Rep. 2024 Dec 2;14(1):29960. doi: 10.1038/s41598-024-79246-w.

ABSTRACT

Fibromyalgia (FM) and ankylosing spondylitis (AS) are both rheumatic diseases characterized by significant musculoskeletal pain. In this study, we investigated the differences of the resting-state network (RSN) connectivity and gray matter volume (GMV) between FM, AS and healthy controls (HCs). We recruited 38 FM patients, 82 AS patients and 61 HCs in this study. All the participants underwent resting-state functional MRI (rs-fMRI) scans in a GE 3.0T MR system. Independent component analysis (ICA) was conducted on the rs-fMRI data, and group differences of the rsFC between different resting-state networks were calculated using dual regression. We also conducted voxel-based morphometry (VBM) analysis to investigate the differences of the GMV in FM, AS and HCs. The rsFC between the dorsal default mode network (DDMN) and the body of left caudate nucleus was significantly decreased in FM patients in comparison to AS patients (87 voxels, p = 0.025). VBM analysis showed that the GMV of the left posterior lobe of cerebellum was significantly increased in FM patients compared with AS patients (88 voxels, p = 0.036). Neither ICA nor VBM analysis revealed significant differences of RSN connectivity or GMV between FM patients and HCs. The altered rsFC between DMN and the caudate nucleus suggested an aberrant cortico-striato-thalamo-cortical circuit in FM patients, indicating aberrant reward processing, with potential association with mood, motivation and cognitive functions. The increased GMV in the left posterior lobe of cerebellum indicated the participation of cerebellum in the abnormal pain processing in FM patients.

PMID:39622846 | DOI:10.1038/s41598-024-79246-w

Eur J Psychotraumatol. 2024;15(1):2430925. doi: 10.1080/20008066.2024.2430925. Epub 2024 Dec 2.

ABSTRACT

Background: Post-traumatic stress disorder (PTSD) is increasingly prevalent in individuals with adverse childhood experiences (ACE). However, the underlying neurobiology of ACE-related PTSD remains unclear.Objective: The present study investigated the brain connectivity in ACE-related PTSD using multimodal neuroimaging data.Methods: Using a total of 119 participants with ACE (70 with ACE-related PTSD and 49 ACE-exposed controls), this study acquired T1-weighted MRI, diffusion-weighted MRI, and resting-state fMRI data to examine structural and functional connectivity between groups. Joint connectivity matrix independent component analysis (Jcm-ICA) was employed to allow shared information from all modalities to be examined and assess structural and functional connectivity differences between groups.Results: Jcm-ICA revealed distinct connectivity alterations in key brain regions involved in cognitive control, self-referential processing, and social behaviour. Compared to controls, the PTSD group exhibited functional hyperconnectivity of the right medial prefrontal cortex (PFC) of the default mode network and right inferior temporal cortex, and functional hypoconnectivity in the lateral-PFC of the central executive network and structural hypoconnectivity in white matter pathways including the right orbitofrontal region (OFC) linked to social behaviour. Post-hoc analyses using the joint brain-based information revealed that the severity of ACE, the number of traumas, and PTSD symptoms later in life significantly predicted the effects of ACE-related PTSD on the brain. Notably, no direct association between brain connectivity alterations and PTSD symptoms or the number of traumas within the PTSD group was observed.Conclusion: This study offers novel insights into the neurobiology of ACE-related PTSD using multimodal data fusion. We identified alterations in key brain networks (DMN, CEN) and OFC, suggesting potential deficits in cognitive control and social behaviour alongside heightened emotional processing in individuals with PTSD. Furthermore, our findings highlight the combined influence of ACE exposure, number of traumas experienced, and PTSD severity on brain connectivity disruptions, potentially informing future interventions.

PMID:39621357 | DOI:10.1080/20008066.2024.2430925

Front Neurosci. 2024 Nov 15;18:1452216. doi: 10.3389/fnins.2024.1452216. eCollection 2024.

ABSTRACT

Functional gastrointestinal disorders (FGIDs) are characterized by complex interactions between the gut and brain, leading to altered brain function and symptom manifestation. We used neuroimaging meta-analytic techniques in order to analyze the correlation between FGIDs and aberrant brain activity. A systematic review was performed to ascertain resting-state functional magnetic resonance imaging (rs-fMRI) studies examining brain function in FGIDs. Pooled meta-analyses by seed-based d mapping with permutation of subject images (SDM-PSI) were performed to assess variations in regional brain activity, and sensitivity analyses were applied to evaluate the robustness of findings. Meta-regression analyses were then carried out to examine possible links between demographic factors and neuroimaging changes. Our meta-analysis revealed significant changes in regional brain activities among FGIDs patients compared to healthy controls (HC). Increased brain activation was observed in several regions including the postcentral gyrus, calcarine fissure/surrounding cortex, superior frontal gyrus, and insula, while decreased activity was noted in the left posterior cingulate gyrus, right median cingulate/paracingulate gyri, and the left caudate nucleus. Furthermore, meta-regression analyses indicated negative associations between disease duration and alterations in specific brain regions. These findings underscored the intricate interplay between gut dysfunction and aberrant brain activity in FGIDs. Early intervention and multidisciplinary approaches addressing both gastrointestinal symptoms and associated emotional distress are crucial for improving the quality of life of the patients.

PMID:39618709 | PMC:PMC11604809 | DOI:10.3389/fnins.2024.1452216

BMC Psychiatry. 2024 Nov 30;24(1):862. doi: 10.1186/s12888-024-06331-9.

ABSTRACT

BACKGROUND: Studies have confirmed brain network topology disruption in schizophrenia (SZ). Electroconvulsive therapy (ECT) rapidly improves acute psychiatric symptoms, yet the exact mechanism by which it impacts brain network topology in SZ patients remains unclear. This study aims to explore topological changes in SZ patients' whole-brain functional networks during ECT, ultimately elucidating implicated neurological mechanisms.

METHODS: This study collected resting-state functional magnetic resonance imaging (rs-fMRI) data from 53 patients with schizophrenia before and after ECT, as well as data from 46 age-, gender-, and education-matched healthy control participants (HC). Using the Brainnetome Atlas, brain functional networks were constructed for each participant. Graph theory methods were applied to measure global and nodal topological properties. Clinical symptoms of patients were assessed using the Positive And Negative Syndrome Scale (PANSS). Independent sample t-tests were employed to compare topological properties between patients and healthy controls, while paired t-tests were used to assess before and after ECT differences within the patient group. Finally, partial correlation analyses were conducted to examine the relationship between changes in topological properties and changes in PANSS scores among patients before and after ECT.

RESULTS: Before ECT, compared to the HC group, the patient group demonstrated reduced local efficiency (Eloc) and clustering coefficient (Cp). In the right superior temporal gyrus, degree centrality (Dc) and nodal global efficiency (Ne) were lower, whereas in the left cingulate gyrus, Ne and Dc were higher. Following ECT, Eloc and Cp normalized in the patient group. Additionally, nodal local efficiency (NLe) and nodal clustering coefficient (NCp) increased in the bilateral superior frontal gyrus. Conversely, in the left inferior parietal lobule, Ne and Dc decreased, and nodal shortest path length (NLp) increased. Both NLe and NCp were lower in the bilateral lateral occipital cortex, both before and after ECT. However, no significant correlation was observed between changes in PANSS scores and alterations in global and nodal topological properties before and after ECT treatment.

CONCLUSIONS: Our study suggests that ECT may improve psychiatric symptoms by modulating the integration and dissociation functions within damaged brain networks in SZ patients. Specifically, the balance between the integration and dissociation functions of the default mode network (DMN), central executive network (CEN), and auditory networks (AN) may play a crucial role in the improvement of psychiatric symptoms.

PMID:39616308 | PMC:PMC11607971 | DOI:10.1186/s12888-024-06331-9

Brain Imaging Behav. 2024 Nov 30. doi: 10.1007/s11682-024-00960-0. Online ahead of print.

ABSTRACT

Major depressive disorder (MDD) is a pervasive mental disorder that significantly impairs functional capabilities, underscoring the necessity for precise stratification of its severity to facilitate tailored treatment. This study investigated the utility of voxel-mirrored homotopic connectivity (VMHC) derived from resting-state functional magnetic resonance imaging (fMRI) data as a neuroimaging biomarker to differentiate varying severities of MDD in a sample drawn from the REST-meta-MDD project, which included 392 first-episode MDD patients and 440 healthy controls (HC) from 9 sites. Patients were classified into mild to moderate and severe depression groups according to the 17-item Hamilton Depression Scale (HAMD) scores. VMHC differences between these subgroups and their associations with HAMD scores were further examined. The results revealed significant reductions in VMHC within the fusiform gyrus for patients with mild to moderate depression compared to HCs, alongside more extensive reductions across the insula, postcentral gyrus, and angular gyrus in severe depression. Notably, increased VMHC in the middle cingulate cortex was identified in severe MDD patients relative to those with mild to moderate depression, with this increase showed a significant positive correlation with the HAMD scores. Additionally, receiver operating characteristic (ROC) curve analysis demonstrated that VMHC values in these regions effectively differentiate patients from HCs and across varying severities of MDD. These findings suggest that VMHC could serve as a valuable metric for clinical diagnosis and the stratification of depression severity, providing insights into the underlying neurobiological mechanisms associated with the disorder.

PMID:39614038 | DOI:10.1007/s11682-024-00960-0

Neuroimage Clin. 2024 Nov 26;45:103717. doi: 10.1016/j.nicl.2024.103717. Online ahead of print.

ABSTRACT

OBJECTIVE: Atypical major depressive disorder (MDD) is a distinct subtype of MDD, characterized by increased appetite and/or weight gain, excessive sleep, leaden paralysis, and interpersonal rejection sensitivity. Delineating different neural circuits associated with atypical and typical MDD would better inform clinical personalized interventions.

METHODS: Using resting-state fMRI, we investigated the voxel-level regional homogeneity (ReHo) and functional connectivity (FC) in 55 patients with atypical MDD, 51 patients with typical MDD, and 49 healthy controls (HCs). Support vector machine (SVM) approaches were applied to examine the validity of the findings in distinguishing the two types of MDD.

RESULTS: Compared to patients with typical MDD and HCs, patients with atypical MDD had increased ReHo values in the right lateral orbitofrontal cortex (OFC) and enhanced FC between the right lateral OFC and right dorsolateral prefrontal cortex (dlPFC), and between the right striatum and left OFC. The ReHo in the right lateral OFC and the significant FCs found were significantly correlated with body mass index (BMI) in all groups of participants with MDD. The connectivity of the right striatum and left OFC was positively correlated with the retardation scores in the atypical MDD group. Using the ReHo of the right lateral OFC as a feature, we achieved 76.42% accuracy to differentiate atypical MDD from typical MDD.

CONCLUSION: Our findings show that atypical MDD might be associated with altered OFC activity and connectivity. Furthermore, our findings highlight the key role of lateral OFC in atypical MDD, which may provide valuable information for future personalized interventions.

PMID:39613493 | DOI:10.1016/j.nicl.2024.103717

Sleep Med. 2024 Nov 22;125:155-167. doi: 10.1016/j.sleep.2024.11.026. Online ahead of print.

ABSTRACT

BACKGROUND: Sleep deprivation(SD) is known to impair cognitive function and emotional regulation, however, its specific effects on the functional connectivity of visual-related brain regions remain unclear.

OBJECTIVES: This study aimed to investigate the impact of 36-h acute sleep deprivation on functional connectivity in visual neural circuits and its relationship with cognitive and emotional changes.

METHODS: Sixty healthy male participants were assessed before and after 36 h of sleep deprivation using resting-state fMRI, the Psychomotor Vigilance Task (PVT), the Epworth Sleepiness Scale (ESS), and the Profile of Mood States (POMS). Functional connectivity changes were analyzed using paired t-tests and False Discovery Rate (FDR) correction.

KEY RESULTS: Sleep deprivation significantly altered functional connectivity between the prefrontal cortex, hippocampus, and visual processing regions. These changes correlated with slower PVT reaction times, increased subjective sleepiness (ESS), and emotional disturbances (POMS), including heightened tension and reduced self-esteem.

CONCLUSIONS: The findings suggest that acute sleep deprivation impairs cognitive performance and emotional regulation by changing functional connectivity in key brain regions. These results may strengthen our understanding of neurobiology of SD and its potential negative effects.

PMID:39612715 | DOI:10.1016/j.sleep.2024.11.026

J Neurosci Res. 2024 Dec;102(12):e70001. doi: 10.1002/jnr.70001.

ABSTRACT

The interaction between major depressive disorder (MDD) and overweight/obesity has received considerable attention owing to its widespread occurrence and the intricate biopsychological implications involved. Despite extensive research, the neural mechanisms underlying these comorbid conditions, particularly in terms of functional network connectivity (FNC), are still not well understood. This study aimed to clarify these mechanisms by utilizing resting-state functional magnetic resonance imaging (rs-fMRI) to examine both static and dynamic FNC. We analyzed data from 57 patients with both MDD and overweight/obesity (MDD-OW), 57 MDD patients of normal weight (MDD-NW), and 44 healthy controls, using techniques such as independent component analysis, sliding window analysis, K-means clustering, and graph theory. In contrast to static FNC, which showed no significant differences, dynamic FNC analysis identified four consistent states across all participants. Both MDD groups demonstrated reduced flexibility in functional coordination among these states and decreased nodal characteristics within the salience network. Notably, the MDD-OW group displayed enhanced dynamic FNC between the default mode network (DMN) and the executive control network (ECN) during certain states, which was inversely associated with the severity of depressive symptoms. These results highlight the importance of altered dynamic connectivity patterns in individuals with MDD and concurrent overweight/obesity, especially between the DMN and ECN, suggesting their potential utility as biomarkers for depressive states. This research contributes to our understanding of how comorbid overweight/obesity affects brain network dynamics in depressive disorders and provides a basis for targeted therapeutic strategies.

PMID:39611284 | DOI:10.1002/jnr.70001

J Neurosci Methods. 2024 Nov 26:110322. doi: 10.1016/j.jneumeth.2024.110322. Online ahead of print.

ABSTRACT

BACKGROUND: Predicting future brain health is a complex endeavor that often requires integrating diverse data sources. The neural patterns and interactions identified through neuroimaging serve as the fundamental basis and early indicators that precede the manifestation of observable behaviors or psychological states.

NEW METHOD: In this work, we introduce a multimodal predictive modeling approach that leverages an imaging-informed methodology to gain insights into future behavioral outcomes. We employed three methodologies for evaluation: an assessment-only approach using support vector regression (SVR), a neuroimaging-only approach using random forest (RF), and an image-assisted method integrating the static functional network connectivity (sFNC) matrix from resting-state functional magnetic resonance imaging (rs-fMRI) alongside assessments. The image-assisted approach utilized a partially conditional variational autoencoder (PCVAE) to predict brain health constructs in future visits from the behavioral data alone.

RESULTS: Our performance evaluation indicates that the image-assisted method excels in handling conditional information to predict brain health constructs in subsequent visits and their longitudinal changes. These results suggest that during the training stage, the PCVAE model effectively captures relevant information from neuroimaging data, thereby potentially improving accuracy in making future predictions using only assessment data.

COMPARISON WITH EXISTING METHODS: The proposed image-assisted method outperforms traditional assessment-only and neuroimaging-only approaches by effectively integrating neuroimaging data with assessment factors.

CONCLUSION: This study underscores the potential of neuroimaging-informed predictive modeling to advance our comprehension of the complex relationships between cognitive performance and neural connectivity.

PMID:39608579 | DOI:10.1016/j.jneumeth.2024.110322

Neuroimage Clin. 2024 Nov 26;45:103715. doi: 10.1016/j.nicl.2024.103715. Online ahead of print.

ABSTRACT

BACKGROUND: Although intermittent theta burst stimulation (iTBS) has shown effectiveness in addressing working memory (WM) deficits in individuals with schizophrenia (SZ), the current body of evidence is limited and the specific mechanisms involved remain unclear. Therefore, this pilot fMRI study aimed to examine the efficacy of parietal iTBS in ameliorating WM impairments and explore its influence on the resting-state effective connectivity within the frontoparietal network in patients with SZ.

METHOD: A total of 48 patients diagnosed with SZ were randomly assigned to an active or sham iTBS group and underwent 20 sessions of active or sham iTBS over 4 weeks. Subsequently, all patients underwent cognitive tests, clinical symptom assessments, and resting-state functional MRI (rs-fMRI) scans. The effective connectivity between the frontal and parietal brain regions during the rs-fMRI scans was analyzed using a spectral dynamic causal modeling approach. Additionally, this trial was registered at the Chinese Clinical Trial Registry in November 2022 (registry number: ChiCTR2200057286).

RESULTS: iTBS treatment improved the positive symptoms, negative symptoms, general psychopathology, and WM deficits. Following the iTBS intervention, the active group demonstrated a significant increase in connectivity strengths from the right MFG to the right SPL (p = 0.031) and from the left SPL to the left MFG (p = 0.010) compared to the pre-treatment levels. Additionally, compared to the sham group, the active group displayed a significantly higher connectivity strength from the right MFG to the right SPL (p = 0.042) after iTBS treatment.

CONCLUSION: All these findings suggest that iTBS targeting the parietal region may influence the resting-state effective connectivity within the frontoparietal network, thereby offering promising therapeutic implications for alleviating the cognitive deficits in SZ.

PMID:39608227 | DOI:10.1016/j.nicl.2024.103715

Eur J Nutr. 2024 Nov 28;64(1):34. doi: 10.1007/s00394-024-03548-y.

ABSTRACT

PURPOSE: Healthy diets are believed to be associated with a reduced risk of experiencing common mental disorders (CMDs) and related symptomatology (such as ruminative thinking), and with healthier brain chemistry and structure, especially in the frontal regions implicated in CMDs, cognitive control, and food choice. Nevertheless, there is very limited research on the relationship between diet health/quality and brain function. In this study we assessed the associations between adherence to the Mediterranean diet and resting state functional connectivity (rs-FC) of the prefrontal cortex (PFC) with the whole brain and whether this connectivity would be associated with ruminative thinking as a transdiagnostic factor for CMDs.

METHODS: Thirty-seven adults (Mean Age = 25.57, SD = 7.18) completed the Mediterranean Diet Adherence Screener (MEDAS) and were classified into high- and low-quality diet groups and completed the Ruminative Response Scale. All participants underwent resting-state functional MRI (fMRI) to determine whole-brain rs-FC of the medial prefrontal cortex (mPFC).

RESULTS: Participants in the high MEDAS group (vs. low MEDAS group) exhibited significantly greater rs-FC of the mPFC seed with the thalamus, caudate and putamen. Additionally, the strength of rs-FC of the mPFC seed with these regions was positively associated with the MEDAS scores across groups in both crude and adjusted models. There were no significant associations between the strength of rs-FC of the mPFC seed with the cluster of voxels with the thalamus, caudate, and putamen and ruminative thinking.

DISCUSSION: This work shows that healthy dietary patterns are associated with rs-FC in the frontal-subcortical circuitry in healthy volunteers. Considering the implications of the dysregulation of this circuity, adhering to healthy dietary patterns may offer a promising alternative/complementary method to improve CMDs, cognitive control, and food choices.

PMID:39607478 | DOI:10.1007/s00394-024-03548-y

Brain Commun. 2024 Nov 21;6(6):fcae411. doi: 10.1093/braincomms/fcae411. eCollection 2024.

ABSTRACT

Spinal cord injury (SCI) below-level neuropathic pain is a difficult condition to treat both pharmacologically and surgically. Successful treatment using surgically created lesions of the spinal cord dorsal root entry zone (DREZ), guided by intramedullary monitoring of neuronal electrical hyperactivity, has shown that DREZs both cephalad and caudal to the level of injury can be the primary generators of SCI below-level pain. Below-level pain perception follows a unique somatotopic map of DREZ pain generators, and neuronal transmission to brain pain centres can occur primarily through sympathetic nervous system (SNS) pathways. This study evaluated changes in brain resting-state and task-based functional magnetic resonance imaging responses before and after neuroelectrically guided DREZ microcoagulation surgery. Eight persons with clinically complete SCI who suffered chronic, severe and unrelenting below-level neuropathic pain refractory to all pharmacological management were investigated before and after the surgical intervention. Baseline differences between DREZ subjects, group-matched low pain SCI and healthy controls were observed in medial primary somatosensory and motor cortex connectivity to the hippocampus, amygdala and medial prefrontal cortex. The DREZ surgery led to short-term (12 days) almost complete pain relief in all participants and long-term (1+ year) pain relief in all participants receiving DREZ lesioning both cephalad and caudal to the level of injury (six out of eight participants). Follow-up 12 days post-operatively indicated that DREZ surgery normalized prior negative functional coupling between primary sensory (S1) and motor (M1) cortices to the hippocampus, amygdala and the medial prefrontal cortex, increased M1 to putamen and amygdala connectivity and decreased limbic to cerebellar connectivity. DREZ hyperactivity was found both cephalad and caudal to the level of injury. The regional distribution of hyperactive regions corresponded not to classical dermatomes but rather mapped on to intermediolateral (IML) cell column end organ innervation of body regions of below-level pain perception, consistent with a non-classical SNS-mediated somatotopic map of DREZ below-level pain generators. The results indicate that neuroelectrically guided DREZ microcoagulation alters a medial prefrontal-somatosensory-limbic network that is separate from classical pain pathways. This provides further evidence that below-level SCI pain originates in hyperactive DREZs and can be relayed to the brain via the SNS.

PMID:39605971 | PMC:PMC11601164 | DOI:10.1093/braincomms/fcae411

Front Nucl Med. 2024 Nov 13;4:1332747. doi: 10.3389/fnume.2024.1332747. eCollection 2024.

ABSTRACT

INTRODUCTION: Neural fingerprinting is a technique used to identify individuals based on their unique brain activity patterns. While deep learning techniques have been demonstrated to outperform traditional correlation-based methods, they often require retraining to accommodate new subjects. Furthermore, the limited availability of samples in neuroscience research can impede the quick adoption of deep learning methods, presenting a challenge for their broader application in neural fingerprinting.

METHODS: This study addresses these challenges by using contrastive learning to eliminate the need for retraining with new subjects and developing a data augmentation methodology to enhance model robustness in limited sample size conditions. We utilized the LEMON dataset, comprising 3 Tesla MRI and resting-state fMRI scans from 138 subjects, to compute functional connectivity as a baseline for fingerprinting performance based on correlation metrics. We adapted a recent deep learning model by incorporating data augmentation with short random temporal segments for training and reformulated the fingerprinting task as a contrastive problem, comparing the efficacy of contrastive triplet loss against conventional cross-entropy loss.

RESULTS: The results of this study confirm that deep learning methods can significantly improve fingerprinting performance over correlation-based methods, achieving an accuracy of about 98% in identifying a single subject out of 138 subjects utilizing 39 different functional connectivity profiles.

DISCUSSION: The contrastive method showed added value in the "leave subject out" scenario, demonstrating flexibility comparable to correlation-based methods and robustness across different data sizes. These findings suggest that contrastive learning and data augmentation offer a scalable solution for neural fingerprinting, even with limited sample sizes.

PMID:39605927 | PMC:PMC11598699 | DOI:10.3389/fnume.2024.1332747

bioRxiv [Preprint]. 2024 Nov 13:2023.09.23.559145. doi: 10.1101/2023.09.23.559145.

ABSTRACT

Psychedelics hold therapeutic promise for mood disorders due to rapid, sustained results. Human neuroimaging studies have reported dramatic serotonin-2A receptor-(5-HT2AR)-dependent changes in functional brain reorganization that presumably reflect neuromodulation. However, the potent vasoactive effects of serotonin have been overlooked. We found psilocybin-mediated alterations to fMRI-HRFs in humans, suggesting potentially altered NVC. To assess the neuronal, hemodynamic, and neurovascular coupling (NVC) effects of the psychedelic 5-HT2AR agonist, 2,5-Dimethoxy-4-iodoamphetamine (DOI), wide-field optical imaging (WFOI) was used in awake Thy1-jRGECO1a mice during stimulus-evoked and resting-state conditions. While DOI partially altered tasked-based NVC, more pronounced NVC alterations occurred under resting-state conditions and were strongest in association regions. Further, calcium and hemodynamic activity reported different accounts of RSFC changes under DOI. Co-administration of DOI and the 5-HT2AR antagonist, MDL100907, reversed many of these effects. Dissociation between neuronal and hemodynamic signals emphasizes a need to consider neurovascular effects of psychedelics when interpreting blood-oxygenation-dependent neuroimaging measures.

PMID:39605498 | PMC:PMC11601243 | DOI:10.1101/2023.09.23.559145

Sci Rep. 2024 Nov 27;14(1):29453. doi: 10.1038/s41598-024-80294-5.

ABSTRACT

Major Depressive Disorder (MDD) is a common mental disorder characterized by cognitive impairment, and its pathophysiology remains to be explored. In this study, we aimed to explore the efficacy of brain network topological properties (TPs) in identifying MDD patients, revealing variational brain regions with efficient TPs. Functional connectivity (FC) networks were constructed from resting-state functional magnetic resonance imaging (rs-fMRI). Small-worldness did not exhibit significant variations in MDD patients. Subsequently, two-sample t-tests were employed to screen FC and reconstruct the network. The discriminative ability of TPs between MDD patients and healthy controls was analyzed using receiver operating characteristic (ROC), ROC analysis showed the small-worldness of binary reconstructed FC network (p < 0.05) was reduced in MDD patients, with area under the curve (AUC) of local efficiency (Le) and clustering coefficient (Cp) as sample features having AUC of 0.6351 and 0.6347 respectively being optimal. The AUC of Le and Cp for retained brain regions by T-test (p < 0.05) were 0.6795 and 0.6956 respectively. Further, support vector machine (SVM) model assessed the effectiveness of TPs in identifying MDD patients, and it identified the Le and Cp in brain regions selected by the least absolute shrinkage and selection operator (LASSO), with average accuracy from leave-one-site-out cross-validation being 62.03% and 61.44%. Additionally, shapley additive explanations (SHAP) was employed to elucidate variations in TPs across brain regions, revealing that predominant variations among MDD patients occurred within the default mode network. These results reveal efficient TPs that can provide empirical evidence for utilizing nodal TPs as effective inputs for deep learning on graph structures, contributing to understanding the pathological mechanisms of MDD.

PMID:39604455 | DOI:10.1038/s41598-024-80294-5

Biol Psychiatry Cogn Neurosci Neuroimaging. 2024 Nov 25:S2451-9022(24)00347-1. doi: 10.1016/j.bpsc.2024.11.010. Online ahead of print.

ABSTRACT

BACKGROUND: Modafinil is primarily employed to treat narcolepsy but also as an off-label cognitive enhancer. Functional Magnetic Resonance Imaging (fMRI) studies indicate that modafinil modulates the connectivity of neocortical networks primarily involved in attention and executive functions. However, much less is known about the drug's effects on subcortical structures. Following preliminary findings, we evaluated modafinil's activity on the connectivity of distinct cerebellar regions with the neocortex. We assessed the spatial relationship of these effects with the expression of neurotransmitter receptors/transporters.

METHODS: Patterns of resting-state fMRI (rs-fMRI) connectivity were estimated in 50 participants from scans acquired pre- and post-administration of a single (100 mg) dose of modafinil (n=25) or placebo (n=25). Using specific cerebellar regions as seeds for voxel-wise analyses, we examined modafinil's modulation on cerebellar-neocortical connectivity. Next, we conducted a quantitative evaluation of the spatial overlap between the modulation of cerebellar-neocortical connectivity and the expression of neurotransmitter receptors/transporters obtained by publicly available databases.

RESULTS: Modafinil increased the connectivity of Crus I and Vermis IX with prefrontal regions. Crus I connectivity changes were associated with the expression of dopaminergic D2 receptors. The Vermis I-II showed enhanced coupling with the dorsal anterior cingulate cortex and matched the expression of histaminergic H3 receptors. The Vermis VII-VIII displayed increased connectivity with the visual cortex, an activity associated with dopaminergic and histaminergic neurotransmission.

CONCLUSION: Our study reveals modafinil's modulatory effects on cerebellar-neocortical connectivity. The modulation mainly involves Crus I and the Vermis and spatially overlaps the distribution of dopaminergic and histaminergic receptors and serotonin transporters.

PMID:39603413 | DOI:10.1016/j.bpsc.2024.11.010

Neuroscience. 2024 Nov 25:S0306-4522(24)00648-1. doi: 10.1016/j.neuroscience.2024.11.060. Online ahead of print.

ABSTRACT

Heart failure (HF) frequently suffers from brain abnormalities and cognitive impairments. This study aims to investigate brain structure and function alteration in patients with chronic HF. This retrospective study included 49 chronic HF and 49 health controls (HCs). Voxel-based morphometry was conducted on structural MRI to quantify gray matter volume (GMV), and functional connectivity (FC) was assessed with seed-based analysis using resting-state fMRI. White matter microstructure integrity was also evaluated through tract-based spatial statistics employing DTI. Correlations between multimodal MRI features and cognitive performance were further investigated in patients with chronic HF. Patients with chronic HF exhibited significantly reduced regional GMV, white matter microstructure injury (Family wise error correction, p＜0.05), and decreased FC in multiple brain regions involved in cognition, sensorimotor, visual function (Gaussian random field correction, voxel level p＜0.0001 and cluster-level p＜0.01). There was no observed increases in GMV or FC compared with HCs. Decreased GMV showed positive correlations with cognitive performance (r = 0.025-0.577, p = 0.025-0.001), while decreased fractional anisotropy was negatively correlated with anxiety scores (r = -0.339, p = 0.040) in patients with chronic HF. This study revealed that patients with chronic HF exhibited brain structure injury affecting gray matter and white matter, as well as FC abnormalities of brain regions responsible for cognition, sensorimotor and visual function. These findings suggest GMV could serve as a neuroimaging biomarker for cognitive impairments and a potential target for neuroprotective therapies in patients with chronic HF.

PMID:39603406 | DOI:10.1016/j.neuroscience.2024.11.060

Front Neurosci. 2024 Nov 12;18:1488193. doi: 10.3389/fnins.2024.1488193. eCollection 2024.

ABSTRACT

INTRODUCTION: Right-to-left shunting has been significantly associated with migraine, although the neural mechanisms remain complex and not fully elucidated. The aim of this study was to investigate the variability of brain asymmetry in individuals with migraine with right-to-left shunting, migraine without right-to-left shunting and normal controls using resting-state fMRI technology and to construct a three-classification model.

METHODS: Firstly, asymmetries in functional connectivity and brain network topology were quantified to laterality indices. Secondly, the laterality indices were employed to construct a three-classification model using decision tree and random forest algorithms. Ultimately, through a feature score analysis, the key brain regions that contributed significantly to the classification were extracted, and the associations between these brain regions and clinical features were investigated.

RESULTS: Our experimental results showed that the initial classification accuracy reached 0.8961. Subsequently, validation using an independent sample set resulted in a classification accuracy of 0.8874. Further, after expanding the samples by the segmentation strategy, the classification accuracies were improved to 0.9103 and 0.9099. Additionally, the third sample set yielded a classification accuracy of 0.8745. Finally, 9 pivotal brain regions were identified and distributed in the default network, the control network, the visual network, the limbic network, the somatomotor network and the salience/ventral attention network.

DISCUSSION: The results revealed distinct lateralization features in the brains of the three groups, which were closely linked to migraine and right-to-left shunting symptoms and could serve as potential imaging biomarkers for clinical diagnosis. Our findings enhanced our understanding of migraine and right-to-left shunting mechanisms and offered insights into assisting clinical diagnosis.

PMID:39600655 | PMC:PMC11588730 | DOI:10.3389/fnins.2024.1488193

J Pain Res. 2024 Nov 22;17:3959-3971. doi: 10.2147/JPR.S470750. eCollection 2024.

ABSTRACT

OBJECTIVE: This study investigates the differences in brain functional activity and connectivity patterns between Cancer Pain (CP) patients and Healthy Controls (HCs) using resting-state functional magnetic resonance imaging (rs-fMRI) to identify potential neuroimaging biomarkers.

METHODS: This study collected rs-fMRI data from 25 CP patients and 25 hCs, processed the functional MRI images, and calculated metrics such as amplitude of low-frequency fluctuation (ALFF), Regional Homogeneity (ReHo), and FC. Through statistical analysis, differences in brain functional activity and connectivity between the cancer pain group and the healthy control group were investigated, followed by machine learning classification.

RESULTS: The results showed that compared to the normal group, reductions in the ALFF were primarily observed in the bilateral inferior temporal gyrus; ReHo increased in the right middle temporal gyrus and decreased in the left cerebellum Crus2. Using the statistically different brain areas as seed points to construct FC networks and performing statistical analysis, it was found that the regions with decreased FC connection strength between the cancer pain group and the normal group were mainly in the prefrontal cortex (PFC), the postcentral gyrus of the parietal lobe, and the cerebellum. Statistical results indicated that there was no significant correlation between pain scores (Numeric Rating Scale, NRS) and neuroimaging metrics. According to the machine learning classification, the FC features of the right precentral gyrus achieved higher diagnostic efficacy (AUC = 0.804) compared to ALFF and ReHo in distinguishing between CP patients and HCs.

CONCLUSION: Brain activity and FC in CP patients show abnormalities in regions such as the inferior temporal gyrus, middle temporal gyrus, prefrontal cortex, parietal lobe, and cerebellum. These areas may be interconnected through neural networks and jointly participate in functions related to pain perception, emotion regulation, cognitive processing, and motor control. However, the precise connections and mechanisms of action require further research.

PMID:39600396 | PMC:PMC11590652 | DOI:10.2147/JPR.S470750

Brain Sci. 2024 Nov 5;14(11):1122. doi: 10.3390/brainsci14111122.

ABSTRACT

Background: Previous studies have demonstrated that mind wandering during incubation phases enhances post-incubation creative performance. Recent empirical evidence, however, has highlighted a specific form of mind wandering closely related to creativity, termed freely moving mind wandering (FMMW). In this study, we examined the behavioral and neural associations between FMMW and creativity. Methods: We initially validated a questionnaire measuring FMMW by comparing its results with those from the Sustained Attention to Response Task (SART). Data were collected from 1316 participants who completed resting-state fMRI scans, the FMMW questionnaire, and creative tasks. Correlation analysis and Bayes factors indicated that FMMW was associated with creative thinking (AUT). To elucidate the neural mechanisms underlying the relationship between FMMW and creativity, Hidden Markov Models (HMM) were employed to analyze the temporal dynamics of the resting-state fMRI data. Results: Our findings indicated that brain dynamics associated with FMMW involve integration within multiple networks and between networks (r = -0.11, pFDR < 0.05). The links between brain dynamics associated with FMMW and creativity were mediated by FMMW (c' = 0.01, [-0.0181, -0.0029]). Conclusions: These findings demonstrate the relationship between FMMW and creativity, offering insights into the neural mechanisms underpinning this relationship.

PMID:39595885 | DOI:10.3390/brainsci14111122