Most recent paper

Annu Int Conf IEEE Eng Med Biol Soc. 2024 Jul;2024:1-5. doi: 10.1109/EMBC53108.2024.10782625.

ABSTRACT

Surgical resection is one of the main treatment options for brain tumors. However, there is a risk of postoperative cognitive deterioration associated with resective surgery. Recent studies suggest that pre-surgery brain dynamics captured using functional Magnetic Resonance Imaging (fMRI) could provide valuable information about the risk of post-surgery cognitive decline. However, most of these studies are based on simple regression analysis of the raw fMRI signals that do not capture the underlying complex brain dynamics. Here, we investigated the role of higher-order functional brain networks in predicting cognitive decline after surgical resection of brain tumors. More specifically, we looked at the predictive power of second-order functional brain networks in estimating post-surgery working memory (WM) performance. Our results show that the second-order functional brain networks can accurately predict the working memory decline in patients with glioma and meningioma tumors. These findings suggest that there is an interesting relationship between pre-surgical higher-order brain dynamics and the risk of cognitive decline after surgery, which could potentially yield a better prognostic marker for treatment planning of brain tumor patients.

PMID:40039369 | DOI:10.1109/EMBC53108.2024.10782625

Annu Int Conf IEEE Eng Med Biol Soc. 2024 Jul;2024:1-4. doi: 10.1109/EMBC53108.2024.10782404.

ABSTRACT

Intrinsic Connectivity Networks (ICNs) reflect functional brain organization responsible for various cognitive processes, including sensory perception, motor control, memory, and attention. In this study, we used the Multivariate-Objective Optimization Independent Component Analysis with Reference (MOO-ICAR) and the NeuroMark 2.1 (adult) template to estimate subject-specific ICNs in resting-state functional magnetic resonance imaging (rsfMRI) data of infants. The NeuroMark 2.1 template contains 105 multi-scale canonical ICNs derived from 100k+ adults across multiple datasets. The multi-scale ICNs capture functional segregation across various levels of granularity across brain, revealing functional sources and their interactions. The results showed that the 105 ICNs in infants were spatially aligned with those in the template and revealed age-related distinctive patterns in static Functional Network Connectivity (sFNC), particularly in the sub-cortical and high-level cognitive domains. This study is the first to investigate the presence and development of these multi-scale ICNs in infant rsfMRI data. Our findings confirmed the presence of identifiable canonical ICNs in infants as young as six months, showcasing a strong association between these networks and age and suggesting potential biomarkers for early identification of neurodevelopmental disability.

PMID:40039283 | DOI:10.1109/EMBC53108.2024.10782404

Annu Int Conf IEEE Eng Med Biol Soc. 2024 Jul;2024:1-4. doi: 10.1109/EMBC53108.2024.10782953.

ABSTRACT

A common analysis approach for resting state functional magnetic resonance imaging (rs-fMRI) dynamic functional network connectivity (dFNC) data involves clustering windowed correlation time-series and assigning time windows to clusters (i.e., states) that can be quantified to summarize aspects of the dFNC dynamics. However, those methods can be dominated by a select few features and obscure key dynamics related to less dominant features. This study presents an iterative feature learning approach to identify a maximally significant and minimally complex subset of dFNC features within the default mode network (DMN) in schizophrenia (SZ). Utilizing dFNC data from individuals with SZ and healthy controls (HC), our approach uncovers a subset of features that has a greater number of dFNC states with disorder-related dynamics than is found when all features are present in the clustering. We find that anterior cingulate cortex/posterior cingulate cortex (ACC/PCC) interactions are consistently related to SZ across the most significant iterations of the feature learning analysis and that individuals with SZ tend to spend more time in states with greater intra-ACC anticorrelation and almost no time in a state of high intra-ACC correlation that HCs periodically enter. Our findings highlight the need for nuanced analyses to reveal disorder-related dynamics and advance our understanding of neuropsychiatric disorders.

PMID:40039134 | DOI:10.1109/EMBC53108.2024.10782953

Annu Int Conf IEEE Eng Med Biol Soc. 2024 Jul;2024:1-4. doi: 10.1109/EMBC53108.2024.10782408.

ABSTRACT

Autism spectrum disorder (ASD) is a neurodevelopmental disorder impacting a person's social communication skills and behaviors. Due to its a wide range of symptoms and presentations, diagnosis is a subjective process reliant on clinician experience and symptom reports. Our pilot study aims to improve this process using a resting-state fMRI biomarker based on dynamic network modeling. Using matched cohorts (14 healthy, 14 ASD) from the DecNef rsfMRI open dataset we built generative models of the influence between cortical regions of the brain, encapsulated by a value we call the sink index; a network-based biomarker that measures the influence on between brain regions. A high sink index suggests high influence from and minimal influence on other parts of the network over time. Three cortical regions were found to have statistically significant differences between ASD and control patients: the left lateral occipital cortex, right frontal pole, and the left postcentral gyrus. Using these results, a high accuracy (AUC 0.91) classifier was generated that can quantitatively predict ASD status.

PMID:40039050 | DOI:10.1109/EMBC53108.2024.10782408

Annu Int Conf IEEE Eng Med Biol Soc. 2024 Jul;2024:1-5. doi: 10.1109/EMBC53108.2024.10782267.

ABSTRACT

Alzheimer's disease is the most common age-related problem and progresses in different stages, from cognitively normal to early mild cognitive impairment, and severe dementia. This study investigates the predictive potential of resting-state functional magnetic resonance imaging (rs-fMRI) and its derived functional connectivity (FC) in understanding Alzheimer's progression. Leveraging deep learning and graph-based models, we introduce two key contributions: 1) a comparative analysis of rs-fMRI time points and FC for Alzheimer's prediction. 2) an innovative graph transformer variant incorporating self-clustering for enhanced prediction accuracy. Experiments on the Alzheimer's Disease Neuroimaging Initiative dataset with 830 subjects reveal two notable conclusions. Firstly, rs-fMRI time points offer limited utility compared to functional network connectivity for transformer-based models, even when considering temporal information. Secondly, a clustering-based attention module proves effective for classifying brain networks in predicting Alzheimer's disease progression, providing valuable insights for future research and clinical applications.

PMID:40039021 | DOI:10.1109/EMBC53108.2024.10782267

Annu Int Conf IEEE Eng Med Biol Soc. 2024 Jul;2024:1-5. doi: 10.1109/EMBC53108.2024.10782250.

ABSTRACT

Using neuroimaging-derived data for age estimation serves as a prominent approach in comprehending the normal pace of brain development and mechanisms underlying cognitive declines due to aging and neurological diseases. Despite the promise of resting-state functional magnetic resonance imaging (rs-fMRI) for brain age prediction, previous deep learning models have prioritized capturing either the temporal dynamics via time courses (TCs) or the inherent network topology revealed by functional network connectivity (FNC). These fragmented models neglect the complementary information available by synergistically integrating both. To address this, we introduced BrainFTFCN, a novel feature fusion network that synergistically integrates TCs and FNC for enhanced brain age prediction and model interpretability. BrainFTFCN uniquely combines a Temporal Attention Autoencoder (TAAE) to model evolving activity patterns within TCs and a Functional Connectivity Graph Attention Network (FCGAT) to capture spatial relationships embedded within FNC. The fused features were then fed into a support vector regression model for final age prediction. BrainFTFCN's efficacy shone on Cam-CAN dataset, outperforming state-of-the-art models by 28.21% in mean absolute error (MAE) and demonstrating consistent improvement across other metrics. Ablation studies solidified the critical role of multi-feature integration in boosting prediction. Notably, the most crucial brain regions and discriminative FNC can be easily unveiled via LASSO regression and GNNExplainer respectively, together unlocking biological interpretability and highlighting the model's potential for uncovering valid aging biomarkers.

PMID:40038971 | DOI:10.1109/EMBC53108.2024.10782250

Annu Int Conf IEEE Eng Med Biol Soc. 2024 Jul;2024:1-4. doi: 10.1109/EMBC53108.2024.10782978.

ABSTRACT

Schizophrenia is a chronic mental disorder thought to affect cognitive processes and emotional regulation by disrupting communication between brain regions. The high level of training and experience required for accurate diagnosis limits access to care for many patients with this debilitating illness, leading to delays in diagnosis and progression of illness for an uncertain period. To improve accuracy of treatment, we investigated a potential quantitative method of tracking progression of schizophrenia using resting-state fMRI. Using data sourced from the DecNef rsfMRI open dataset in High and Low Duration cohorts, we constructed personalized dynamic network models that characterize influence between cortical regions of the brain. The contrasting levels of influence were converted to a phase space and ranked according to a novel network-based biomarker we call the "sink index." When the sink index is high it suggests that a region is being heavily influenced by other parts of the network and is not itself influencing the network strongly. Out of seventy cortical regions, the sink index of the left banks of the superior temporal sulcus was identified as able to significantly differentiate between cohorts and built a classifier of very high accuracy (sens 0.86, spec 1.0, AUC 0.99). Our results support the hypothesis that the pathophysiology of schizophrenia is indicative of aberrant network connectivity patterns.

PMID:40038957 | DOI:10.1109/EMBC53108.2024.10782978

Annu Int Conf IEEE Eng Med Biol Soc. 2024 Jul;2024:1-4. doi: 10.1109/EMBC53108.2024.10782970.

ABSTRACT

One of the key challenges in the use of resting brain functional magnetic resonance imaging (fMRI) network analysis for predicting mental illnesses such as schizophrenia (SZ) is the high noise levels variability among individuals including age, sex, and different protocols used in labs. To deal with these challenging problems, we designed a recognition method for using brain functional networks to classify SZs and healthy controls (HCs). Our method includes two stages of training. In the first stage, we use a deep convolutional neural network (DCNN) to extract valuable deep features from functional network connectivity (FNC) images. In the next stage, these deep features are used as inputs to a gradient-boosting trees classifier. After the training process, the boosting trees classifier gains a remarkable performance compared to the DCNN classifier. We evaluate this approach using a large dataset of schizophrenia and healthy controls divided into separate validation and training sets. Experimental results showed that the recognition accuracy is over 98 %, compared to a support vector machine baseline of 77% demonstrating the ability of our system to distinguish differences between the two groups. We also estimate heatmaps for each FNC image, representing a 2D FNC matrix indicated which pairs of networks are most predictive of SZ. Our method thus provides both high accuracy, and provides insights into the relevant brain regions for SZ.

PMID:40038933 | DOI:10.1109/EMBC53108.2024.10782970

Schizophr Bull. 2025 Mar 4;51(Supplement_2):S160-S172. doi: 10.1093/schbul/sbad116.

ABSTRACT

BACKGROUND AND HYPOTHESIS: Social anhedonia is a core feature of schizotypy and correlates significantly with social functioning and range adaptation. Range adaptation refers to representing a stimulus value based on its relative position in the range of pre-experienced values. This study aimed to examine the resting-state neural correlates of range adaptation and its associations with social anhedonia and social functioning.

STUDY DESIGN: In study 1, 60 participants completed resting-state magnetic resonance spectroscopy and fMRI scans. Range adaptation was assessed by a valid effort-based decision-making paradigm. Self-reported questionnaires was used to measure social anhedonia and social functioning. Study 2 utilized 26 pairs of participants with high (HSoA) and low levels of social anhedonia (LSoA) to examine the group difference in range adaptation's neural correlates and its relationship with social anhedonia and social functioning. An independent sample of 40 pairs of HSoA and LSoA was used to verify the findings.

STUDY RESULTS: Study 1 showed that range adaptation correlated with excitation-inhibition balance (EIB) and ventral prefrontal cortex (vPFC) functional connectivity, which in turn correlating positively with social functioning. Range adaptation was specifically determined by the EIB via mediation of ventral-medial prefrontal cortex functional connectivities. Study 2 found HSoA and LSoA participants exhibiting comparable EIB and vPFC connectivities. However, EIB and vPFC connectivities were negatively correlated with social anhedonia and social functioning in HSoA participants.

CONCLUSIONS: EIB and vPFC functional connectivity is putative neural correlates for range adaptation. Such neural correlates are associated with social anhedonia and social functioning.

PMID:40037829 | DOI:10.1093/schbul/sbad116

Schizophr Bull. 2025 Mar 4;51(Supplement_2):S194-S204. doi: 10.1093/schbul/sbad183.

ABSTRACT

BACKGROUND AND HYPOTHESIS: Mental time travel (MTT) is a crucial ability for daily life. Personal goal-related MTT events has stronger phenomenological characteristics than personal goal-unrelated ones, ie, the "personal goal-advantage effect". However, it remains unclear whether this effect is impacted in individuals with high schizotypal traits (HST) and the neural correlates of this effect have yet to be elucidated. The present study aimed to fill these knowledge gaps. We hypothesized that HST would show a reduced "personal goal-advantage effect" in MTT and would exhibit altered relationships with resting-state functional connectivity.

STUDY DESIGN: In Study 1, 37 HST and 40 individuals with low schizotypal traits (LST) were recruited. Participants generated MTT events with personal goal-related and personal goal-unrelated cues. In Study 2, 39 HST and 38 LST were recruited, they completed the same behavioral task and resting-state functional magnetic resonance imaging (fMRI) scanning.

STUDY RESULTS: Both Study 1 and Study 2 revealed that HST exhibited reduced "personal goal-advantage effect" on MTT specificity. Moreover, Study 2 showed that compared with LST, HST exhibited altered association between the "personal goal-advantage effect" and functional connectivity (ie, between the right precuneus and the left postcentral gyrus and "personal goal-advantage effect" on emotional valence, between the left hippocampus and the right temporal fusiform gyrus and "personal goal-advantage effect" on emotional intensity).

CONCLUSIONS: These findings suggest that HST exhibit a reduced "personal goal-advantage effect" in MTT specificity and altered neural correlates related to this effect. The "personal goal-advantage effect" may be a potential target for intervention in HST.

PMID:40037825 | DOI:10.1093/schbul/sbad183

Schizophr Bull. 2025 Mar 4;51(Supplement_2):S173-S182. doi: 10.1093/schbul/sbae171.

ABSTRACT

BACKGROUND AND HYPOTHESIS: Schizotypy can be utilized as a phenotypic risk marker for schizophrenia and its spectrum and might relate to putative dimensional biological markers of the psychosis spectrum. Among these are amygdala function and structure, which are impaired in schizophrenia, but possibly also correlated with subclinical expression of schizotypy in nonclinical samples. We tested whether different parameters relating to amygdala function would be different in healthy subjects with relatively higher vs lower schizotypy traits.

STUDY DESIGN: Sixty-three psychiatrically healthy subjects (42 with higher vs 21 with lower schizotypy scores, selected on the basis of the Oxford-Liverpool Inventory of Feelings and Experiences positive schizotypy subscale) underwent a multimodal imaging protocol, including functional magnetic resonance imaging (fMRI) during a task-based emotional (fearful) face recognition paradigm, arterial spin labeling for measurement of regional cerebral blood flow (rCBF) at rest, and resting-state fMRI for functional connectivity (FC) analyses, as well as a T1-weighted structural MRI scan.

STUDY RESULTS: The high schizotypy group showed significantly higher right amygdala activation during viewing of fearful emotional images and lower resting-state FC of the left amygdala with a cerebellum cluster, but no differences in resting-state amygdala rCBF or volume.

CONCLUSIONS: Our findings demonstrate a functionally relevant effect of schizotypy on amygdala activation in the absence of baseline rCBF or macroscopic structure. This suggests that while schizotypy might affect some functional or structural parameters in the brain, certain functionally relevant effects only emerge during cognitive or emotional triggers.

PMID:40037817 | DOI:10.1093/schbul/sbae171

Comput Biol Med. 2025 Mar 3;189:109920. doi: 10.1016/j.compbiomed.2025.109920. Online ahead of print.

ABSTRACT

Radial based non-Cartesian sequences may be used for silent functional MRI examinations particularly in settings where scanner noise could pose issues. However, to achieve reasonable temporal resolution, under-sampled 3D radial k-space commonly results in reduced image quality. In recent years, deep learning models for improving image quality have emerged. In this study, we investigate the applicability of deep learning image enhancement methods with a focus on preserving dynamic temporal signal changes. By utilizing high-resolution resting-state fMRI datasets from the Human Connectome Project (HCP) foundation, a ground-truth training set was constructed. The k-space trajectory coordinates of a so-called silent 'Looping Star' fMRI sequence was used to simulate non-Cartesian MRI data from the HCP datasets. Subsequently, these sparse resampled k-space were reconstructed, thereby generating pairs of simulated 'Looping Star' images and ground truth HCP images. The dataset served as the basis for training both 2D-UNet and 3D-UNet deep learning models for image enhancement. A comparative analysis was conducted, and the superior model was further fine-tuned. Evaluation of the final model's performance included standard image quality metrics as well as resting-state fMRI (rs-fMRI) analysis in the time-domain. The 3D-UNet outperformed the 2D-UNet in the image enhancement task, resulting in a significant reduction in error between the network input and the ground truth. Specifically, the 3D-UNet achieved a 97 % reduction in the mean square error between the simulated Looping Star input and the HCP ground truth in the pre-processed dataset. Moreover, the 3D-UNet successfully preserved voxel variations, observed as the correlated activity in the posterior cingulate cortex (PCC) during rs-fMRI analysis while simultaneously mitigating noise in the time-series images. In summary, image quality was improved and artifacts were effectively eliminated through the application of both 2D and 3D deep learning approaches. Comparative analysis of the networks indicated that the use of 3D convolutions is more advantageous than employing a deeper network with 2D convolutions, particularly in scenarios involving global artifacts. Furthermore by demonstrating that the trained neural network successfully preserved temporal characteristics in the BOLD signals, the results suggest applicability in fMRI studies.

PMID:40037172 | DOI:10.1016/j.compbiomed.2025.109920

BMC Psychiatry. 2025 Mar 3;25(1):198. doi: 10.1186/s12888-025-06534-8.

ABSTRACT

BACKGROUND: Previous studies indicate vitamin D impacts autism spectrum disorder (ASD), but its relationship with brain function is unclear. This study investigated the association between serum 25-hydroxyvitamin D [25(OH)D] levels and brain function in preschool children with ASD using resting-state functional magnetic resonance imaging (rs-fMRI), and explored correlations with clinical symptoms.

METHODS: A total of 226 ASD patients underwent rs-fMRI scanning and serum 25(OH)D testing. Clinical symptoms were assessed using Childhood Autism Rating Scale (CARS) and Autism Behavior Checklist (ABC). Patients were categorized into mild and severe groups based on the CARS, and further divided into normal (NVD), insufficient (VDI), and deficient (VDD) serum 25(OH)D levels. Changes in brain function among these groups were analyzed using regional homogeneity (ReHo), with ABC scores used for correlation analysis.

RESULTS: In mild ASD, ReHo increased in the right postcentral gyrus and left precuneus in the VDI and VDD groups compared to NVD, and decreased in the bilateral middle cingulate gyrus and left superior frontal gyrus in the VDD group compared to VDI. In severe ASD, ReHo decreased in the right middle occipital gyrus and increased in the right insula in the VDI group compared to NVD, and increased in the right superior frontal gyrus in the VDD group compared to VDI. Correlation analysis revealed that in mild ASD, ReHo in the right postcentral gyrus was positively correlated with body and object use scores in the NVD and VDI groups, while ReHo in the right middle cingulate gyrus was negatively correlated with relating scores in the VDD and VDI groups. In severe ASD, ReHo in the right insula was positively correlated with language scores in the NVD and VDI groups.

CONCLUSIONS: ASD patients with lower serum 25(OH)D levels show multiple brain functional abnormalities, with specific brain region alterations linked to symptom severity. These findings enhance our understanding of vitamin D's impact on ASD and suggest that future research may explore its therapeutic potential.

PMID:40033268 | DOI:10.1186/s12888-025-06534-8

eNeuro. 2025 Mar 3:ENEURO.0408-24.2025. doi: 10.1523/ENEURO.0408-24.2025. Online ahead of print.

ABSTRACT

Impulsivity is often considered a risk factor for drug addiction; however, not all evidence supports this view. In the present study, we used a food reward delay-discounting task (DDT) to categorize rats as low-, middle-, and high-impulsive but failed to find any difference among these groups in the acquisition and maintenance of cocaine self-administration, regardless of electrical foot-shock punishment. Additionally, there were no group differences in locomotor responses to acute cocaine in rats with or without a history of cocaine self-administration. Unexpectedly, chronic cocaine self-administration selectively increased impulsive choice in low-impulsive rats. Resting-state fMRI analysis revealed a positive correlation between impulsivity and cerebral blood volume in the midbrain, thalamus, and auditory cortex. Using these three regions as seeds, we observed a negative correlation between impulsivity and functional connectivity between the midbrain and frontal cortex, as well as between the thalamus and frontal cortex (including the orbitofrontal, primary, and parietal cortices) in low-impulsive rats. These correlations were attenuated following chronic cocaine self-administration. RNAscope in situ hybridization assays revealed a significant reduction in DA D1, D2, and D3 receptor mRNA expression in the corticostriatal regions of low-impulsive rats after cocaine self-administration. Our findings challenge the widely held view that impulsivity is a vulnerability factor for cocaine addiction. Instead, chronic cocaine use appears to selectively increase impulsive choice decision-making in low-impulsive rats, associated with reduced functional connectivity and DA receptor expression in the mesocorticolimbic DA network.Significance statement Impulsivity has long been considered a risk factor for substance use disorders (SUD). However, findings across different impulsivity measures have been inconsistent or controversial. In this study, we did not find evidence supporting the notion that preexisting choice impulsivity is a predictive factor for compulsive cocaine self-administration. Instead, we found that chronic cocaine self-administration led to a significant increase in impulsive choice decision-making in normally low-impulsive rats. This increase was associated with reduced functional connectivity and reduced dopamine receptor expression in the dopamine-related network. Our findings suggest that choice impulsivity does not predict SUD; rather, chronic cocaine use is a risk factor for developing impulsive behavior in healthy individuals.

PMID:40032530 | DOI:10.1523/ENEURO.0408-24.2025

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