Neuromodulation techniques for modulating cognitive function: Enhancing stimulation precision and intervention effects

Neuromodulation techniques effectively intervene in cognitive function, holding considerable scientific and practical value in fields such as aerospace, medicine, life sciences, and brain research. These techniques utilize electrical stimulation to directly or indirectly target specific brain regions, modulating neural activity and influencing broader brain networks, thereby regulating cognitive function. Regulating cognitive function involves an understanding of aspects such as perception, learning and memory, attention, spatial cognition, and physical function. To enhance the application of cognitive regulation in the general population, this paper reviews recent publications from the Web of Science to assess the advancements and challenges of invasive and non-invasive stimulation methods in modulating cognitive functions. This review covers various neuromodulation techniques for cognitive intervention, including deep brain stimulation, vagus nerve stimulation, and invasive methods using microelectrode arrays. The non-invasive techniques discussed include transcranial magnetic stimulation, transcranial direct current stimulation, transcranial alternating current stimulation, transcutaneous electrical acupoint stimulation, and time interference stimulation for activating deep targets. Invasive stimulation methods, which are ideal for studying the pathogenesis of neurological diseases, tend to cause greater trauma and have been less researched in the context of cognitive function regulation. Non-invasive methods, particularly newer transcranial stimulation techniques, are gentler and more appropriate for regulating cognitive functions in the general population. These include transcutaneous acupoint electrical stimulation using acupoints and time interference methods for activating deep targets. This paper also discusses current technical challenges and potential future breakthroughs in neuromodulation technology. It is recommended that neuromodulation techniques be combined with neural detection methods to better assess their effects and improve the accuracy of non-invasive neuromodulation. Additionally, researching closed-loop feedback neuromodulation methods is identified as a promising direction for future development.

Is age-related myelinodegenerative change an initial risk factor of neurodegenerative diseases?

Myelination, the continuous ensheathment of neuronal axons, is a lifelong process in the nervous system that is essential for the precise, temporospatial conduction of action potentials between neurons. Myelin also provides intercellular metabolic support to axons. Even minor disruptions in the integrity of myelin can impair neural performance and increase susceptibility to neurological diseases. In fact, myelin degeneration is a well-known neuropathological condition that is associated with normal aging and several neurodegenerative diseases, including multiple sclerosis and Alzheimer's disease. In the central nervous system, compact myelin sheaths are formed by fully mature oligodendrocytes. However, the entire oligodendrocyte lineage is susceptible to changes in the biological microenvironment and other risk factors that arise as the brain ages. In addition to their well-known role in action potential propagation, oligodendrocytes also provide intercellular metabolic support to axons by transferring energy metabolites and delivering exosomes. Therefore, myelin degeneration in the aging central nervous system is a significant contributor to the development of neurodegenerative diseases. Interventions that mitigate age-related myelin degeneration can improve neurological function in aging individuals. In this review, we investigate the changes in myelin that are associated with aging and their underlying mechanisms. We also discuss recent advances in understanding how myelin degeneration in the aging brain contributes to neurodegenerative diseases and explore the factors that can prevent, slow down, or even reverse age-related myelin degeneration. Future research will enhance our understanding of how reducing age-related myelin degeneration can be used as a therapeutic target for delaying or preventing neurodegenerative diseases.

Pathological axonal enlargement in connection with amyloidosis, lysosome destabilization, and bleeding is a major defect in Alzheimer's disease

JOURNAL/nrgr/04.03/01300535-202602000-00047/figure1/v/2025-05-05T160104Z/r/image-tiff Alzheimer's disease is a multi-amyloidosis disease characterized by amyloid-β deposits in brain blood vessels, microaneurysms, and senile plaques. How amyloid-β deposition affects axon pathology has not been examined extensively. We used immunohistochemistry and immunofluorescence staining to analyze the forebrain tissue slices of Alzheimer's disease patients. Widespread axonal amyloidosis with distinctive axonal enlargement was observed in patients with Alzheimer's disease. On average, amyloid-β-positive axon diameters in Alzheimer's disease brains were 1.72 times those of control brain axons. Furthermore, axonal amyloidosis was associated with microtubule-associated protein 2 reduction, tau phosphorylation, lysosome destabilization, and several blood-related markers, such as apolipoprotein E, alpha-hemoglobin, glycosylated hemoglobin type A1C, and hemin. Lysosome destabilization in Alzheimer's disease was also clearly identified in the neuronal soma, where it was associated with the co-expression of amyloid-β, Cathepsin D, alpha-hemoglobin, actin alpha 2, and collagen type IV. This suggests that exogenous hemorrhagic protein intake influences neural lysosome stability. Additionally, the data showed that amyloid-β-containing lysosomes were 2.23 times larger than control lysosomes. Furthermore, under rare conditions, axonal breakages were observed, which likely resulted in Wallerian degeneration. In summary, axonal enlargement associated with amyloidosis, micro-bleeding, and lysosome destabilization is a major defect in patients with Alzheimer's disease. This finding suggests that, in addition to the well-documented neural soma and synaptic damage, axonal damage is a key component of neuronal defects in Alzheimer's disease.

Targeting the brain's glymphatic pathway: A novel therapeutic approach for cerebral small vessel disease

Cerebral small vessel disease encompasses a group of neurological disorders characterized by injury to small blood vessels, often leading to stroke and dementia. Due to its diverse etiologies and complex pathological mechanisms, preventing and treating cerebral small vessel vasculopathy is challenging. Recent studies have shown that the glymphatic system plays a crucial role in interstitial solute clearance and the maintenance of brain homeostasis. Increasing evidence also suggests that dysfunction in glymphatic clearance is a key factor in the progression of cerebral small vessel disease. This review begins with a comprehensive introduction to the structure, function, and driving factors of the glymphatic system, highlighting its essential role in brain waste clearance. Afterwards, cerebral small vessel disease was reviewed from the perspective of the glymphatic system, after which the mechanisms underlying their correlation were summarized. Glymphatic dysfunction may lead to the accumulation of metabolic waste in the brain, thereby exacerbating the pathological processes associated with cerebral small vessel disease. The review also discussed the direct evidence of glymphatic dysfunction in patients and animal models exhibiting two subtypes of cerebral small vessel disease: arteriolosclerosis-related cerebral small vessel disease and amyloid-related cerebral small vessel disease. Diffusion tensor image analysis along the perivascular space is an important non-invasive tool for assessing the clearance function of the glymphatic system. However, the effectiveness of its parameters needs to be enhanced. Among various nervous system diseases, including cerebral small vessel disease, glymphatic failure may be a common final pathway toward dementia. Overall, this review summarizes prevention and treatment strategies that target glymphatic drainage and will offer valuable insight for developing novel treatments for cerebral small vessel disease.

Measuring glymphatic function: Assessing the toolkit

Glymphatic flow has been proposed to clear brain waste while we sleep. Cerebrospinal fluid moves from periarterial to perivenous spaces through the parenchyma, with subsequent cerebrospinal fluid drainage to dural lymphatics. Glymphatic disruption is associated with neurological conditions such as Alzheimer's disease and traumatic brain injury. Therefore, investigating its structure and function may improve understanding of pathophysiology. The recent controversy on whether glymphatic flow increases or decreases during sleep demonstrates that the glymphatic hypothesis remains contentious. However, discrepancies between different studies could be due to limitations of the specific techniques used and confounding factors. Here, we review the methods used to study glymphatic function and provide a toolkit from which researchers can choose. We conclude that tracer analysis has been useful, ex vivo techniques are unreliable, and in vivo imaging is still limited. Finally, we explore the potential for future methods and highlight the need for in vitro models, such as microfluidic devices, which may address technique limitations and enable progression of the field.

Chemical exchange saturation transfer MRI for neurodegenerative diseases: An update on clinical and preclinical studies

Chemical exchange saturation transfer magnetic resonance imaging is an advanced imaging technique that enables the detection of compounds at low concentrations with high sensitivity and spatial resolution and has been extensively studied for diagnosing malignancy and stroke. In recent years, the emerging exploration of chemical exchange saturation transfer magnetic resonance imaging for detecting pathological changes in neurodegenerative diseases has opened up new possibilities for early detection and repetitive scans without ionizing radiation. This review serves as an overview of chemical exchange saturation transfer magnetic resonance imaging with detailed information on contrast mechanisms and processing methods and summarizes recent developments in both clinical and preclinical studies of chemical exchange saturation transfer magnetic resonance imaging for Alzheimer's disease, Parkinson's disease, multiple sclerosis, and Huntington's disease. A comprehensive literature search was conducted using databases such as PubMed and Google Scholar, focusing on peer-reviewed articles from the past 15 years relevant to clinical and preclinical applications. The findings suggest that chemical exchange saturation transfer magnetic resonance imaging has the potential to detect molecular changes and altered metabolism, which may aid in early diagnosis and assessment of the severity of neurodegenerative diseases. Although promising results have been observed in selected clinical and preclinical trials, further validations are needed to evaluate their clinical value. When combined with other imaging modalities and advanced analytical methods, chemical exchange saturation transfer magnetic resonance imaging shows potential as an in vivo biomarker, enhancing the understanding of neuropathological mechanisms in neurodegenerative diseases.

Neuronal plasticity and its role in Alzheimer's disease and Parkinson's disease

Neuronal plasticity, the brain's ability to adapt structurally and functionally, is essential for learning, memory, and recovery from injuries. In neurodegenerative diseases such as Alzheimer's disease and Parkinson's disease, this plasticity is disrupted, leading to cognitive and motor deficits. This review explores the mechanisms of neuronal plasticity and its effect on Alzheimer's disease and Parkinson's disease. Alzheimer's disease features amyloid-beta plaques and tau tangles that impair synaptic function, while Parkinson's disease involves the loss of dopaminergic neurons affecting motor control. Enhancing neuronal plasticity offers therapeutic potential for these diseases. A systematic literature review was conducted using databases such as PubMed, Scopus, and Google Scholar, focusing on studies of neuronal plasticity in Alzheimer's disease and Parkinson's disease. Data synthesis identified key themes such as synaptic mechanisms, neurogenesis, and therapeutic strategies, linking molecular insights to clinical applications. Results highlight that targeting synaptic plasticity mechanisms, such as long-term potentiation and long-term depression, shows promise. Neurotrophic factors, advanced imaging techniques, and molecular tools (e.g., clustered regularly interspaced short palindromic repeats and optogenetics) are crucial in understanding and enhancing plasticity. Current therapies, including dopamine replacement, deep brain stimulation, and lifestyle interventions, demonstrate the potential to alleviate symptoms and improve outcomes. In conclusion, enhancing neuronal plasticity through targeted therapies holds significant promise for treating neurodegenerative diseases. Future research should integrate multidisciplinary approaches to fully harness the therapeutic potential of neuronal plasticity in Alzheimer's disease and Parkinson's disease.

Synaptic and synchronic impairments in subcortical brain regions associated with early non-cognitive dysfunction in Alzheimer's disease

For many decades, Alzheimer's disease research has primarily focused on impairments within cortical and hippocampal regions, which are thought to be related to cognitive dysfunctions such as memory and language deficits. The exact cause of Alzheimer's disease is still under debate, making it challenging to establish an effective therapy or early diagnosis. It is widely accepted that the accumulation of amyloid-beta peptide in the brain parenchyma leads to synaptic dysfunction, a critical step in Alzheimer's disease development. The traditional amyloid cascade model is initiated by accumulating extracellular amyloid-beta in brain areas essential for memory and language. However, while it is possible to reduce the presence of amyloid-beta plaques in the brain with newer immunotherapies, cognitive symptoms do not necessarily improve. Interestingly, recent studies support the notion that early alterations in subcortical brain regions also contribute to brain damage and precognitive decline in Alzheimer's disease. A body of recent evidence suggests that early Alzheimer's disease is associated with alterations (e.g., motivation, anxiety, and motor impairment) in subcortical areas, such as the striatum and amygdala, in both human and animal models. Also, recent data indicate that intracellular amyloid-beta appears early in subcortical regions such as the nucleus accumbens, locus coeruleus, and raphe nucleus, even without extracellular amyloid plaques. The reported effects are mainly excitatory, increasing glutamatergic transmission and neuronal excitability. In agreement, data in Alzheimer's disease patients and animal models show an increase in neuronal synchronization that leads to electroencephalogram disturbances and epilepsy. The data indicate that early subcortical brain dysfunctions might be associated with non-cognitive symptoms such as anxiety, irritability, and motivation deficits, which precede memory loss and language alterations. Overall, the evidence reviewed suggests that subcortical brain regions could explain early dysfunctions and perhaps be targets for therapies to slow disease progression. Future research should focus on these non-traditional brain regions to reveal early pathological alterations and underlying mechanisms to advance our understanding of Alzheimer's disease beyond the traditionally studied hippocampal and cortical circuits.

Cerebellar microglia: On the edge between neuroinflammation and neuroregulation

The cerebellum is receiving increasing attention for its cognitive, emotional, and social functions, as well as its unique metabolic profiles. Cerebellar microglia exhibit specialized and highly immunogenic phenotypes under both physiological and pathological conditions. These immune cells communicate with intrinsic and systemic factors and contribute to the structural and functional compartmentalization of the cerebellum. In this review, we discuss the roles of microglia in the cerebellar microenvironment, neuroinflammation, cerebellar adaptation, and neuronal activity, the associated molecular and cellular mechanisms, and potential therapeutic strategies targeting cerebellar microglia in the context of neuroinflammation. Future directions and unresolved questions in this field are further highlighted, particularly regarding therapeutic interventions targeting cerebellar microglia, functional mechanisms and activities of microglia in the cerebellar circuitry, neuronal connectivity, and neurofunctional outcomes of their activity. Cerebellar morphology and neuronal performance are influenced by both intrinsic and systemic factors that are actively monitored by microglia in both healthy and diseased states. Under pathological conditions, local subsets of microglia exhibit diverse responses to the altered microenvironment that contribute to the structural and functional compartmentalization of the cerebellum. Microglia in the cerebellum undergo early maturation during the embryonic stage and display specialized, highly immunogenic phenotypes. In summary, cerebellar microglia have the capacity to serve as regulatory tools that influence outcomes across a wide range of neurological and systemic conditions, including neurodevelopmental, neurodegenerative, metabolic, and stress-related disorders.

Respiratory modulation of beta corticomuscular coherence in isometric hand movements

Respiration is a fundamental physiological function in humans, often synchronized with movement to enhance performance and efficiency. Recent studies have underscored the modulatory effects of respiratory rhythms on brain oscillations and various behavioral responses, including sensorimotor processes. In light of this connection, our study aimed to investigate the influence of different respiratory patterns on beta corticomuscular coherence (CMC) during isometric hand flexion and extension. Utilizing electroencephalogram (EEG) and surface electromyography (sEMG), we examined three breathing conditions: normal breathing, deep inspiration, and deep expiration. Two experimental protocols were employed: the first experiment required participants to simultaneously breathe and exert force, while the other involved maintaining a constant force while varying breathing patterns. The results revealed that deep inspiration significantly enhanced beta CMC during respiration-synchronized tasks, whereas normal breathing resulted in higher CMC compared to deep respiration during sustained force exertion. In the second experiment, beta CMC was cyclically modulated by respiratory phase across all breathing conditions. The difference in the outcomes from the two protocols demonstrated a task-specific modulation of respiration on motor control. Overall, these findings indicate the complex dynamics of respiration-related effects on corticomuscular neural communication and provide valuable insights into the mechanisms underpinning the coupling between respiration and motor function.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11571-025-10245-x.

Teaching CORnet human fMRI representations for enhanced model-brain alignment

Deep convolutional neural networks (DCNNs) have demonstrated excellent performance in object recognition and have been found to share some similarities with brain visual processing. However, the substantial gap between DCNNs and human visual perception still exists. Functional magnetic resonance imaging (fMRI) as a widely used technique in cognitive neuroscience can record neural activation in the human visual cortex during the process of visual perception. Can we teach DCNNs human fMRI signals to achieve a more brain-like model? To answer this question, this study proposed ReAlnet-fMRI, a model based on the SOTA vision model CORnet but optimized using human fMRI data through a multi-layer encoding-based alignment framework. This framework has been shown to effectively enable the model to learn human brain representations. The fMRI-optimized ReAlnet-fMRI exhibited higher similarity to the human brain than both CORnet and the control model in within- and across-subject as well as within- and across-modality model-brain (fMRI and EEG) alignment evaluations. Additionally, we conducted an in-depth analysis to investigate how the internal representations of ReAlnet-fMRI differ from CORnet in encoding various object dimensions. These findings provide the possibility of enhancing the brain-likeness of visual models by integrating human neural data, helping to bridge the gap between computer vision and visual neuroscience.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11571-025-10252-y.

Back to the pre-industrial age? FAOSTAT statistics of food supply reveal radical dietary changes accompanied by declining body height, rising obesity rates, and declining phenotypic IQ in affluent Western countries

Meta-analyses of observational and clinical studies conducted in recent years have raised serious doubts about the validity of the low-fat dietary recommendations introduced in the late 1970s/early 1980s, due to the absence of any convincing link between saturated fat and the risk of cardiovascular diseases. At the same time, long-term food supply statistics from the FAOSTAT database show that these recommendations were at the root of fundamental dietary changes in Western countries, which resulted in a lower consumption of eggs and red meat, a higher consumption of cereals and poultry, a decline in average protein quality and, overall, in a higher glycemic load of the diet. Because current views on human nutrition are based primarily on highly unreliable questionnaire data from observational studies, the purpose of this commentary is to provide an alternative ecological (country-level) perspective and to trace the consequences of these nutritional changes using the FAOSTAT database in combination with available anthropological and health statistics. This comparison shows a close connection between the decline in protein quality and the sudden reversal of the positive height trend in some Western countries, after ∼150 years of continuous growth, which points to suboptimal levels of child nutrition. The sharp increase in the prevalence of obesity and type 2 diabetes is strongly correlated with the increasing consumption of high-glycemic carbohydrates and sweeteners, and is also interconnected with the decrease in body height, because a high-quality, growth-stimulating diet during adolescence is inversely related to obesity. Given the long-term association between height and phenotypic IQ, the lower quality of nutrients in children's diet may also seriously affect intellectual potential and future civilizational development. In light of these findings, current nutritional strategies should be seriously reconsidered and recommended protein intakes for children must be urgently reevaluated.

Acute effects of isolated and combined dietary nitrate and caffeine ingestion on ergometer-based 1000 m time trial performance in highly trained kayakers

BACKGROUND

Dietary nitrate (BR) and caffeine (CAF) ingestion have been shown to increase sports performance. However, the isolated and combined effects of BR and CAF ingestion on time trial (TT) performance as well as the accompanying physiological and perceptual responses have never been investigated in highly trained kayak athletes. Therefore, the present study examined the impact of an isolated and combined supplementation with BR (140 ml beetroot concentrate, ~12.5 mmol nitrate) and CAF (3 mg/kg bodyweight) on 1000 m ergometer TT performance as well as the accompanying physiological (i.e. cardiorespiratory function, muscle oxygenation, muscle activity) and perceptual responses (i.e. fatigue, effort, and exercise-induced pain perception) in male highly trained kayakers. It was hypothesized that the isolated ingestion of BR and CAF would both improve ergometer-based 1000 m TT performance and induce supplement-specific physiological and perceptual responses. Considering the primary effects of BR on muscle function and of CAF on the central nervous system, it was further assumed that the combined ingestion will result in an additional performance increase and supplement-specific physiological and perceptual responses.

METHODS

Using a prospective, randomized, controlled, double-blind crossover design, 12 male highly trained kayak athletes from local clubs were investigated. They completed four measurement sessions resulting in four randomized conditions: (i) BR+CAF; (ii) BR+CAF placebo (BR+PLA); (iii) CAF+BR placebo (CAF+PLA); and (iv) BR placebo + CAF placebo (PLA+PLA). An air-braked instrumented kayak-ergometer was used to record 1000 m TT performance, power output, and stroke frequency. Heart rate (HR), oxygen uptake (VO2), maximum VO2 (VO2max), respiratory equivalent of O2 (VE/VO2), and carbon dioxide (VE/VCO2) were measured continuously. Furthermore, oxygenation of the deltoid muscle was measured with near-infrared spectroscopy (mNIRS) and muscle activity of nine unilateral muscles with surface electromyography (i.e. deltoideus, serratus anterior, triceps brachii caput lateralis, trapezius, infraspinatus, latissimus dorsi, obliquus externus, flexor carpi radialis, and vastus lateralis muscle) during the 1000 m TT. After the TT, fatigue, effort, and exercise-induced pain perception were queried. One- and two-way analysis of variance with repeated measures were conducted to determine differences between conditions for the entire 1000 m TT and predefined sections (0-50 m, 50-100 m, 100-150 m, 150-250 m, 250-500 m, 500-750 m, 750-1000 m), respectively (p ≤ 0.05).

RESULTS

The supplements did not have an ergogenic effect on TT performance compared to the PLA+PLA condition, either in isolation or in combination. The same applied to the majority of physiological parameters and the perceptual responses. Nevertheless, VE/VO2 was lower during the sections 150-250 m (-5.00%; p = 0.02) and 250-500 m (-3.49%; p = 0.03) in the BR+PLA condition, whereby VE/VCO2 was higher during the section 150-250 m (4.19%; p = 0.04) in the CAF+PLA compared to the PLA+PLA condition, respectively.

CONCLUSIONS

Data indicate that the isolated and combined ingestion of BR and CAF had no effect on 1000 m TT performance, the majority of physiological responses, and perceptual responses in highly trained kayakers. These findings might be related to the dosage and/or a ceiling effect due to the already efficient vascular, metabolic, and muscle function, including high amounts of endogenous produced nitric oxide, in athletes.

Altered effective connectivity within brain lesioned regions and cognitive impairment after stroke

Poststroke cognitive impairments (PSCI) reflect widespread network dysfunction due to structural damage, abnormal neural activity, or abnormal connections in affected brain regions. The exact influence of these lesioned regions on the related functional network and their role in PSCI remains unclear. We recruited 35 first-time stroke patients who had basal ganglia infarcts and PSCI, along with 29 age-matched healthy controls. We utilized T1-weighted imaging to inspect structural damage with regional gray matter volume (GMV). Resting-state fMRI data were utilized to examine spontaneous activities with regional Wavelet-ALFF metric, investigate dynamic functional connectivity (dFC) by seeding the region with damaged GMV, and further study effective connectivity within the abnormal dFC network and its impact on PSCI. In comparison to HC, patients showed significant reduced GMV in the bilateral Rolandic operculum (ROL), along with notable abnormal Wavelet-ALFF values in the right Precuneus (PCUN) and left Cerebellum_9 (CER9). Particularly, an abnormal dFC network seeded in the left ROL, demonstrating significantly differential between PSCI and HC groups and remaining consistent across all time windows, was observed. This abnormal dFC network comprised the left ROL as the seed region, the right ROL, bilateral PCUN, bilateral CER9, right Superior Temporal Gyrus (STG), and right Parahippocampal Gyrus (PHG). Notably, in patients, impaired functions across various cognitive domains significantly influenced the altered effective connections among the abnormal regions, particularly impacting the connections between structurally damaged regions and those with abnormal spontaneous activity. These findings suggest that altered effective connectivity networks within lesioned regions may contribute to deficits in various cognitive domains in PSCI.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11571-024-10209-7.

Labor epidural analgesia and autism spectrum disorder in 3-year-old offspring based on data from the Japan Environment and Children's Study: a prospective cohort study

OBJECTIVE

To evaluate the association between labor epidural analgesia (LEA) and autism spectrum disorder (ASD) in 3-year-old offspring in Japan.

METHODS

Prospective cohort study utilizing the Japan Environment and Children's Study, the largest nationwide birth cohort study. A total of 65,742 live singleton offspring were enrolled between January 2011 and March 2014. Offspring born via cesarean delivery or with confirmed chromosomal abnormalities were excluded. Multivariate logistic regression analyses were conducted to estimate adjusted odds ratios (aOR) and 95% confidence intervals (CI), accounting for maternal, paternal, and perinatal covariates. Subgroup analyses were performed based on the sex of the offspring. The primary outcome was the diagnosis of ASD at age 3.

RESULTS

Among the 65,742 offspring (33,684 boys [51.2%]; mean maternal age, 31.1 [4.9] years), 1,324 (2.0%) were exposed to LEA. ASD was diagnosed in 14 (1.1%) offspring exposed to LEA and 257 (0.4%) not exposed to LEA by age 3. After adjusting for potential confounders, multivariate logistic regression revealed that LEA was associated with an increased risk of ASD (aOR: 2.23; 95% CI: 1.28-3.87). Subgroup analysis indicated that the association was significant in male offspring (aOR: 2.55; 95% CI: 1.40-4.65), but not in female offspring (aOR: 1.41; 95% CI: 0.34-5.91).

CONCLUSION

This study suggests a mild association between LEA and ASD in 3-year-old male offspring. However, the findings should be cautiously interpreted given the limited number of ASD cases in this study. Causal relationships cannot be established since this was an observational study.

A stacking classifier for distinguishing stages of Alzheimer's disease from a subnetwork perspective

Accurately distinguishing stages of Alzheimer's disease (AD) is crucial for diagnosis and treatment. In this paper, we introduce a stacking classifier method that combines six single classifiers into a stacking classifier. Using brain network models and network metrics, we employ t-tests to identify abnormal brain regions, from which we construct a subnetwork and extract its features to form the training dataset. Our method is then applied to the ADNI (Alzheimer's Disease Neuroimaging Initiative) datasets, categorizing the stages into four categories: Alzheimer's disease, mild cognitive impairment (MCI), mixed Alzheimer's mild cognitive impairment (ADMCI), and healthy controls (HCs). We investigate four classification groups: AD-HCs, AD-MCI, HCs-ADMCI, and HCs-MCI. Finally, we compare the classification accuracy between a single classifier and our stacking classifier, demonstrating superior accuracy with our stacking classifier from a subnetwork-based viewpoint.

Aberrant regional neural fluctuations and functional connectivity in insomnia comorbid depression revealed by resting-state functional magnetic resonance imaging

Insomnia is a common mental illness seriously affecting people lives, that might progress to major depression. However, the neural mechanism of patients with CID comorbid MDD remain unclear. Combining fractional amplitude of low-frequency fluctuation (fALFF) and seed-based functional connectivity (FC), this study investigated abnormality in local and long-range neural activity of patients with CID comorbid MDD. Here, we acquired resting-state blood oxygenation level dependent (BOLD) data from 57 patients with CID comorbid MDD and 57 healthy controls (HC). Compared with the controls, patients with CID comorbid MDD exhibited abnormal functional activity in posterior cerebral cortex related to the visual cortex, including the middle occipital gyrus (MOG), the cuneus and the lingual gyrus, specifically, lower fALFF values in the right MOG, left cuneus, and right postcentral gyrus, increased FC between the right MOG and the left cerebellum, and decreased FC between the right MOG and the right lingual gyrus. Neuropsychological correlation analysis revealed that the decreased fALFF in the right MOG was negatively correlated with all the neuropsychological scores of insomnia and depression, reflecting common relationships with symptoms of CID and MDD. While the decreased fALFF of the left cuneus was distinctly correlated with the scores of depression related scales. The decreased FC between the right MOG and the right lingual gyrus was distinctly correlated with the scores of insomnia related scales. This study not only widened neuroimaging evidence that associated with insomnia and depressive symptoms of patients with CID comorbid MDD, but also provided new potential targets for clinical treatment.

The role of beta band phase resetting in audio-visual temporal order judgment

The integration of auditory and visual stimuli is essential for effective language processing and social perception. The present study aimed to elucidate the mechanisms underlying audio-visual (A-V) integration by investigating the temporal dynamics of multisensory regions in the human brain. Specifically, we evaluated inter-trial coherence (ITC), a neural index indicative of phase resetting, through scalp electroencephalography (EEG) while participants performed a temporal-order judgment task that involved auditory (beep, A) and visual (flash, V) stimuli. The results indicated that ITC phase resetting was greater for bimodal (A + V) stimuli compared to unimodal (A or V) stimuli in the posterior temporal region, which resembled the responses of A-V multisensory neurons reported in animal studies. Furthermore, the ITC got lager as the stimulus-onset asynchrony (SOA) between beep and flash approached 0 ms. This enhancement in ITC was most clearly seen in the beta band (13-30 Hz). Overall, these findings highlight the importance of beta rhythm activity in the posterior temporal cortex for the detection of synchronous audiovisual stimuli, as assessed through temporal order judgment tasks.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11571-024-10183-0.

Effects of transcutaneous auricular vagus nerve stimulation (taVNS) on motor planning: a multimodal signal study

Motor planning plays a pivotal role in daily life. Transcutaneous auricular vagus nerve stimulation (taVNS) has been demonstrated to enhance decision-making efficiency, illustrating its potential use in cognitive modulation. However, current research primarily focuses on behavioral and single-modal electrophysiological signal, such as electroencephalography (EEG) and electrocardiography (ECG). To investigate the effect of taVNS on motor planning, a total of 21 subjects were recruited for this study and were divided into two groups: active group (n = 10) and sham group (n = 11). Each subject was required to be involved in a single-blind, sham-controlled, between-subject end-state comfort (ESC) experiment. The study compared behavioral indicators and electrophysiological features before and following taVNS. The results indicated a notable reduction in reaction time and an appreciable increase in the proportion of end-state comfort among the participants following taVNS, accompanied by notable alterations in motor-related cortical potential (MRCP) amplitude, low-frequency power of HRV (LF), and cortico-cardiac coherence, particularly in the parietal and occipital regions. These findings show that taVNS may impact the brain and heart, potentially enhancing their interaction, and improve participants' ability of motor planning.

Bidirectional causality of physical exercise in retinal neuroprotection

Physical exercise is recognized as an effective intervention to improve mood, physical performance, and general well-being. It achieves these benefits through cellular and molecular mechanisms that promote the release of neuroprotective factors. Interestingly, reduced levels of physical exercise have been implicated in several central nervous system diseases, including ocular disorders. Emerging evidence has suggested that physical exercise levels are significantly lower in individuals with ocular diseases such as glaucoma, age-related macular degeneration, retinitis pigmentosa, and diabetic retinopathy. Physical exercise may have a neuroprotective effect on the retina. Therefore, the association between reduced physical exercise and ocular diseases may involve a bidirectional causal relationship whereby visual impairment leads to reduced physical exercise and decreased exercise exacerbates the development of ocular disease. In this review, we summarize the evidence linking physical exercise to eye disease and identify potential mediators of physical exercise-induced retinal neuroprotection. Finally, we discuss future directions for preclinical and clinical research in exercise and eye health.

Functional abnormalities of the glymphatic system in cognitive disorders

Various pathological mechanisms represent distinct therapeutic targets for cognitive disorders, but a balance between clearance and production is essential for maintaining the stability of the brain's internal environment. Thus, the glymphatic system may represent a common pathway by which to address cognitive disorders. Using the established model of the glymphatic system as our foundation, this review disentangles and analyzes the components of its clearance mechanism, including the initial inflow of cerebrospinal fluid, the mixing of cerebrospinal fluid with interstitial fluid, and the outflow of the mixed fluid and the clearance. Each section summarizes evidence from experimental animal models and human studies, highlighting the normal physiological properties of key structures alongside their pathological manifestations in cognitive disorders. The same pathologic manifestations of different cognitive disorders appearing in the glymphatic system and the same upstream influences are main points of interest of this review. We conclude this article by discussing new findings and outlining the limitations identified in current research progress.

Brain functional connectivity changes on fMRI in patients with chronic pelvic pain treated with the Neuro Emotional Technique: a randomised controlled trial

BACKGROUND

Chronic pelvic pain is a substantial clinical challenge that profoundly impacts quality of life for many women. The Neuro Emotional Technique (NET) is a novel mind-body intervention designed to attenuate emotional arousal of distressing thoughts and pain. This study evaluated functional connectivity changes in key areas of the brain in patients with chronic pelvic pain receiving the NET intervention. The goal was to assess whether the NET intervention was associated with functional connectivity (FC) changes in the brain related to reductions in emotional distress and pain, particularly in the limbic areas, sensory/pain regions, and cerebellum.

METHODS

This is a prospectively designed study that included twenty-six patients with a diagnosis of chronic pelvic pain who were randomised to either the NET intervention or a waitlist control. To evaluate the primary outcome of neurophysiological effects, all participants received resting state functional blood oxygen level dependent (BOLD) magnetic resonance imaging (rs-fMRI) before and after the NET intervention or waitlist control period. Pain, mood, anxiety, and quality of life also were assessed.

RESULTS

Compared to the control group, the NET group demonstrated significant improvements in pain interference and pain intensity, and in emotional measures such anxiety and depression. Functional connectivity in the NET group compared to controls, was significantly decreased in the amygdala, cerebellum, and postcentral gyrus. There were also significant correlations between FC changes and changes in clinical measures.

CONCLUSIONS

This study is an initial step towards describing a neurological signature of reducing emotional distress in women with chronic pelvic pain. Specifically, FC changes between the cerebellum and the amygdala and sensory areas appears to be associated with a reduction in pain and the effects of that pain. Future, larger clinical trials are warranted to further evaluate these mechanisms and NET as a potential therapeutic intervention in patients with chronic pelvic pain.

Hippocampal connectivity changes after traumatic memory reactivation with propranolol for posttraumatic stress disorder: a randomized fMRI study

Background: Reactivation of traumatic memory under the influence of propranolol has shown encouraging clinical results in the treatment of posttraumatic stress disorder (PTSD), but the neural correlates remain unknown. To identify these correlates, we examined the changes in brain functional connectivity specifically associated with the influence of propranolol and their correlation with improvement in PTSD symptoms.Objectives: To identify resting-state functional connectivity (rs-FC) changes specifically associated with propranolol after a traumatic memory reactivation procedure (TMRP) in PTSD patients.Method: Thirty patients (50% of women) with PTSD were enrolled in a randomized controlled study comprised of six sessions of a traumatic memory reactivation procedure (TMRP) under the influence of propranolol (n = 16), compared to the same reactivation under a placebo (n = 14). Patients were scanned twice by functional magnetic resonance before and after treatment. Resting state functional connectivity (rs-FC) was compared across groups and over time.Results: Post versus pretreatment comparisons found an increase in rs-FC between the right hippocampus and the left parahippocampal gyrus in the propranolol group, but not in the placebo group. Symptom improvement in both groups were associated with an increase in rs-FC between the parahippocampal gyrus and both the supramarginal gyrus and the amygdala.Conclusions: During TMRP treatment, propranolol appears to constrain functional connectivity changes in the explicit memory brain system. These findings require further replication and exploration but could distinguish the effect of TMRP on the brain from other forms of PTSD psychotherapy.

Optimizing non-invasive vagus nerve stimulation for treatment in stroke

Stroke remains a leading cause of long-term disability worldwide. There is an unmet need for neuromodulatory therapies that can mitigate against neurovascular injury and potentially promote neurological recovery. Transcutaneous vagus nerve stimulation has been demonstrated to show potential therapeutic effects in both acute and chronic stroke. However, previously published research has only investigated a narrow range of stimulation settings and indications. In this review, we detail the ongoing studies of transcutaneous vagus nerve stimulation in stroke through systematic searches of registered clinical trials. We summarize the upcoming clinical trials of transcutaneous vagus nerve stimulation in stroke, highlighting their indications, parameter settings, scope, and limitations. We further explore the challenges and barriers associated with the implementation of transcutaneous vagus nerve stimulation in acute stroke and stroke rehabilitation, focusing on critical aspects such as stimulation settings, target groups, biomarkers, and integration with rehabilitation interventions.

Analysis of brain network effective connectivity in juvenile myoclonic epilepsy

Juvenile Myoclonic Epilepsy (JME) is a prevalent idiopathic generalized epilepsy whose neurophysiological mechanisms remain elusive. This study aims to elucidate the aberrant brain network patterns in JME through a multi-modal fMRI approach combining local consistency, functional connectivity, and causal interaction analysis. Resting-state fMRI data were acquired from 37 JME patients and 35 healthy controls. Regional homogeneity (ReHo) and amplitude of low-frequency fluctuations (ALFF) analyses identified eight brain regions with significant between-group differences (FDR-corrected p < 0.05), including the right middle frontal gyrus, right insula, right medial/paracingulate gyrus, bilateral superior frontal gyri, left postcentral gyrus, and left superior occipital gyrus. These regions served as regions of interest (ROIs) for subsequent functional and effective connectivity analyses. Functional connectivity analysis revealed increased connectivity strength between the right middle frontal gyrus and right medial or paracingulate gyrus, as well as between the right insula and right medial/paracingulate gyrus (two-sample t test, p < 0.01), despite decreased local synchrony in these regions. Dynamic causal modeling (DCM) demonstrated bidirectional enhancement of effective connectivity between the right insula and right medial or paracingulate gyrus in patients (Bayesian posterior probability > 0.95). These findings suggest that the observed decoupling of local neuronal synchronization and long-range connectivity may reflect compensatory neuroadaptive processes, particularly involving the salience network (insula) and cognitive control circuitry (cingulate regions).The integration of ReHo/ALFF mapping with DCM provides a novel framework for understanding the neurodevelopmental trajectory of JME, highlighting the critical role of cortico-subcortical dysregulation in its pathogenesis.

The potential associations between acupuncture sensation and brain functional network: a EEG study

Acupuncture has been widely used as an effective treatment for post-stroke rehabilitation. However, the potential association between acupuncture sensation, an important factor influencing treatment efficacy, and brain functional network is unclear. This research sought to reveal and quantify the changes in brain functional network associated with acupuncture sensation. So multi-channel EEG signals were collected from 30 healthy participants and the Massachusetts General Hospital Acupuncture Sensation Scale (MASS) was utilized to assess their needling sensations. Phase Lag Index (PLI) was used to construct the brain functional network, which was analyzed with graph theoretic methods. It showed that in the needle insertion (NI) state the MASS Index was significantly higher than in the needle retention (NR) state (P < 0.001), and the mean values of PLI were also higher than in the Pre-Rest state and NR state significantly (P < 0.01). In the NI state global efficiency, local efficiency, nodal efficiency, and degree centrality were significantly higher than in the Pre-Rest state and the NR state (P < 0.05), while the opposite is true for the shortest path length (P < 0.01). Then Pearson correlation analysis showed a correlation between MASS Index and graph theory metrics (P < 0.05). Finally, Support Vector Regression (SVR) was used to predict the MASS Index with a minimum mean absolute error of 0.65. These findings suggest that the NI state of acupuncture treatment changes the structure of the brain functional network and affects the graph theory metrics of the brain functional network, which may be an objective biomarker for quantitative evaluation of acupuncture sensation.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11571-025-10233-1.

Neural dynamics of deception: insights from fMRI studies of brain states

Deception is a complex behavior that requires greater cognitive effort than truth-telling, with brain states dynamically adapting to external stimuli and cognitive demands. Investigating these brain states provides valuable insights into the brain's temporal and spatial dynamics. In this study, we designed an experiment paradigm to efficiently simulate lying and constructed a temporal network of brain states. We applied the Louvain community clustering algorithm to identify characteristic brain states associated with lie-telling, inverse-telling, and truth-telling. Our analysis revealed six representative brain states with unique spatial characteristics. Notably, two distinct states-termed truth-preferred and lie-preferred-exhibited significant differences in fractional occupancy and average dwelling time. The truth-preferred state showed higher occupancy and dwelling time during truth-telling, while the lie-preferred state demonstrated these characteristics during lie-telling. Using the average z-score BOLD signals of these two states, we applied generalized linear models with elastic net regularization, achieving a classification accuracy of 88.46%, with a sensitivity of 92.31% and a specificity of 84.62% in distinguishing deception from truth-telling. These findings revealed representative brain states for lie-telling, inverse-telling, and truth-telling, highlighting two states specifically associated with truthful and deceptive behaviors. The spatial characteristics and dynamic attributes of these brain states indicate their potential as biomarkers of cognitive engagement in deception.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11571-025-10222-4.

Joint disentangled representation and domain adversarial training for EEG-based cross-session biometric recognition in single-task protocols

The increasing adoption of wearable technologies highlights the potential of electroencephalogram (EEG) signals for biometric recognition. However, the intrinsic variability in cross-session EEG data presents substantial challenges in maintaining model stability and reliability. Moreover, the diversity within single-task protocols complicates achieving consistent and generalized model performance. To address these issues, we propose the Joint Disentangled Representation with Domain Adversarial Training (JDR-DAT) framework for EEG-based cross-session biometric recognition within single-task protocols. The JDR-DAT framework disentangles identity-specific features through mutual information estimation and incorporates domain adversarial training to enhance longitudinal robustness. Extensive experiments on longitudinal EEG data from two publicly available single-task protocol datasets-RSVP-based (Rapid Serial Visual Presentation) and MI-based (Motor Imagery)-demonstrate the efficacy of the JDR-DAT framework, with the proposed method achieving average accuracies of 85.83% and 96.72%, respectively.

Spatial and frequency domain-based feature fusion for accurate detection of schizophrenia using AI-driven approaches

Schizophrenia is a neuropsychiatric disorder that hampers brain functions and causes hallucinations, delusions, and bizarre behavior. The stigmatization associated with this disabling disorder drives the need to build diagnostic models with impeccable performances. Neuroimaging modality such as structural MRI is coupled with machine learning techniques to perform schizophrenia diagnosis with increased reliability. We investigate the structural aberrations present in the structural MR images using machine learning techniques. In this study, we propose a new hybrid approach using spatial and frequency domain-based features for the early automated detection of schizophrenia using machine learning techniques. The spatial or texture features are extracted using the local binary pattern method, and frequency-based features, including magnitude and phase, are extracted using the fast fourier transform feature extraction technique. Hybrid features, combining spatial and frequency-based features, are utilized for schizophrenia classification using support vector machine, random forest, and k-nearest neighbor with stratified 10-fold cross-validation. The support vector machine and random forest classifiers achieve encouraging detection performances on the hybrid feature set, with 86.5% and 85.1% accuracy, respectively. Among the three classifiers, k-nearest neighbor shows outstanding detection performance with an accuracy of 98.1%. The precision and recall achieved by the k-nearest neighbor classifier are 98.1% and 98.0% respectively, reflecting accurate detection of schizophrenia by the proposed model.

Unveiling structural damage of the corpus callosum in amyotrophic lateral sclerosis through diffusion tensor imaging and spread direction perspectives

OBJECTIVE

Damage to the corpus callosum (CC) in amyotrophic lateral sclerosis (ALS) patients has been confirmed via electrophysiological, neuroimaging, and autopsy studies. Additionally, the CC is hypothesized to serve as a pathway for the spread of pathological information. This study aimed to investigate whether the CC plays a mediating role in the symptomatic spread of ALS.

METHODS

In this observational study, diffusion tensor imaging (DTI) data were acquired from 45 individuals with the upper motor neuron-dominant (UMN-D) phenotype of ALS. The UMN-D ALS patients were categorized into two groups based on the direction of symptom spread, including 25 patients with horizontal spread (group H) and 20 patients with vertical spread (group V). Diffusivity indices were derived through whole-brain analysis and probabilistic fiber tracking.

RESULTS

According to the voxel-based analysis and tract-based spatial statistics, differences in axial diffusivity (AD) in the CC were observed between disease subgroups, with patients in group H showing higher AD values than those in group V. Fiber tracking analysis revealed persistent differences in the AD indices of CC-primary motor cortex (PMC) fibers between the two disease subgroups.

CONCLUSION

In UMN-D ALS patients, the direction of symptom spread may be related to the degree of CC involvement. The AD metric may be a more specific indicator of CC damage.

Visual image reconstructed without semantics from human brain activity using linear image decoders and nonlinear noise suppression

In recent years, substantial strides have been made in the field of visual image reconstruction, particularly in its capacity to generate high-quality visual representations from human brain activity while considering semantic information. This advancement not only enables the recreation of visual content but also provides valuable insights into the intricate processes occurring within high-order functional brain regions, contributing to a deeper understanding of brain function. However, considering fusion semantics in reconstructing visual images from brain activity involves semantic-to-image guide reconstruction and may ignore underlying neural computational mechanisms, which does not represent true reconstruction from brain activity. In response to this limitation, our study introduces a novel approach that combines linear mapping with nonlinear noise suppression to reconstruct visual images perceived by subjects based on their brain activity patterns. The primary challenge associated with linear mapping lies in its susceptibility to noise interference. To address this issue, we leverage a flexible denoised deep convolutional neural network, which can suppress noise from linear mapping. Our investigation encompasses linear mapping as well as the training of shallow and deep autoencoder denoised neural networks, including a pre-trained, state-of-the-art denoised neural network. The outcome of our study reveals that combining linear image decoding with nonlinear noise reduction significantly enhances the quality of reconstructed images from human brain activity. This suggests that our methodology holds promise for decoding intricate perceptual experiences directly from brain activity patterns without semantic information. Moreover, the model has strong neural explanatory power because it shares structural and functional similarities with the visual brain.

Mechanisms underlying EEG power changes during wakefulness in insomnia patients: a model-driven study

Insomnia, as a common sleep disorder, is the most common complaints in medical practice affecting a large proportion of the population on a situational, recurrent or chronic basis. It has been demonstrated that, during wakefulness, patients with insomnia exhibit increased EEG power in theta, beta, and gamma band. However, the relevant mechanisms underlying such power changes are still lack of understanding. In this paper, by combining the neural computational model with the real EEG data, we focus on exploring what's behind the EEG power changes for insomniac. We first develop a modified Liley model, named FSR-Liley, by respectively considering the fast and slow synaptic responses in inhibitory neurons along with the one-way projection between them. Then we introduce a parameter selection and evaluation method based on Markov chain Monte Carlo algorithm and Wasserstein distance, by which the sensitive parameters are selected automatically, and meanwhile, the optimal values of selected parameters are evaluated. Finally, through combining with EEG data, we determine the sensitive parameters in FSR-Liley and accordingly provide the mechanistic hypotheses: (1) decrease in P e i f , corresponding to the input from the thalamus to cortical inhibitory population with fast synaptic response, leads to the increased theta and beta power; (2) decrease in N e i f , corresponding to the projection from cortical excitatory population to inhibitory population with fast synaptic response, causes the increased gamma power. The results in this paper provide insights into the mechanisms of EEG power changes in insomnia and establish a theoretical foundation to support further experimental research.

Monitoring nap deprivation-induced fatigue using fNIRS and deep learning

Fatigue-induced incidents in transportation, aerospace, military, and other areas have been on the rise, posing a threat to human life and safety. The determination of fatigue states holds significant importance, especially through reliable and conveniently available physiological indicators. Here, a portable custom-built fNIRS system was used to monitor the fatigue state caused by nap deprivation. fNIRS signals in ten channels at the prefrontal cortex were collected, changes in blood oxygen concentration were analyzed, followed by a deep learning model to classify fatigue states. For the high-dimensionality and multi-channel characteristics of the fNIRS signal data, a novel 1D revised CNN-ResNet network was proposed based on the double-layer channel attenuation residual block. The results showed a 97.78% accuracy in fatigue state classification, significantly superior than several conventional methods. Furthermore, a fatigue-arousal experiment was designed to explore the feasibility of forced arousal of fatigued subjects through exercise stimulation. The fNIRS results showed a significant increase in brain activity with the conduction of exercise. The proposed method serves as a reliable tool for the evaluation of fatigue states, potentially reducing fatigue-induced harms and risks.

Neural oscillations predict flow experience

Flow experience, characterized by immersion in the activity at hand, provides a motivational boost and promotes positive behaviors. However, the oscillatory representations of flow experience are still poorly understood. In this study, the difficulty of the video game was adjusted to manipulate the individual's personalized flow or non-flow state, and EEG data was recorded throughout. Our results show that, compared to non-flow tasks, flow tasks exhibit higher theta power, moderate alpha power, and lower beta power, providing evidence for a focused yet effortless brain pattern during flow. Additionally, we employed Lasso regression to predict individual subjective flow scores based on neural data, achieving a correlation coefficient of 0.571 (p < 0.01) between the EEG-predicted scores and the actual self-reported scores. Our findings offer new insights into the oscillatory representation of flow and emphasize that flow, as a measure of individual experience quality, can be objectively and quantitatively predicted through neural oscillations.

Reciprocal causation relationship between rumination thinking and sleep quality: a resting-state fMRI study

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.

Unraveling the functional complexity of the locus coeruleus-norepinephrine system: insights from molecular anatomy to neurodynamic modeling

The locus coeruleus (LC), as the primary source of norepinephrine (NE) in the brain, is central to modulating cognitive and behavioral processes. This review synthesizes recent findings to provide a comprehensive understanding of the LC-NE system, highlighting its molecular diversity, neurophysiological properties, and role in various brain functions. We discuss the heterogeneity of LC neurons, their differential responses to sensory stimuli, and the impact of NE on cognitive processes such as attention and memory. Furthermore, we explore the system's involvement in stress responses and pain modulation, as well as its developmental changes and susceptibility to stressors. By integrating molecular, electrophysiological, and theoretical modeling approaches, we shed light on the LC-NE system's complex role in the brain's adaptability and its potential relevance to neurological and psychiatric disorders.

Event-related potentials associated with cognitive control in adolescents exposed to complex childhood trauma

ABSTRACTBackground: Complex childhood trauma (CCT), characterized by repeated and prolonged exposure to adverse experiences, disrupts cognitive, emotional, and neural development. Adolescence, a critical developmental period, is particularly vulnerable to these effects, with CCT increasing the risk of psychopathologies, including post-traumatic stress disorder (PTSD). Despite this, the neurophysiological underpinnings of trauma-related deficits in cognitive control remain insufficiently explored, particularly in the developing brains of children and adolescents. This study aimed to investigate the neurophysiological markers of cognitive control in adolescents with CCT using event-related potential (ERP) components to propose an electrophysiological phenotype associated with CCT, as a vulnerability for PTSD.Methods: Twenty adolescents with CCT and 40 age- and gender-matched healthy controls performed a cued GO/NOGO task. ERP components - contingent negative variation (CNV), NoGo-N2, and NoGo-P3 - were analysed alongside behavioural measures such as omission and commission errors and reaction time, using a preregistered protocol. Statistical analysis included Mann-Whitney tests and cluster-based permutation tests for ERP comparisons.Results: Adolescents with CCT showed significant impairments in both proactive (reduced CNV amplitudes) and reactive (diminished NoGo-N2 and NoGo-P3 amplitudes) control mechanisms. Behaviourally, the CCT group exhibited higher omission errors and shorter reaction times than controls. Exploratory analysis revealed reduced amplitudes in the visual negativity (VN) component, suggesting disruptions in predictive processing. Latent component analysis identified ERP markers with potential diagnostic utility, linking deficits to key neural circuits associated with cognitive control and predictive processing.Conclusion: Study findings highlight significant impairments in cognitive control mechanisms and disrupted predictive processing in adolescents with CCT, emphasizing the importance of addressing trauma-related neural deficits during adolescence. Given that CCT is a significant risk factor for PTSD, the study provides insights into shared neurobiological pathways, supporting the development of targeted interventions. ERP markers like CNV, NoGo-N2, NoGo-P3, and VN show promise for improving diagnostic precision and monitoring therapeutic outcomes in trauma-exposed youth.

The mechanism of a 16-Week Baduanjin intervention in altering neural inhibition responses to food cues in healthy adults - an ERP study

The positive impact of exercise on inhibitory control has been validated in numerous studies; however, few studies have explored the effect of long-term exercise on food-related inhibitory control. The aim of the present study was to explore the effects of long-term exercise on the inhibitory response to food cues and the underlying neural mechanism. We recruited 51 healthy participants who were pseudo randomly divided into an exercise group and a non-exercise group, body mass index (BMI), age and sex. The exercise group underwent 16 weeks of Baduanjin intervention at a frequency of 3 days/week for 60 min/session. The assessment consisted of a personal information questionnaire, a hunger questionnaire and Go/NoGo tasks. The electroencephalography (EEG) data were recorded as the participants performed the Go/NoGo task. Only EEG data for the NoGo stimuli were analysed. The participants were asked to complete the entire procedure at baseline and within 1 week after the end of the exercise. There were 26 participants in the exercise group (age: 18.90 ± 0.49; number of females (%): 23 (88.46%); BMI: 21.79 ± 4.40) and 25 participants in the control group (age: 19.19 ± 0.63; number of females (%):20 (80.00%); BMI: 21.26 ± 3.36).A significant interaction effect of group and time on NoGo accuracy and N2/P2/P3 amplitudes was observed. Specifically, 16 weeks of Baduanjin exercise significantly increased NoGo accuracy, decreased the N2 amplitude and increased the P2/P3 amplitudes for food-related NoGo stimuli. We speculated that exercise may improve inhibitory control by reasonably regulating the allocation of attentional resources and improving the strategic orientation of attention.

Divergence of gut bacteria through the selection of genomic variants implicated in the metabolism of sugars, amino acids, and purines by small extracellular vesicles in milk

Here, we report that small extracellular vesicles (sEVs) in milk mediate the communication between bacteria and animal kingdoms, increase the divergence of bacteria in the intestine, and alter metabolite production by bacteria. We show that bovine milk sEVs select approximately 55,000 genomic variants in 19 species of bacteria from the murine cecum ex vivo. The genomic variants are transcribed into mRNA. The selection of genomic variants by milk sEVs alters bacterial metabolism, leading to an up to 12-fold difference in the abundance of more than 1000 metabolites in bacteria cultured in milk sEV-free media compared to sEV-containing media. Evidence is particularly strong that selection of genomic variants by milk sEV changes the metabolism of sugars, amino acids, and purines which might contribute to the development of spatial learning and memory deficiencies and seizure phenotypes reported for milk sEV-depleted infants and mice. Human milk is a rich source of sEVs, whereas formula contains only trace amounts of milk sEVs. This report implicates nutritional sEVs in altered microbial metabolism beyond the mere selection of bacterial communities.

Value of multi-modal MRI in predicting the effect of high-intensity focused ultrasound for uterine fibroids

OBJECTIVE

To explore the value of advanced MRI techniques in predicting the effect of high-intensity focused ultrasound (HIFU) on uterine fibroids and to establish a prediction model.

METHODS

This study enrolled 40 patients with 52 uterine fibroids who underwent HIFU at our hospital. All patients were scanned using multiple MRI sequences before HIFU therapy, including non-contrast enhanced MRI, contrast-enhanced MRI, diffusion weighted imaging (DWI), arterial spin labeling (ASL), and T1 mapping. MRI sequences that could predict the HIFU effect were identified, and the predictive performance was evaluated using ROC curves. Univariate and multivariate logistic analyses were employed to investigate independent predictors and establish a prediction model. In addition, we assessed and verified the performance of the model.

RESULTS

Hyperintense on T2 weighted imaging (T2WI), large apparent diffusion coefficient (ADC) values derived from DWI, and high perfusion index derived from ASL were associated with a poor HIFU effect. Univariate and multivariate logistic analyses suggested that uterine position, ADC value and perfusion index were independent predictors for establishing the prediction model. The AUC of the prediction model was 0.939. Both the Hosmer-Lemeshow test and the calibration curve indicated good calibration. The decision curve analysis (DCA) curve showed good clinical benefits, and the leave-one-out cross-validation (LOOCV) revealed that the model had good predictive performance and generalization ability.

CONCLUSION

ADC values and perfusion index are predictors of the effect of HIFU on uterine fibroids. The prediction model including uterine position, ADC value, and perfusion index as predictors has good predictive performance.

The length and the width of the human brain circuit connections are strongly correlated

The correlations of several fundamental properties of human brain connections are investigated in a consensus connectome, constructed from 1064 braingraphs, each on 1015 vertices, corresponding to 1015 anatomical brain areas. The properties examined include the edge length, the fiber count, or edge width, meaning the number of discovered axon bundles forming the edge and the occurrence number of the edge, meaning the number of individual braingraphs where the edge exists. By using our previously published robust braingraphs at https://braingraph.org, we have prepared a single consensus graph from the data and compared the statistical similarity of the edge occurrence numbers, edge lengths, and fiber counts of the edges. We have found a strong positive Spearman correlation between the edge occurrence numbers and the fiber count numbers, showing that statistically, the most frequent cerebral connections have the largest widths, i.e., the fiber count. We have found a negative Spearman correlation between the fiber lengths and fiber counts, showing that, typically, the shortest edges are the widest or strongest by their fiber counts. We have also found a negative Spearman correlation between the occurrence numbers and the edge lengths: it shows that typically, the long edges are infrequent, and the frequent edges are short.

Noninvasive grading of renal interstitial fibrosis and prediction of annual renal function loss in chronic kidney disease: the optimal solution of seven MR diffusion models

OBJECTIVES

To explore the optimal choice of seven diffusion models (DWI, IVIM, DKI, CTRW, FROC, SEM, and sADC) to assess renal interstitial fibrosis (IF) and annual renal function loss in chronic kidney disease (CKD).

METHODS

One hundred thirty-three CKD patients and 30 controls underwent multi-b diffusion sequence scans. Patients were divided into the training, testing, and temporal external validation sets. Least absolute shrinkage and selection operator regression and logistic regression were used to select the optimal metrics for distinguishing the mild from moderate-to-severe IF. The performances of imaging, clinical, and combined models were compared. A linear mixed-effects model calculated estimated glomerular filtration rate (eGFR) slope, and multiple linear regression assessed the association between metrics and 1-3-year eGFR slopes.

RESULTS

The training, testing, and temporal external validation sets had 75, 30, and 28 patients, respectively. The combined model incorporating cortical fIVIM, MKDKI and eGFR was superior to the clinical model combining the eGFR and 24-hour urinary protein in all sets (net reclassification index [NRI] > 0, p < 0.05). Decision curve analysis showed the combined model provided greater net clinical benefit across most thresholds. Fifty-two, 35, and 16 patients completed 1-, 2-, and 3-year follow-ups. After adjusting for covariates, cortical fIVIM correlated with the 1-year eGFR slope (β = 30.600, p = 0.001), and cortical αSEM correlated with the 2- and 3-year eGFR slopes (β = 44.859, p = 0.002; β = 95.631, p = 0.019).

CONCLUSIONS

A combined model of cortical fIVIM, MKDKI and eGFR provides a useful comprehensive tool for grading IF, with cortical fIVIM and αSEM as potential biomarkers for CKD progression.

The effects of stress on working-memory-related prefrontal processing: an fNIRS study

Acute stress causes a shift from executive to automated behavior. A key executive function suffering from this shift is working memory. Working memory is mainly negatively affected in the first 10 and more than 25 minutes after acute stress. These phases coincide with increased central levels of noradrenaline and cortisol. Increased levels of both hormones can cause a relative deactivation in prefrontal areas related to working memory processing. However, so far, there is little research that investigates the complete relationship between acute stress and resulting changes in stress hormones, neural activation, and working memory processing, over time. In this study, we used functional near-infrared spectroscopy to measure prefrontal activity during an nback task in a stress (28 subjects, 7 female/21 male) and a control group (28 subjects, 10 female/18 male) once (20 minutes) before and twice (4 and 24 minutes) after a socially evaluated cold pressor test or a warm water control condition. Additionally, we regularly measured changes in salivary cortisol and α-amylase (a correlate of central noradrenaline) during the experiment. While salivary cortisol was increased starting 14 minutes after acute stress, no effect of stress on salivary α-amylase or working memory performance was found. On a neural level, we found a marginally stronger decline in 3-back-related prefrontal activity from the first to the third measurement point in the stress than in the control group. These results present tentative evidence for a negative effect of acute stress on working-memory-related prefrontal processing mediated by central cortisol levels.

Generation, interrogation, and future applications of microglia-containing brain organoids

Brain organoids encompass a large collection of in vitro stem cell-derived 3D culture systems that aim to recapitulate multiple aspects of in vivo brain development and function. First, this review provides a brief introduction to the current state-of-the-art for neuro-ectoderm brain organoid development, emphasizing their biggest advantages in comparison with classical two-dimensional cell cultures and animal models. However, despite their usefulness for developmental studies, a major limitation for most brain organoid models is the absence of contributing cell types from endodermal and mesodermal origin. As such, current research is highly investing towards the incorporation of a functional vasculature and the microglial immune component. In this review, we will specifically focus on the development of immune-competent brain organoids. By summarizing the different approaches applied to incorporate microglia, it is highlighted that immune-competent brain organoids are not only important for studying neuronal network formation, but also offer a clear future as a new tool to study inflammatory responses in vitro in 3D in a brain-like environment. Therefore, our main focus here is to provide a comprehensive overview of assays to measure microglial phenotype and function within brain organoids, with an outlook on how these findings could better understand neuronal network development or restoration, as well as the influence of physical stress on microglia-containing brain organoids. Finally, we would like to stress that even though the development of immune-competent brain organoids has largely evolved over the past decade, their full potential as a pre-clinical tool to study novel therapeutic approaches to halt or reduce inflammation-mediated neurodegeneration still needs to be explored and validated.

Role of iron in brain development, aging, and neurodegenerative diseases

It is now understood that iron crosses the blood-brain barrier via a complex metabolic regulatory network and participates in diverse critical biological processes within the central nervous system, including oxygen transport, energy metabolism, and the synthesis and catabolism of myelin and neurotransmitters. During brain development, iron is distributed throughout the brain, playing a pivotal role in key processes such as neuronal development, myelination, and neurotransmitter synthesis. In physiological aging, iron can selectively accumulate in specific brain regions, impacting cognitive function and leading to intracellular redox imbalance, mitochondrial dysfunction, and lipid peroxidation, thereby accelerating aging and associated pathologies. Furthermore, brain iron accumulation may be a primary contributor to neurodegenerative diseases such as Alzheimer's and Parkinson's diseases. Comprehending the role of iron in brain development, aging, and neurodegenerative diseases, utilizing iron-sensitive Magnetic Resonance Imaging (MRI) technology for timely detection or prediction of abnormal neurological states, and implementing appropriate interventions may be instrumental in preserving normal central nervous system function.

Neuroenhancement by repetitive transcranial magnetic stimulation (rTMS) on DLPFC in healthy adults

The term "neuroenhancement" describes the enhancement of cognitive function associated with deficiencies resulting from a specific condition. Nevertheless, there is currently no agreed-upon definition for the term "neuroenhancement", and its meaning can change based on the specific research being discussed. As humans, our continual pursuit of expanding our capabilities, encompassing both cognitive and motor skills, has led us to explore various tools. Among these, repetitive Transcranial Magnetic Stimulation (rTMS) stands out, yet its potential remains underestimated. Historically, rTMS was predominantly employed in studies focused on rehabilitation objectives. A small amount of research has examined its use on healthy subjects with the goal of improving cognitive abilities like risk-seeking, working memory, attention, cognitive control, learning, computing speed, and decision-making. It appears that the insights gained in this domain largely stem from indirect outcomes of rehabilitation research. This review aims to scrutinize these studies, assessing the effectiveness of rTMS in enhancing cognitive skills in healthy subjects. Given that the dorsolateral prefrontal cortex (DLPFC) has become a popular focus for rTMS in treating psychiatric disorders, corresponding anatomically to Brodmann areas 9 and 46, and considering the documented success of rTMS stimulation on the DLPFC for cognitive improvement, our focus in this review article centers on the DLPFC as the focal point and region of interest. Additionally, recognizing the significance of theta burst magnetic stimulation protocols (TBS) in mimicking the natural firing patterns of the brain to modulate excitability in specific cortical areas with precision, we have incorporated Theta Burst Stimulation (TBS) wave patterns. This inclusion, mirroring brain patterns, is intended to enhance the efficacy of the rTMS method. To ascertain if brain magnetic stimulation consistently improves cognition, a thorough meta-analysis of the existing literature has been conducted. The findings indicate that, after excluding outlier studies, rTMS may improve cognition when compared to appropriate control circumstances. However, there is also a considerable degree of variation among the researches. The navigation strategy used to reach the stimulation site and the stimulation location are important factors that contribute to the variation between studies. The results of this study can provide professional athletes, firefighters, bodyguards, and therapists-among others in high-risk professions-with insightful information that can help them perform better on the job.

Reconstruction and application of multilayer brain network for juvenile myoclonic epilepsy based on link prediction

Juvenile myoclonic epilepsy (JME) exhibits abnormal functional connectivity of brain networks at multiple frequencies. We used the multilayer network model to address the heterogeneous features at different frequencies and assess the mechanisms of functional integration and segregation of brain networks in JME patients. To address the possibility of false edges or missing edges during network construction, we combined multilayer networks with link prediction techniques. Resting-state functional magnetic resonance imaging (rs-fMRI) data were procured from 40 JME patients and 40 healthy controls. The Multilayer Network framework is utilized to integrate information from different frequency bands and to fuse similarity metrics for link prediction. Finally, calculate the entropy of the multiplex degree and multilayer clustering coefficient of the reconfigured multilayer frequency network. The results showed that the multilayer brain network of JME patients had significantly reduced ability to integrate and separate information and significantly correlated with severity of JME symptoms. This difference was particularly evident in default mode network (DMN), motor and somatosensory network (SMN), and auditory network (AN). In addition, significant differences were found in the precuneus, suboccipital gyrus, middle temporal gyrus, thalamus, and insula. Results suggest that JME patients have abnormal brain function and reduced cross-frequency interactions. This may be due to changes in the distribution of connections within and between the DMN, SMN, and AN in multiple frequency bands, resulting in unstable connectivity patterns. The generation of these changes is related to the pathological mechanisms of JME and may exacerbate cognitive and behavioral problems in patients.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11571-024-10191-0.

Classification for Alzheimer's disease and frontotemporal dementia via resting-state electroencephalography-based coherence and convolutional neural network

The study aimed to diagnose of Alzheimer's Disease (AD) and Frontotemporal Dementia (FTD) based on brain functional connectivity features extracted via resting-state Electroencephalographic (EEG) signals, and subsequently developed a convolutional neural network (CNN) model, Coherence-CNN, for classification. First, a publicly available dataset of EEG resting state-closed eye recordings containing 36 AD subjects, 23 FTD subjects, and 29 cognitively normal (CN) subjects was used. Then, coherence metrics were utilized to quantify brain functional connectivity, and the differences in coherence between groups across various frequency bands were investigated. Next, spectral clustering was used to analyze variations and differences in brain functional connectivity related to disease states, revealing distinct connectivity patterns in brain electrode position maps. The results demonstrated that brain functional connectivity between different regions was more robust in the CN group, while the AD and FTD groups exhibited various degrees of connectivity decline, reflecting the pronounced differences in connectivity patterns associated with each condition. Furthermore, Coherence-CNN was developed based on CNN and the feature of coherence for three-class classification, achieving a commendable accuracy of 94.32% through leave-one-out cross-validation. This study revealed that Coherence-CNN demonstrated significant performance for distinguishing AD, FTD, and CN groups, supporting the disorder of brain functional connectivity in AD and FTD.

EEG microstate biomarkers for schizophrenia: a novel approach using deep neural networks

Schizophrenia remains a challenging neuropsychiatric disorder with complex diagnostic processes. Current clinical approaches often rely on subjective assessments, highlighting the critical need for objective, quantitative diagnostic methods. This study aimed to develop a robust classification approach for schizophrenia using EEG microstate analysis and advanced machine learning techniques. We analyzed EEG signals from 14 healthy individuals and 14 patients with schizophrenia during a 15-min resting-state session across 19 EEG channels. A data augmentation strategy expanded the dataset to 56 subjects in each group. The signals were preprocessed and segmented into five frequency bands (delta, theta, alpha, beta, gamma) and five microstates (A, B, C, D, E) using k-means clustering. Five key features were extracted from each microstate: duration, occurrence, standard deviation, coverage, and frequency. A Deep Neural Network (DNN) model, along with other machine learning classifiers, was developed to classify the data. A comprehensive fivefold cross-validation approach evaluated model performance across various EEG channels, frequency bands, and feature combinations. Significant alterations in microstate transition probabilities were observed, particularly in higher frequency bands. The gamma band showed the most pronounced differences, with a notable disruption in D → A transitions (absolute difference = 0.100). The Random Forest classifier achieved the highest accuracy of 99.94% ± 0.12%, utilizing theta band features from the F8 frontal channel. The deep neural network model demonstrated robust performance with 98.31% ± 0.68% accuracy, primarily in the occipital region. Feature size 2 consistently provided optimal classification across most models. Our study introduces a novel, high-precision EEG microstate analysis approach for schizophrenia diagnosis, offering an objective diagnostic tool with potential applications in neuropsychiatric disorders. The findings reveal critical insights into neural dynamics associated with schizophrenia, demonstrating the potential for transforming clinical diagnostic practices through advanced machine learning and neurophysiological feature extraction.