Most recent paper

Sci Rep. 2024 Dec 3;14(1):30025. doi: 10.1038/s41598-024-81102-w.

ABSTRACT

Non-invasive neuromodulation represents a major opportunity for brain interventions, and transcranial focused ultrasound (FUS) is one of the most promising approaches. However, some challenges prevent the community from fully understanding its outcomes. We aimed to address one of them and unravel the temporal dynamics of FUS effects in humans. Twenty-two healthy volunteers participated in the study. Eleven received FUS in the right inferior frontal cortex while the other 11 were stimulated in the right thalamus. Using a temporal dynamic approach, we compared resting-state fMRI seed-based functional connectivity obtained before and after FUS. We also assessed behavioural changes as measured with a task of reactive motor inhibition. Our findings reveal that the effects of FUS are predominantly time-constrained and spatially distributed in brain regions functionally connected with the directly stimulated area. In addition, mediation analysis highlighted that FUS applied in the right inferior cortex was associated with behavioural alterations which was directly explained by the applied acoustic pressure and the brain functional connectivity change we observed. Our study underscored that the biological effects of FUS are indicative of behavioural changes observed more than an hour following stimulation and are directly related to the applied acoustic pressure.

PMID:39627315 | DOI:10.1038/s41598-024-81102-w

Brain Topogr. 2024 Dec 3;38(1):18. doi: 10.1007/s10548-024-01094-8.

ABSTRACT

Human beings exhibit varying risk-taking behaviors in response to different risk levels. Despite numerous studies on risk-taking in decision-making, the neural mechanisms of decision-making regarding risk levels remains unclear. To investigate the neural correlates of individual differences in risk-taking under different risk-levels, we analyzed behavioral data of the Balloon Analogue Risk Task (BART) and resting-state functional Magnetic Resonance Imaging (rs-fMRI) data of healthy participants (22-39 years, N = 93) from the University of California, Los Angeles Consortium for Neuropsychiatric Phenomics dataset. In the BART, the participants decided to pump for more points or stop pumping to avoid explosion of the balloons, where the risk level was manipulated by the explosion likelihood which was distinguished by the balloon color (blue for low-, red for high- risk condition). Compared with low-risk condition, the participants pumped less number, exploded more balloons, and showed more variability in pump numbers in high-risk condition, demonstrating the effective manipulation of the risky level. Next, resting state features and functional connectivity (rsFC) strength were associated with behavioral measures in low- and high-risk conditions. We found that the explosion number of balloons were correlated with the low frequency fluctuations (ALFF) in the left dorsolateral prefrontal cortex (L. DLPFC), the rsFC strength between L. DLPFC and the left anterior orbital gyrus in the low-risk condition. In the high-risk condition, we found variability in pump numbers was correlated with the ALFF in the left middle/superior frontal gyrus, the fractional ALFF (fALFF) in the medial segment of precentral gyrus (M. PrG), and the rsFC strength between the M. PrG and bilateral precentral gyrus. Our results highlighted significance of the L. DLPFC in lower risky decision making and the precentral gyrus in higher risky decision making, suggesting that distinctive neural correlates underlie the individual differences of decision-making under different risk level.

PMID:39625684 | DOI:10.1007/s10548-024-01094-8

Brain Imaging Behav. 2024 Dec 3. doi: 10.1007/s11682-024-00959-7. Online ahead of print.

ABSTRACT

The study aimed to examine alterations in surface-based amplitude of low-frequency fluctuations (ALFF) and fractional amplitude of low-frequency fluctuations (fALFF) in primary open-angle glaucoma (POAG) patients using resting-state functional magnetic resonance imaging (rs-fMRI), and to investigate their relationships with visual function and molecular profiling. A total of 70 POAG patients and 45 age- and sex-matched healthy controls (HCs) underwent rs-fMRI scans. The differences between POAG and HCs groups were compared by two-sample t-test. Spearman's correlation analyses assessed the relationship between ALFF/fALFF values and ophthalmic parameters. Spatial correlation analysis of the patients-control difference map with brain imaging data further explores underlying neurobiological mechanisms. POAG patients displayed altered brain activity compared to HCs, including decreased ALFF/fALFF in the visual network and increased in the frontoparietal and default mode networks. They exhibited reduced fALFF in the somatomotor network and increased ALFF in the dorsal and ventral attention networks. These changes are linked to neurotransmitter systems, with fALFF particularly associated with the dopamine system. Moreover, the altered ALFF/fALFF in brain regions related to vision and attention - the occipital lobe, temporal lobe, parietal lobe, paracentral lobule, and frontal lobe correlated with ophthalmic examination parameters. Surface-based ALFF/fALFF in POAG decreased in visual processing regions and increased in brain regions related to cognitive control, working memory, and attention. These changes were linked to neurotransmitter distributions important for emotional stability and mental health, potentially informing treatment approaches for POAG patients.

PMID:39625606 | DOI:10.1007/s11682-024-00959-7

Stress Health. 2024 Dec 3:e3515. doi: 10.1002/smi.3515. Online ahead of print.

ABSTRACT

Depression is one of the most common mental disorders. Perceived stress is a significant trigger and has adverse effects on depression. The complex longitudinal relationship between perceived stress and depression at the symptom level has significant implications for clinical intervention but is understudied. In our study, 823 students (67% female, median age 20.38, IQR 19.42-21.43) from a university in Tianjin were randomly sampled and completed measures of PHQ-9 and PSS-10, while 393 (65% female, median age 20.42, IQR 19.46-21.45) were followed up at three points, six months apart. The longitudinal relationships were estimated using cross-lagged modelling and cross-lagged panel network modelling. Among them, 49 students (59% female, median age 19.48, IQR 18.76-20.12) participated in resting-state functional magnetic resonance imaging (fMRI) scans. Cross-lagged analyses showed that depression and perceived stress predicted each other at the global level. At the dimensional level, depression and perceived helplessness were mutually predictive, while depression and perceived coping did not. In the cross-lagged panel network analyses, we identified symptoms in the top 20% of Bridge Expected Influence as bridging symptoms, specifically 'Guilt' (PHQ6) and 'Felt nervous and stressed' (PSS3). Notably, 'guilt' consistently demonstrated the highest Bridge Expected Influence across all time points and showed the strongest predictive power for perceived stress. We found that fALFF in the left superior frontal gyrus (SFG) mediated the association between "guilt" and perceived stress. Our findings elucidate the bidirectional relationship between symptoms of depression and perceived stress, identifying guilt is the most critical symptom of depression for the followed perceived stress, with SFG activity mediating this association.

PMID:39624971 | DOI:10.1002/smi.3515

Front Aging Neurosci. 2024 Nov 18;16:1467054. doi: 10.3389/fnagi.2024.1467054. eCollection 2024.

ABSTRACT

AIM: Mild cognitive impairment (MCI) includes two distinct subtypes, namely progressive MCI (pMCI) and stable MCI (sMCI). The objective of this study was to identify the topological reorganization of brain functional networks in patients with pMCI and sMCI.

METHODS: Resting-state functional magnetic resonance imaging (rs-fMRI) was applied to patients with pMCI, sMCI and healthy controls. Graph theory was applied to study the topological characteristics of the brain's functional networks, examining global and nodal metrics, modularity, and rich-club organization. Analysis of covariance and two sample t-tests were applied to assess differences in topological attributes between patient groups, alongside correlation analysis, which examined the value of changing topological attributes in predicting various clinical outcomes.

RESULTS: Significant differences between each group with regard to network metrics were observed. These included clustering coefficients and small-worldness. At the nodal level, several nodes with an abnormal degree centrality and nodal efficiency were detected. In rich club, pMCI and sMCI patients showed declined connectivity compared with HC. Significant differences were observed in the intra- and inter-module connections among the three groups. Particularly noteworthy was the irreplaceable role of the cerebellar module in network interactions.

CONCLUSION: Our study revealed significant differences in network topological properties among sMCI, pMCI and HC patients, which were significantly correlated with cognitive function. Most notably, the cerebellar module played a crucial role in the overall network interactions. In conclusion, these findings could aid in the development of imaging markers used to expedite diagnosis and intervention prior to Alzheimer's disease onset.

PMID:39624168 | PMC:PMC11609165 | DOI:10.3389/fnagi.2024.1467054

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