Most recent paper

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

Life (Basel). 2025 Feb 13;15(2):290. doi: 10.3390/life15020290.

ABSTRACT

PURPOSE: Porto-systemic shunting (PSS) in patients with Abernethy malformation (AM) or obstruction of the portal vein (OVP) is often associated with normal liver parenchyma and hepatic function. This association provides an interesting natural model for studying the brain functional connectivity changes secondary to PSS but independently from hepatic (dys)function. Because PSS can be eliminated with appropriate interventions, these particular conditions offer a unique physio-pathological model where the same patient can be studied in both "active PSS" and "absent PSS" conditions (pre- and post-cure analyses).

METHODS: Four children (<18 years) who were evaluated for Abernethy malformation (n = 2) or portal cavernoma (n = 2) and underwent corrective surgery (living-donor liver transplantation for AM, or Meso-Rex bypass for OPV, respectively) were included in the study. Brain magnetic resonance imaging and resting-state functional magnetic resonance imaging (rest-fMRI) were acquired in all patients before and after the corrective surgery. A functional connectome analysis was performed before ("active PSS" condition) and after ("absent PSS"-physiological condition) the cure of PSS.

RESULTS: As a result of the cancelation of PSS, rest-fMRI connectomics revealed a statistically significant (p < 0.05 family-wise error) improvement in global brain functional connectivity in both groups following each surgical procedure.

CONCLUSIONS: In this clinical model of isolated PSS (with absence of hepatic dysfunction), brain functional connectivity was altered even in young patients and in the absence of hyperammonemia; moreover, specific interventions to cancel out PSS consequently significantly improved brain functional connectivity.

PMID:40003699 | DOI:10.3390/life15020290

Brain Sci. 2025 Feb 14;15(2):200. doi: 10.3390/brainsci15020200.

ABSTRACT

Growth mindset, which asserts that intelligence and abilities can be cultivated through effort and learning, has garnered substantial attention in psychological and educational research. While the psychological and behavioral impacts of growth mindset are well-established, the underlying neural mechanisms remain relatively underexplored. Furthermore, there is a lack of comprehensive reviews synthesizing the neural evidence on growth mindset, hindering a fuller understanding of this concept. This scoping review aims to synthesize existing empirical studies on the neural mechanisms of growth mindset, focusing on research objectives, methods, and participant characteristics. A total of 15 studies were reviewed, revealing six primary research objectives: (1) neural mechanisms of error and feedback processing, (2) domain-specific mindsets, (3) neural changes resulting from mindset interventions, (4) mindsets and grit, (5) the neuroanatomy of mindsets, and (6) neural mechanisms of stereotype violation, with error and feedback processing being the most frequently investigated. Ten of the 15 studies employed EEG, while other techniques included structural MRI, task-based fMRI, and resting-state fMRI, with the majority of research focusing on adult populations. Although the existing literature offers valuable insights, further research is needed to explore additional aspects of mindsets, particularly in children, and to refine the methodologies used to investigate the neural mechanisms underlying growth mindset.

PMID:40002532 | DOI:10.3390/brainsci15020200

Brain Behav. 2025 Feb;15(2):e70356. doi: 10.1002/brb3.70356.

ABSTRACT

INTRODUCTION: Cognitive impairment after a stroke significantly affects patients' quality of life, yet not all strokes lead to such impairment, and the underlying reasons remain unclear. This study employs resting-state functional magnetic resonance imaging (rs-fMRI) to compare subcortical stroke patients with and without cognitive impairment. Our goal is to identify distinct abnormalities in regional brain spontaneous activity and functional connectivity (FC) to better understand the neural basis of post-stroke cognitive outcomes.

METHODS: A total of 62 first-ever non-acute subcortical stroke patients were classified into post-stroke with abnormal cognition (PSAC) and with normal cognition (PSNC) groups. Rs-MRI was utilized to assess regional homogeneity (ReHo) in 32 PSAC, 30 PSNC, and 62 age- and sex-matched healthy controls (HC). Then we performed the seed-based whole-brain FC analysis based on the ReHo results. A partial correlation analysis examined the relationship between altered ReHo or FC and Montreal Cognitive Assessment (MoCA) scores.

RESULTS: It showed varied activity in cognitive-related brain regions in both stroke groups compared to HC, such as the right superior frontal gyrus, the right middle temporal gyrus, the right postcentral gyrus, and the left cerebellar lobules. The PSAC group had increased activity in the bilateral inferior temporal gyrus as well. Significant differences in activity were also found between PSAC and PSNC groups, with the PSAC group showing decreased activity in the left gyrus rectus (REC) and increased activity in cerebellar lobules. FC analysis revealed decreased connections in the PSAC group, particularly involving the left REC. Activity and FC in left REC and cerebellum also significantly correlated with MoCA scores.

CONCLUSIONS: These findings suggest unique patterns of brain activity and connectivity in non-acute subcortical stroke patients with cognitive impairment, shedding light on potential neural mechanisms underlying post-stroke cognitive impairment. While the left REC may be a potential neural regulatory stimulus target in clinical applications.

PMID:40001287 | DOI:10.1002/brb3.70356

J Neuroimaging. 2025 Jan-Feb;35(1):e70027. doi: 10.1111/jon.70027.

ABSTRACT

BACKGROUND AND PURPOSE: In neurosurgery, functional MRI is crucial for preoperative planning to obtain the cortical cortex map of language areas. This preliminary work involved analyzing the functional MRIs of 20 oncological patients. Our question is if resting-state functional MRI (rs-fMRI) can replace standard task-based functional MRI (tb-fMRI) in routine clinical applications. The aim of this challenge is to determine if rs-fMRI is as effective as tb-fMRI and to develop a systematic approach for the extraction of a cortical language map.

METHODS: We started by analyzing our rs-fMRI images and validated the correct mapping of language regions using an independent components analysis approach; then, we used the analysis of connectivity networks to compare the two techniques.

RESULTS: The regions identified in rs-fMRI align with established medical knowledge; a comparison of rs-fMRI and tb-fMRI reveals that the four language regions-Broca's and Wernicke's areas in both hemispheres-exhibit activation in both techniques; furthermore, we highlighted that rs-fMRI reveals more comprehensive details about functional connectivity in contrast to tb-fMRI.

CONCLUSIONS: rs-MRI and tb-MRI provide similar levels of efficacy in revealing the functional areas of the brain for preoperative mapping when a lesion lies in areas related to language; thus, both techniques can be utilized for this goal. Based on this, we developed an rs-fMRI processing pipeline for clinical usage and applied it to a patient outside the study.

PMID:40000389 | DOI:10.1111/jon.70027

Brain. 2025 Feb 25:awaf075. doi: 10.1093/brain/awaf075. Online ahead of print.

ABSTRACT

Functional neuroimaging has provided several new tools for improving both the diagnosis and prognosis in patients with DoC. These tools are now being used to detect residual and covert awareness in behaviourally non-responsive patients with an acquired severe brain injury and predict which patients are likely to recover. Despite endorsement of advanced imaging by multiple clinical bodies, widespread implementation of imaging techniques such as functional MRI (fMRI), electroencephalography (EEG), and positron emission tomography (PET) in both acute and prolonged disorders of consciousness patients has been hindered by perceived costs, technological barriers, and lack of expertise needed to acquire, interpret, and implement these methods. In this review we provide a comprehensive overview of neuroimaging in DoC, the different technical approaches employed (i.e. fMRI, EEG, PET), the imaging paradigms used (active, passive, resting state) and the types of inferences that have been made about residual cortical function based on those paradigms (e.g., perception, awareness, communication). Next, we outline how these barriers might be overcome, discuss which select patients stand to benefit the most from these neuroimaging techniques, and consider when during their clinical trajectory imaging tests are likely to be most useful. Moreover, we make recommendations that will help clinicians decide which advanced imaging technologies and protocols are likely to be most appropriate in any particular clinical case. Finally, we describe how these techniques can be implemented in routine clinical care to augment current clinical tools and outline future directions for the field as a whole.

PMID:39997570 | DOI:10.1093/brain/awaf075

Mov Disord. 2025 Feb 25. doi: 10.1002/mds.30146. Online ahead of print.

ABSTRACT

BACKGROUND: Freezing of gait (FOG) is a common gait disorder that often accompanies Parkinson's disease (PD). The current understanding of brain functional organization in FOG was built on the assumption that the functional connectivity (FC) of networks is static, but FC changes dynamically over time. We aimed to characterize the dynamic functional connectivity (DFC) in patients with FOG based on high temporal-resolution functional MRI (fMRI).

METHODS: Eighty-seven PD patients, including 29 with FOG and 58 without FOG, and 32 healthy controls underwent resting-state fMRI. Spatial independent component analysis and a sliding-window approach were used to estimate DFC.

RESULTS: Four patterns of structured FC 'states' were identified: a frequent and sparsely connected network (State I), a less frequent but highly synchronized network (State IV), and two states with opposite connecting directions between the visual network and the sensorimotor network (positively connected in State II, negatively connected in State III). Compared with the non-FOG group, patients with FOG spent significantly less time in State II and more time in State III. The longer dwell time in State III was correlated with more severe FOG symptoms. The fractional window of State III tended to correlate to visual-spatial and executive dysfunction in FOG. Moreover, fewer transitions between brain states and lower variability in local efficiency were observed in FOG, suggesting a relatively 'rigid' brain.

CONCLUSIONS: This study highlights how visuomotor network dynamics are related to the presence and severity of FOG in PD patients, which provides new insights into understanding the pathophysiological mechanisms that underly FOG. © 2025 International Parkinson and Movement Disorder Society.

PMID:39996352 | DOI:10.1002/mds.30146

Front Aging Neurosci. 2025 Feb 10;17:1522591. doi: 10.3389/fnagi.2025.1522591. eCollection 2025.

ABSTRACT

The underlying neuropathological mechanisms in Binswanger's disease (BD) with mild cognitive impairment (BD-MCI) remain unclear. The multiparameter functional magnetic resonance imaging (fMRI) including amplitude of low-frequency fluctuations (ALFF), fractional amplitude of low-frequency fluctuations (fALFF), regional homogeneity (ReHo), independent component analysis (ICA), and edge-link analysis was utilized to explore the abnormal brain networks of BD-MCI patients. Compared with the BD without MCI group, this study revealed that the ALFF values in the BD-MCI group were significantly increased in the Temporal_Inf_R, Frontal_Mid_Orb_L, and Hippocampus_L, while decreased in the SupraMarginal_R and Precuneus_R. The fALFF value in the BD-MCI group exhibited a reduction in the Frontal_Med_Orb_L. Additionally, ReHo values in the BD-MCI group increased in the Hippocampus_R but decreased in several areas including Precentral_L, Putamen_L, Postcentral_R, Supp_Motor_Area_R, and SupraMarginal_L. The results of ICA revealed that patients diagnosed with BD-MCI exhibited abnormal connectivity patterns across 12 groups of independent components and 5 distinct groups of brain networks. In one group, the internal connectivity within the brain network exhibited abnormalities. The correlation analysis between ALFF and ReHo values and clinical scales revealed a significant negative correlation between the bilateral hippocampus and Mini-Mental State Examination (MMSE) scores. Conversely, ReHo values for Postcentral_R and SupraMarginal_L were significantly positively correlated with MMSE scores. In summary, the results of our study suggest that patients diagnosed with BD-MCI display atypical activity across several brain regions. The observed changes in these areas encompass a range of functional networks. The reduced coordination among these functional networks may play a role in the deterioration of cognitive functions and decision-making capabilities, potentially serving as a critical mechanism contributing to the early manifestation of cognitive impairments.

PMID:39995946 | PMC:PMC11847846 | DOI:10.3389/fnagi.2025.1522591

Front Neurol. 2025 Feb 10;16:1498049. doi: 10.3389/fneur.2025.1498049. eCollection 2025.

ABSTRACT

INTRODUCTION: The brain's spontaneous neural activity can be recorded during rest using resting state functional magnetic resonance imaging (rs-fMRI), and intricate brain functional networks and interaction patterns can be discovered through correlation analysis. As a crucial component of rs-fMRI analysis, effective connectivity analysis (EC) may provide a detailed description of the causal relationship and information flow between different brain areas. It has been very helpful in identifying anomalies in the brain activity of depressed teenagers.

METHODS: This study explored connectivity abnormalities in brain networks and their impact on clinical symptoms in patients with depression through resting state functional magnetic resonance imaging (rs-fMRI) and effective connectivity (EC) analysis. We first introduce some common EC analysis methods, discuss their application background and specific characteristics.

RESULTS: EC analysis reveals information flow problems between different brain regions, such as the default mode network, the central executive network, and the salience network, which are closely related to symptoms of depression, such as low mood and cognitive impairment. This review discusses the limitations of existing studies while summarizing the current applications of EC analysis methods. Most of the early studies focused on the static connection mode, ignoring the causal relationship between brain regions. However, effective connection can reflect the upper and lower relationship of brain region interaction, and provide help for us to explore the mechanism of neurological diseases. Existing studies focus on the analysis of a single brain network, but rarely explore the interaction between multiple key networks.

DISCUSSION: To do so, we can address these issues by integrating multiple technologies. The discussion of these issues is reflected in the text. Through reviewing various methods and applications of EC analysis, this paper aims to explore the abnormal connectivity patterns of brain networks in patients with depression, and further analyze the relationship between these abnormalities and clinical symptoms, so as to provide more accurate theoretical support for early diagnosis and personalized treatment of depression.

PMID:39995788 | PMC:PMC11847690 | DOI:10.3389/fneur.2025.1498049

Quant Imaging Med Surg. 2025 Feb 1;15(2):1653-1668. doi: 10.21037/qims-24-916. Epub 2025 Jan 22.

ABSTRACT

BACKGROUND: Migraine is a common, disabling, chronic headache disorder. Acupuncture is one of the effective complementary therapies for migraine. However, the neural mechanisms of acupuncture on migraine remain unclear. With the increased number of neuroimaging studies of acupuncture for migraine in recent years, there is an urgent need to update the data for pooled analyses. This study aimed to comprehensively summarize the relevant literature, identify brain regions with significant changes in brain activity after acupuncture, and explore the potential neural mechanisms of acupuncture on migraine.

METHODS: A search was conducted by two independent researchers for neuroimaging studies using resting-state functional magnetic resonance imaging (fMRI) or positron emission tomography (PET) on the effects of acupuncture on migraine up to October 2023 in the databases of PubMed, MEDLINE, Embase, China National Knowledge Infrastructure (CNKI), Wanfang Data, Chinese Science and Technology Journal Database (VIP), and Chinese Biomedical Literature Database (SinoMed). Observational studies and clinical trials in Chinese or English were included; abstracts and studies without peer review were excluded. Brain regions with increased or decreased activity in the true acupuncture (TA) and sham acupuncture (SA) groups were pooled. A meta-analysis was performed using the activation likelihood estimation (ALE) algorithm. Fail-safe N (FSN) analysis was performed for publication bias and jackknife analysis was implemented for sensitivity analysis.

RESULTS: The ALE meta-analysis included 15 peer-reviewed functional brain imaging studies with 514 migraine patients (401 female; mean age 32.38 years) and 163 healthy controls (130 female; mean age 27.28 years). A total of 12 studies scored 18 and above on the quality assessment (out of a total of 20). The results showed two increased activity clusters (the left pons and posterior insula) and four decreased activity clusters [the left cerebellum, temporal lobe, and right precuneus (two clusters)] after TA relative to baseline (P<0.001 uncorrected, volume >100 mm3). We also identified five clusters of increased and seven clusters of decreased activity of SA relative to the baseline, and no overlap regions were found between the TA and SA groups (P<0.001 uncorrected, volume >100 mm3). The results showed high replicability and reliability.

CONCLUSIONS: Acupuncture for migraine is a complex but targeted neuromodulation process, different from the random, nonspecific effects of SA. Emotional processing and sensitization reduction may be critical neurofunctional mechanisms of acupuncture. More high-quality randomized controlled studies are needed to validate the results.

PMID:39995740 | PMC:PMC11847202 | DOI:10.21037/qims-24-916

Autism Res. 2025 Feb 25. doi: 10.1002/aur.70008. Online ahead of print.

ABSTRACT

Autism spectrum disorder (ASD) is a type of neurodevelopmental disorder characterized by atypical brain development. Previous whole-brain BrainAGE studies have unveiled the presence of accelerated or delayed brain function developmental patterns in individuals with ASD. However, it remains unclear whether these patterns manifest at a global level throughout the entire brain or are specific to certain functional sub-networks. The study included resting-state functional magnetic resonance imaging (fMRI) data from 127 individuals with ASD and 135 healthy controls (aged between 5 and 40 years). ALFF maps were measured for each participant. Then, sub-network-level BrainAGE analyses were conducted across 10 sub-networks using the Individual-weighted Multilayer Perceptron Network (ILWMLP) regression method. The BrainAGE analyses revealed atypical developmental trajectories in sensorimotor-related sub-networks, encompassing auditory, motor, and sensorimotor sub-networks. In individuals with ASD, delayed brain function development was observed in the auditory and sensorimotor networks, with a more pronounced delay observed in older individuals. Conversely, the motor network exhibited accelerated development in younger individuals but delayed development in older individuals. Our findings unveiled aberrant developmental patterns in sensorimotor-related sub-networks among individuals with ASD, exhibiting distinct atypical profiles across different sub-networks. These results might contribute to a deeper understanding of the deviant brain development observed in ASD.

PMID:39995361 | DOI:10.1002/aur.70008

Cogn Neurodyn. 2025 Dec;19(1):41. doi: 10.1007/s11571-025-10223-3. Epub 2025 Feb 20.

ABSTRACT

Rumination thinking is a type of negative repetitive thinking, a tendency to constantly focus on the causes, consequences and other aspects of negative events, which has implications for a variety of psychiatric disorders. Previous studies have confirmed a strong association between rumination thinking and poor sleep or insomnia, but the direction of causality between the two is not entirely clear. This study examined the relationship between rumination thinking and sleep quality using a longitudinal approach and resting-state functional MRI data. Participants were 373 university students (males: n = 84, 18.67 ± 0.76 years old) who completed questionnaires at two time points (T1 and T2) and had resting-state MRI data collected. The results of the cross-lagged model analysis revealed a bidirectional causal relationship between rumination thinking and sleep quality. Additionally, the functional connectivity (FC) of the precuneus and lingual gyrus was found to be negatively correlated with rumination thinking and sleep quality. Furthermore, mediation analysis showed that rumination thinking at T1 fully mediated the relationship between FC of the precuneus-lingual and sleep quality at T2. These findings suggest that rumination thinking and sleep quality are causally related in a bidirectional manner and that the FC of the precuneus and lingual gyrus may serve as the neural basis for rumination thinking to predict sleep quality. Overall, this study provides new insights for enhancing sleep quality and promoting overall health.

SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11571-025-10223-3.

PMID:39991016 | PMC:PMC11842644 | DOI:10.1007/s11571-025-10223-3