Most recent paper

IEEE Trans Neural Syst Rehabil Eng. 2024 Nov 13;PP. doi: 10.1109/TNSRE.2024.3497893. Online ahead of print.

ABSTRACT

Transcutaneous auricular vagus nerve stimulation (taVNS) is a promising neurostimulation approach for emotion regulation. This research aimed to clarify the underlying neural basis responsible for taVNS's impact on emotional regulation related brain regions. Thirty-two healthy volunteers were allocated into a taVNS group, which received electrical stimulation at the concha area of the ear, and a sham group, which received earlobe stimulation. Resting-state functional magnetic resonance imaging data were collected from both the taVNS and sham groups pre- and post-stimulation. To evaluate the alterations in neural activity and connectivity resulting from auricular electrical stimulation, degree centrality and functional connectivity analyses were used. The results indicated that taVNS modulated the neural activity of several brain regions, including the bilateral precuneus, temporal gyrus, precentral gyrus, and postcentral gyrus, whereas earlobe stimulation did not produce such effects. taVNS may improve emotion regulation by modulating neural activation and functional connectivity in key brain regions, then facilitating the integration of emotional responses, memories, and experiences. Thus, these brain regions may serve as potential therapeutic targets for taVNS in treating disorders associated with emotional dysregulation. These findings provide insight into the neural basis through which taVNS influences emotion regulation and hold potential for the development of neuromodulation-based therapeutic strategies for emotional disorders.

PMID:40030198 | DOI:10.1109/TNSRE.2024.3497893

Res Dir Depress. 2024 Oct 1;1:e21. doi: 10.1017/dep.2024.6. eCollection 2024.

ABSTRACT

BACKGROUND: Neural predictors underlying variability in depression outcomes are poorly understood. Functional MRI measures of subgenual cortex connectivity, self-blaming and negative perceptual biases have shown prognostic potential in treatment-naïve, medication-free and fully remitting forms of major depressive disorder (MDD). However, their role in more chronic, difficult-to-treat forms of MDD is unknown.

METHODS: Forty-five participants (n = 38 meeting minimum data quality thresholds) fulfilled criteria for difficult-to-treat MDD. Clinical outcome was determined by computing percentage change at follow-up from baseline (four months) on the self-reported Quick Inventory of Depressive Symptomatology (16-item). Baseline measures included self-blame-selective connectivity of the right superior anterior temporal lobe with an a priori Brodmann Area 25 region-of-interest, blood-oxygen-level-dependent a priori bilateral amygdala activation for subliminal sad vs happy faces, and resting-state connectivity of the subgenual cortex with an a priori defined ventrolateral prefrontal cortex/insula region-of-interest.

FINDINGS: A linear regression model showed that baseline severity of depressive symptoms explained 3% of the variance in outcomes at follow-up (F[3,34] = .33, p = .81). In contrast, our three pre-registered neural measures combined, explained 32% of the variance in clinical outcomes (F[4,33] = 3.86, p = .01).

CONCLUSION: These findings corroborate the pathophysiological relevance of neural signatures of emotional biases and their potential as predictors of outcomes in difficult-to-treat depression.

PMID:40028885 | PMC:PMC11869767 | DOI:10.1017/dep.2024.6

Front Psychiatry. 2025 Feb 14;16:1458602. doi: 10.3389/fpsyt.2025.1458602. eCollection 2025.

ABSTRACT

BACKGROUND: Apathy is a prevalent psychiatric condition after stroke, affecting approximately 30% of stroke survivors. It is associated with slower recovery and an increased risk of depression. Understanding the pathophysiological mechanisms of post stroke apathy (PSA) is crucial for developing targeted rehabilitation strategies.

METHODS: In this study, we recruited a total of 18 PSA patients, 18 post-stroke non-apathy (NPSA) patients, and 18 healthy controls (HCs). Apathy was measured using the Apathy Evaluation Scale (AES). Resting-state functional magnetic resonance imaging (rs-fMRI) was utilized to investigate spontaneous brain activity. We estimated the amplitude of low-frequency fluctuation (ALFF) across three different frequency bands (typical band: 0.01-0.08 Hz; slow-4: 0.027-0.073 Hz; slow-5: 0.01-0.027 Hz) and the fractional amplitude of low-frequency fluctuation (fALFF).

RESULTS: Band-specific ALFF differences among the three groups were analyzed. Significant differences were found in the typical band within the left lingual gyrus, right fusiform gyrus, right superior temporal gyrus (STG), and left insula. In the slow-4 band, significant differences were observed in the left middle frontal gyrus (MFG) and right STG. In the slow-5 band, significant differences were identified in the left calcarine cortex and right insula. For fALFF values, significant differences were found in the left lingual gyrus and right thalamus. Moreover, positive correlations were observed between AES scores and the ALFF values in the right STG (r = 0.490, p = 0.002) in the typical band, left MFG (r = 0.478, p = 0.003) and right STG (r = 0.451, p = 0.006) in the slow-4 band, and fALFF values of the right thalamus (r = 0.614, p < 0.001).

CONCLUSION: This study is the first to investigate the neural correlates of PSA using voxel-level analysis and different ALFF banding methods. Our findings indicate that PSA involves cortical and subcortical areas, including the left MFG, right STG, and right thalamus. These results may help elucidate the neural mechanisms underlying PSA and could serve as potential neuroimaging indicators for early diagnosis and intervention.

PMID:40027597 | PMC:PMC11868042 | DOI:10.3389/fpsyt.2025.1458602

Brain Imaging Behav. 2025 Mar 3. doi: 10.1007/s11682-025-00989-9. Online ahead of print.

ABSTRACT

This systematic narrative review examines neuroimaging studies that investigated the neural correlates of mindfulness-based interventions in youth (ages 0-18). We extracted 13 studies with a total of 467 participants aged 5-18 years from the MEDLINE database on February 21st, 2024. These studies included both typically developing youth and those at risk of developing or recovering from neuropsychiatric disorders. Most studies (76.9%) utilized a pre-post intervention design, with resting-state functional magnetic resonance imaging (fMRI) being the most common imaging modality (46.1%), followed by task-based fMRI (38.4%), diffusion-weighted imaging (15.4%), and structural MRI (7.7%). Despite substantial heterogeneity across study designs and findings, several consistent patterns emerged. Resting-state fMRI studies generally reported increased functional connectivity within and between networks, notably involving the salience network, frontoparietal network, and default mode network. Studies using diffusion-weighted imaging indicated enhancements in white matter microstructural properties, supporting overall connectivity improvements. Several task-based fMRI studies identified decreased activation of the default mode network and heightened reactivity of the salience network during or after mindfulness practice, with real-time neurofeedback further amplifying these effects. While preliminary, the reviewed studies suggest that mindfulness interventions may alter both functional and structural connectivity and activity in youth, potentially bolstering self-regulation and cognitive control. Nonetheless, the variability in methodologies and small sample sizes restricts the generalizability of these results. Future research should prioritize larger and more diverse samples, and standardized mindfulness-based interventions to deepen our understanding of the neural mechanisms underlying mindfulness-based interventions in youth and to optimize their efficacy.

PMID:40025263 | DOI:10.1007/s11682-025-00989-9

Pediatr Res. 2025 Mar 2. doi: 10.1038/s41390-025-03951-z. Online ahead of print.

ABSTRACT

BACKGROUND: Neonatal hypoxic-ischemic encephalopathy (HIE) injures the infant brain during the basic formation of the developing functional connectome. This study aimed to investigate long-term changes in the functional connectivity (FC) networks of the adolescent brain following neonatal HIE treated with therapeutic hypothermia (TH).

METHODS: This prospective, population-based cohort study included all infants (n = 66) with TH-treated neonatal HIE in Stockholm during 2007-2009 and a control group (n = 43) of children with normal neonatal course. Assessment with resting-state functional magnetic resonance imaging (fMRI) was performed at Karolinska Institutet, Stockholm at age 9-12 years.

RESULTS: fMRI data met quality criteria for 35 children in the HIE-cohort (mean [SD] age at MRI: 11.2 [0.74] years, 46% male) and 30 children in the control group (mean [SD] age at MRI: 10.1 [0.78] years, 53% male). Adverse outcome was present in 40% of children in the HIE-cohort. Non-parametric statistical analysis failed to detect any significant (p < 0.001) alterations of FC networks in the HIE-cohort, nor between children in the HIE-cohort with or without neurological symptoms.

CONCLUSION: Findings of persistent alterations in specific functional networks did not remain significant after correction for multiple comparisons in this cohort of adolescent children exposed to TH-treated neonatal HIE.

IMPACT: Neonatal hypoxic-ischemic encephalopathy (HIE) could not be associated with alterations in functional connectivity in this cohort of adolescent children. Findings of aberrant connectivity identified in two functional networks were no longer significant after correction for multiple comparisons. Larger, multi-center studies are needed to understand whether network abnormalities persist long term and are related to outcomes in neonatal HIE.

PMID:40025254 | DOI:10.1038/s41390-025-03951-z

J Integr Med. 2025 Jan 27:S2095-4964(25)00018-4. doi: 10.1016/j.joim.2025.01.006. Online ahead of print.

ABSTRACT

OBJECTIVE: Comorbid pain and depression are common but remain difficult to treat. Electroacupuncture (EA) can effectively improve symptoms of depression and relieve pain, but its neural mechanism remains unclear. Therefore, we used resting-state functional magnetic resonance imaging (rs-fMRI) to detect cerebral changes after initiating a mouse pain model via constriction of the infraorbital nerve (CION) and then treating these animals with EA.

METHODS: Forty male C57BL/6J mice were divided into 4 groups: control, CION model, EA, and sham acupuncture (without needle insertion). EA was performed on the acupoints Baihui (GV20) and Zusanli (ST36) for 20 min, once a day for 10 consecutive days. The mechanical withdrawal threshold was tested 3 days after the surgery and every 3 days after the intervention. The depressive behavior was evaluated with the tail suspension test, open-field test, elevated plus maze (EPM), sucrose preference test, and marble burying test. The rs-fMRI was used to detect the cerebral changes of the functional connectivity (FC) in the mice following EA treatment.

RESULTS: Compared with the CION group, the mechanical withdrawal threshold increased in the EA group at the end of the intervention (P < 0.05); the immobility time in tail suspension test decreased (P < 0.05); and the times of the open arm entry and the open arm time in the EPM increased (both P < 0.001). There was no difference in the sucrose preference or marble burying tests (both P > 0.05). The fMRI results showed that EA treatment downregulated the amplitude of low-frequency fluctuations and regional homogeneity values, while these indicators were elevated in brain regions including the amygdala, hippocampus and cerebral cortex in the CION model for comorbid pain and depression. Selecting the amygdala as the seed region, we found that the FC was higher in the CION group than in the control group. Meanwhile, EA treatment was able to decrease the FC between the amygdala and other brain regions including the caudate putamen, thalamus, and parts of the cerebral cortex.

CONCLUSION: EA can downregulate the abnormal activation of neurons in the amygdala and improve its FC with other brain regions, thus exerting analgesic and antidepressant effects. Please cite this article as: Yin X, Zeng XL, Lin JJ, Xu WQ, Cui KY, Guo XT, Li W, Xu SF. Brain functional changes following electroacupuncture in a mouse model of comorbid pain and depression: a resting-state functional magnetic resonance imaging study. J Integr Med. 2025; Epub ahead of print.

PMID:40024869 | DOI:10.1016/j.joim.2025.01.006

Neuroimage. 2025 Feb 27:121107. doi: 10.1016/j.neuroimage.2025.121107. Online ahead of print.

ABSTRACT

Autism spectrum disorder (ASD) is associated with atypical brain connectivity, yet its hierarchical organization remains underexplored. In this study, we applied the Liang information flow method to analyze stepwise causal functional connectivity in ASD, offering a novel approach to understanding how different brain networks interact. Using resting-state fMRI data from ASD individuals and healthy controls, we observed significant alterations in both positive and negative causal connections across the ventral attention network, limbic network, frontal-parietal network, and default mode network. These disruptions were detected at multiple hierarchical levels, indicating changes in communication patterns across brain regions. By leveraging features of hierarchical causal connectivity, we achieved high classification accuracy between ASD and healthy individuals. Additionally, changes in network node degrees were found to correlate with ASD clinical symptoms, particularly social and communication behaviors. Our findings provide new insights into disrupted hierarchical brain connectivity in ASD and demonstrate the potential of this approach for distinguishing ASD from typical development.

PMID:40023264 | DOI:10.1016/j.neuroimage.2025.121107

Brain Behav. 2025 Mar;15(3):e70380. doi: 10.1002/brb3.70380.

ABSTRACT

INTRODUCTION: Chronic low back pain (CLBP) is a prevalent and debilitating condition. Cognitive behavioral therapy (CBT) can improve coping mechanisms for CLBP and pain-related outcomes. However, the mechanisms by which they do so remain undetermined. We explored the neural correlates of CLBP symptoms and CBT action using functional magnetic resonance imaging (fMRI) in women with CLBP and comorbid depression.

METHODS: Forty individuals underwent fMRI followed by 8 weeks of either treatment as usual (TAU) or one of two CBT in addition to TAU: acceptance and commitment therapy (ACT) or behavioral activation treatment for depression (BATD). Pain intensity, depression, psychological inflexibility, and pain catastrophizing scores were obtained at baseline and follow-up. Functional connectivity (FC) patterns of the salience network (SN), sensorimotor network (SMN), and the mesolimbic pathway (MLP), derived from resting-state fMRI examination were correlated with both baseline and delta (baseline-follow-up) pain-related psychological measures.

RESULTS: Individuals receiving ACT and BATD showed reduced depression, psychological inflexibility, and pain catastrophizing. Strong baseline connectivity of the SN and SMN corresponded with higher pain intensity, but strong connectivity of the MLP and precuneus corresponded with lower pain intensity. Pain intensity changes correlated with mesolimbic-salience connectivity following ACT, and with sensorimotor connectivity following BATD. Specifically, stronger baseline FC between the MLP and posterior insula predicted greater pain intensity reduction with ACT, while stronger FC between the SMN and secondary somatosensory cortex predicted greater pain intensity reduction with BATD. FC of the SN correlated with changes in psychological inflexibility across both therapies.

CONCLUSIONS: We illustrate the potential of FC as a biomarker of CLBP plus depression and the response to CBT. Our data suggest ACT and BATD have differing underlying brain mechanisms. These findings indicate that FC biomarkers could guide personalized treatment, improving individual outcomes.

PMID:40022281 | DOI:10.1002/brb3.70380

Brain Behav. 2025 Mar;15(3):e70376. doi: 10.1002/brb3.70376.

ABSTRACT

PURPOSE: Frontoparietal networks (FPN) are well-recognized for their role in high-level cognition, including mental imagery, executive control, and working memory (WM). A prevailing hypothesis advances that these functions evolved from fundamental motor abilities, such as action planning and motor control. However, whether sensorimotor regions of these FPN contribute to the executive components of WM, and whether this contribution is dependent on task modality, remains underexplored.

METHOD: This study applied analyses of resting-state functional connectivity (rs-FC) to investigate the contribution of FPN regions to WM that have an established role in motor planning. In a sample of 60 healthy individuals, we explored whether performance in verbal and spatial N-back WM tasks is associated with rs-FC of frontoparietal brain regions that exhibit increased activation during motor planning.

FINDING: Comparing verbal and spatial N-back tasks revealed that verbal WM was associated with stronger connectivity between the left medial superior frontal gyrus and left inferior parietal lobule (IPL), as well as the right IPL and the left superior parietal lobule. In contrast, spatial WM was linked to stronger connectivity between the right middle frontal and inferior temporal gyrus, as well as the left occipital pole and postcentral gyrus.

CONCLUSION: These findings reveal distinct FC patterns underlying verbal and spatial WM and highlight the contribution of brain regions that are important for motor planning to modality-specific WM processes, such as information updating.

PMID:40022219 | DOI:10.1002/brb3.70376

Imaging Neurosci (Camb). 2023;1. doi: 10.1162/imag_a_00046. Epub 2023 Dec 8.

ABSTRACT

Individual differences in the spatial organization of resting-state networks have received increased attention in recent years. Measures of individual-specific spatial organization of brain networks and overlapping network organization have been linked to important behavioral and clinical traits and are therefore potential biomarker targets for personalized psychiatry approaches. To better understand individual-specific spatial brain organization, this paper addressed three key goals. First, we determined whether it is possible to reliably estimate weighted (non-binarized) resting-state network maps using data from only a single individual, while also maintaining maximum spatial correspondence across individuals. Second, we determined the degree of spatial overlap between distinct networks, using test-retest and twin data. Third, we systematically tested multiple hypotheses (spatial mixing, temporal switching, and coupling) as candidate explanations for why networks overlap spatially. To estimate weighted network organization, we adopt the Probabilistic Functional Modes (PROFUMO) algorithm, which implements a Bayesian framework with hemodynamic and connectivity priors to supplement optimization for spatial sparsity/independence. Our findings showed that replicable individual-specific estimates of weighted resting-state networks can be derived using high-quality fMRI data within individual subjects. Network organization estimates using only data from each individual subject closely resembled group-informed network estimates (which was not explicitly modeled in our individual-specific analyses), suggesting that cross-subject correspondence was largely maintained. Furthermore, our results confirmed the presence of spatial overlap in network organization, which was replicable across sessions within individuals and in monozygotic twin pairs. Intriguingly, our findings provide evidence that overlap between 2-network pairs is indicative of coupling. These results suggest that regions of network overlap concurrently process information from both contributing networks, potentially pointing to the role of overlapping network organization in the integration of information across multiple brain systems.

PMID:40017584 | PMC:PMC11867625 | DOI:10.1162/imag_a_00046

Ann Neurosci. 2025 Jan;32(1):13-20. doi: 10.1177/09727531231191889. Epub 2023 Aug 28.

ABSTRACT

BACKGROUND: Resting-state functional connectivity analysis has a potential to unearth the putative neuronal underpinnings of various disorders of the brain. Major depressive disorder (MDD) is regarded as a disorder arising from alterations in functional networks of the brain.

PURPOSE: There is paucity of literature on resting-state functional magnetic resonance imaging (Rs-fMRI) in MDD, especially from the Indian subcontinent. The purpose of our study was to elucidate the differences in Rs-fMRI connectivity between MDD patients and age and gender matched healthy controls (HC).

METHODS: In this prospective single institute-based study, the patients were recruited consecutively based on Hamilton depression rating scale (HAM-D). Age and gender matched HC were also recruited. Rs-fMRI and anatomical MRI images were acquired for all the subjects (MDD and HC group) and subsequent analysis was done using the CONN toolbox.

RESULTS: A total of 49 subjects were included in the final analysis (MDD = 28 patients, HC = 21). HAM-D score was noted to be 24.4 ± 4.8 in the MDD group. There was no significant difference between MDD and HC groups as far as age, gender, employment status, and level of education is concerned. Region-of-interest-based analysis of Rs-fMRI data showed a significantly lower connectivity between the left insula and left nucleus accumbens and between left paracingulate gyrus and bilateral posterior middle temporal gyri in MDD group as compared to HC group.

CONCLUSION: There is reduced connectivity between certain key regions of the brain in MDD patients, that is, between the left insular cortex and the left nucleus accumbens and between the left paracingulate gyrus and the bilateral posterior middle temporal gyrus. These findings could explain the basis of clinical features of MDD such as anhedonia, rumination of thoughts, reduced visuo-spatial comprehension, reduced language function, and response to external stimuli.

PMID:40017570 | PMC:PMC11863249 | DOI:10.1177/09727531231191889

BMC Psychiatry. 2025 Feb 27;25(1):182. doi: 10.1186/s12888-025-06632-7.

ABSTRACT

BACKGROUND: Schizophrenia patients frequently present with structural and functional abnormalities of the hippocampus (Hipp). Further, these abnormalities are often associated with specific symptom profiles.

AIM: To determine whether schizophrenia patients show specific functional connectivity (FC) and activity abnormalities in each hippocampal subregion compared to the BD (bipolar disorder) and HC (healthy control) groups.

METHODS: Basal activation state and functional connectivity (FC) in four subregions of the bilateral Hipp were examined: left caudal (cHipp_L), right caudal (cHipp_R), left rostral (rHipp_L), and right rostral (rHipp_R). Resting-state functional magnetic resonance images were obtained from 62 schizophrenia patients, 57 bipolar disorder (BD) patients, and 45 healthy controls (HCs), and analyzed for fractional amplitude of low-frequency fluctuations (fALFF) as a measure of basal neural activity and for whole-brain FC based on the hippocampal subregions.

RESULTS: The schizophrenia group exhibited greater fALFF in bilateral cHipp (the caudal part of hippocampus) and rHipp (the rostral part of hippocampus) subregions compared to BD and HC groups as well as increased FC between the bilateral cHipp and multiple brain regions, including the thalamus, putamen, middle frontal gyrus, parietal cortex, and precuneus. Moreover, fALFF values of the bilateral cHipp were positively correlated with the severity of clinical symptoms as measured by the Positive and Negative Syndrome Scale.

CONCLUSIONS: These findings confirm an important contribution of hippocampal dysfunction, especially of the cHipp, in schizophrenia. Further, hyper-connectivity and hyperactivity of the cHipp could serve as a biomarker for therapeutic development.

PMID:40016773 | DOI:10.1186/s12888-025-06632-7

J Neurosci. 2025 Feb 27:e0802242025. doi: 10.1523/JNEUROSCI.0802-24.2025. Online ahead of print.

ABSTRACT

The brain comprises a complex network of interacting regions. To understand the roles and mechanisms of this intricate network, it is crucial to elucidate its structural features related to cognitive functions. Recent empirical evidence suggests that both feedforward and feedback signals are necessary for conscious perception, emphasizing the importance of subnetworks with bidirectional interactions. However, the link between such subnetworks and conscious perception remains unclear due to the complexity of brain networks. In this study, we propose a framework for extracting subnetworks with strong bidirectional interactions-termed the "cores" of a network-from brain activity. We applied this framework to resting-state and task-based human fMRI data from participants of both sexes to identify regions forming strongly bidirectional cores. We then explored the association of these cores with conscious perception and cognitive functions. We found that the extracted central cores predominantly included cerebral cortical regions rather than subcortical regions. Additionally, regarding their relation to conscious perception, we demonstrated that the cores tend to include regions previously reported to be affected by electrical stimulation that altered conscious perception, although the results are not statistically robust due to the small sample size. Furthermore, in relation to cognitive functions, based on a meta-analysis and comparison of the core structure with a cortical functional connectivity gradient, we found that the central cores were related to unimodal sensorimotor functions. The proposed framework provides novel insights into the roles of network cores with strong bidirectional interactions in conscious perception and unimodal sensorimotor functions.Significance Statement To understand the brain's network, we need to decipher its structural features linked to cognitive functions. Recent studies suggest the importance of subnetworks with bidirectional interactions for conscious perception, but their exact relationship remains unclear due to the brain's complexity. Here we propose a framework for extracting subnetworks with strong bidirectional interactions, or network "cores." We applied it to fMRI data and explored the association of the cores with conscious perception and cognitive functions. The central cores predominantly included cortical regions rather than subcortical ones, and tended to comprise previously reported regions wherein electrical stimulation altered perception, suggesting the potential importance of bidirectional cores for conscious perception. Additionally, further analysis revealed the relationship of the cores to unimodal sensorimotor functions.

PMID:40015987 | DOI:10.1523/JNEUROSCI.0802-24.2025

Front Aging Neurosci. 2025 Feb 12;17:1527323. doi: 10.3389/fnagi.2025.1527323. eCollection 2025.

ABSTRACT

OBJECTIVES: To propose a multimodal functional brain network (FBN) and structural brain network (SBN) topological feature fusion technique based on resting-state functional magnetic resonance imaging (rs-fMRI), diffusion tensor imaging (DTI), 3D-T1-weighted imaging (3D-T1WI), and demographic characteristics to diagnose mild cognitive impairment (MCI) in patients with unilateral middle cerebral artery (MCA) steno-occlusive disease.

METHODS: The performances of different algorithms on the MCI dataset were evaluated using 5-fold cross-validation. The diagnostic results of the multimodal performance were evaluated using t-distributed stochastic neighbor embedding (t-SNE) analysis. The four-modal analysis method proposed in this study was applied to identify brain regions and connections associated with MCI, thus confirming its validity.

RESULTS: Based on the fusion of the topological features of the multimodal FBN and SBN, the accuracy for the diagnosis of MCI in patients with unilateral MCA steno-occlusive disease reached 90.00%. The accuracy, recall, sensitivity, and F1-score were higher than those of the other methods, as was the diagnostic efficacy (AUC = 0.9149).

CONCLUSION: The multimodal FBN and SBN topological feature fusion technique, which incorporates rs-fMRI, DTI, 3D-T1WI, and demographic characteristics, obtains the most discriminative features of MCI in patients with unilateral MCA steno-occlusive disease and can effectively identify disease-related brain areas and connections. Efficient automated diagnosis facilitates the early and accurate detection of MCI and timely intervention and treatment to delay or prevent disease progression.

PMID:40013095 | PMC:PMC11861546 | DOI:10.3389/fnagi.2025.1527323

Sci Rep. 2025 Feb 26;15(1):6863. doi: 10.1038/s41598-025-91457-3.

ABSTRACT

Late-life depression represents a significant health concern, linked to disruptions in brain connectivity and immune functioning, mood regulation, and cognitive function. This pilot study explores a digital intervention targeting mental health, brain health, and immune functioning in individuals aged 55-60 with subjective cognitive decline, elevated stress and depressive symptoms. Seventeen participants engaged in a two-week intervention comprising spatial cognition, psychological techniques based on mindfulness, attention-training exercises, and cognitive behavioral therapy. Pre-and post-intervention changes in resting-state functional connectivity, inflammation, and psychological health were evaluated. Key findings include: (1) Reduced self-reported depression with a large effect size, (2) Decreased connectivity within the default mode network (DMN), (3) Enhanced anticorrelation between the DMN-Salience networks that was associated with improved depression scores (4) Reduced salivary IL-18 concentration with a medium effect size, correlated with decreased DMN-amygdala connectivity. There was a trend towards reduced anxiety, with no significant changes in quality of life. To our knowledge, this is the first study to investigate the effect of digital intervention on immune markers, clinical behavioral outcomes, and brain function, demonstrating positive synergistic potential across all three levels. These preliminary findings, which need replication in larger, controlled studies, have important implications for basic science and scalable digital interventions.

PMID:40011544 | DOI:10.1038/s41598-025-91457-3

Int J Geriatr Psychiatry. 2025 Mar;40(3):e70058. doi: 10.1002/gps.70058.

ABSTRACT

OBJECTIVES: Depressive symptoms are frequent in the early stages of dementia with Lewy bodies (DLB), and more than half of DLB patients would have a history of depression. Our study sought to investigate the functional connectivity (FC) changes associated with depressive symptoms in prodromal to mild DLB patients compared with controls.

METHODS: MRI data were collected from 66 DLB patients and 18 controls. Depression was evaluated with the Mini International Neuropsychiatric Interview. Resting-state FC (rsFC) was investigated with the CONN toolbox using a seed-based approach and both regression and comparison analyses.

RESULTS: Correlations were found between the depression scores and the rsFC between fronto-temporal and primary visual areas in DLB patients (p < 0.05, FDR corrected). Depressed DLB patients also showed decreased rsFC within the salience network (SN), increased rsFC between the default mode network (DMN) and the language network (LN) and decreased rsFC between the cerebellar network (CN) and the fronto-parietal network (FPN) compared to non-depressed DLB patients (p < 0.05, uncorrected). Comparison analyses between antidepressant-treated and non-treated DLB patients highlighted FC changes in treated patients involving the SN, the DMN, the FPN and the dorsal attentional network (p < 0.05, uncorrected).

CONCLUSIONS: Our findings revealed that depressive symptoms would especially be associated with rsFC changes between fronto-temporal and primary visual areas in DLB patients. Such alterations could contribute to difficulties in regulating emotions, processing biases towards negative stimuli, and self-focused ruminations.

TRIAL REGISTRATION: This study is part of the cohort study AlphaLewyMA (https://clinicaltrials.gov/ct2/show/NCT01876459).

PMID:40011213 | DOI:10.1002/gps.70058

J Psychiatr Res. 2025 Feb 21;183:260-268. doi: 10.1016/j.jpsychires.2025.02.025. Online ahead of print.

ABSTRACT

BACKGROUND: The abnormalities in brain functional connectivity (FC) and graph topology (GT) in patients with schizophrenia (SZ) are unclear. Researchers proposed machine learning algorithms by combining FC or GT to identify SZ from healthy controls. The schizophrenia classification and neuroimaging markers screening using FC and GT feature fusion are blank.

METHODS: We proposed multi-feature fusion method combining functional connectivity and graph topology for schizophrenia classification and neuroimaging markers screening. Firstly, we acquired and preprocessed the private rs-fMRI data from the second affiliated hospital of Xinxiang Medical University in china. Secondly, we calculated the functional connectivity matrix and graph topology features. Thirdly, we used the two-sample t-test and the minimum absolute contraction selection operator (LASSO) to extract the features with statistical differences. Lastly, we used machine learning to classify schizophrenia and screen neuroimaging markers.

RESULTS: The result showed that the SVM model with the best feature (i.e., FC and GT) has the best performance (ACC = 0.935(95 percent confidence interval, 0.932 to 0.938), SEN = 0.920(95 percent confidence interval, 0.917 to 0.922), SPE = 0.950(95 percent confidence interval, 0.946 to 0.954), F1 = 0.935(95 percent confidence interval, 0.933 to 0.938), AUC = 0.935(95 percent confidence interval, 0.932 to 0.937)). We also found that the differences in FC and GT features are mainly located in the default network, the attention network, and the subcortical network. The feature strength of FC and GT showed a general decline in patients with SZ, and the node clustering coefficient of the thalamus and the FC of Putamen_L and Frontal_Mid_Orb_R showed an increase.

CONCLUSION: It demonstrated that the multi-feature fusion has the advantage in distinguishing SZ from healthy individuals providing new insights into the underlying pathogenesis of SZ.

PMID:40010076 | DOI:10.1016/j.jpsychires.2025.02.025

Psychol Med. 2025 Feb 26;55:e63. doi: 10.1017/S0033291725000303.

ABSTRACT

BACKGROUND: Depression has been linked to disruptions in resting-state networks (RSNs). However, inconsistent findings on RSN disruptions, with variations in reported connectivity within and between RSNs, complicate the understanding of the neurobiological mechanisms underlying depression.

METHODS: A systematic literature search of PubMed and Web of Science identified studies that employed resting-state functional magnetic resonance imaging (fMRI) to explore RSN changes in depression. Studies using seed-based functional connectivity analysis or independent component analysis were included, and coordinate-based meta-analyses were performed to evaluate alterations in RSN connectivity both within and between networks.

RESULTS: A total of 58 studies were included, comprising 2321 patients with depression and 2197 healthy controls. The meta-analysis revealed significant alterations in RSN connectivity, both within and between networks, in patients with depression compared with healthy controls. Specifically, within-network changes included both increased and decreased connectivity in the default mode network (DMN) and increased connectivity in the frontoparietal network (FPN). Between-network findings showed increased DMN-FPN and limbic network (LN)-DMN connectivity, decreased DMN-somatomotor network and LN-FPN connectivity, and varied ventral attention network (VAN)-dorsal attentional network (DAN) connectivity. Additionally, a positive correlation was found between illness duration and increased connectivity between the VAN and DAN.

CONCLUSIONS: These findings not only provide a comprehensive characterization of RSN disruptions in depression but also enhance our understanding of the neurobiological mechanisms underlying depression.

PMID:40008424 | DOI:10.1017/S0033291725000303

Brain Spine. 2025 Feb 3;5:104202. doi: 10.1016/j.bas.2025.104202. eCollection 2025.

ABSTRACT

INTRODUCTION: Functional coupling of the tumor to extralesional brain areas and the pretherapeutic cognitive performance status have each independently been identified as prognostically relevant in glioma patients. It is however unclear, whether tumor-connectivity correlates with cognitive performance or the cognitive outcome.

RESEARCH QUESTION: To investigate potential associations between pre- and postoperative resting-state fMRI connectivity (FC) and cognitive functions in glioma patients compared to healthy controls.

MATERIAL AND METHODS: 18 patients and 18 age-matched, healthy controls underwent resting-state fMRI and neuropsychological testing pre- and 4.5 months (mean) postoperatively. FC of the tumor to extralesional brain (Tu-EL) was determined, as well as FC of extralesional brain (EL) and the contralesional hemisphere (conEL). Groups were compared with regard to behavioral and FC measures.

RESULTS: Patients showed deficits in all cognitive domains tested. While postoperative performance tended to be worse, deterioration was not statistically significant between timepoints. EL FC did not differ between groups, but conEL FC (p < .045) was increased in patients as compared to controls. Tu-EL FC was significantly associated with worse attention performance (p < .001), and, by trend (p < .058), with worse attentional outcome in patients.

DISCUSSION AND CONCLUSION: Intrinsic functional coupling to the rest of the brain was associated with worse cognitive performance and might relate to pathological tumor-neuron interaction on the macroscale, reflecting the invasive nature of diffusely infiltrating glioma. Deepening our understanding of FC measures at the connectomic level in the context of cancer neuroscience may aid in identifying neurophysiological correlates of cognitive impairment and in prognosticating cognitive outcome in glioma patients.

PMID:40007801 | PMC:PMC11851226 | DOI:10.1016/j.bas.2025.104202

Heliyon. 2025 Feb 6;11(3):e42464. doi: 10.1016/j.heliyon.2025.e42464. eCollection 2025 Feb 15.

ABSTRACT

The utilization of functional magnetic resonance imaging (fMRI) is critical in the preoperative planning phase of brain tumor surgery because it allows for a delicate balance between maximizing tumor resection and maintaining brain function. A decade of fMRI development was examined in this study, with a particular emphasis on its use in diagnosing and assessing the efficacy of brain cancer treatments. We examined the foundational principles, practical implementations, and verification of fMRI via direct brain stimulation, with particular emphasis on its capacity to detect cerebral regions affected by tumors that are eloquent in nature. Recently, fMRI has undergone significant progress, allowing for its integration into clinical workflows to facilitate precise mapping of brain functions. This extensive analysis encompasses the scrutiny of resting-state fMRI (Rs-fMRI) as a method of capturing functional connectivity, thereby providing significant insights into the management of patients with brain tumors. Methodological advancements, clinical applicability, and future orientations of fMRI are highlighted in this review, which emphasizes the substantial influence of the technique on neurosurgical planning and patient outcomes.

PMID:40007791 | PMC:PMC11850128 | DOI:10.1016/j.heliyon.2025.e42464