Participants will be adults aged 30 to 80 who have experienced their first stroke, are between one and six months after onset, have Chinese as their native language and can cooperate with treatment and assessment. Stroke must be confirmed by computed tomography or magnetic resonance imaging and meet Chinese diagnostic criteria for cerebral infarction or hemorrhage. Cognitive impairment will be established under a 2021 expert consensus and confirmed with the Montreal Cognitive Assessment and Mini-Mental State Examination, with thresholds adjusted for education. The MoCA threshold is below 26 points, with one additional point for people with 12 years or fewer of education, while MMSE cutoffs vary by educational level. People with unstable vital signs, severe aphasia, significant visual or hearing impairment, serious carotid disease, implanted devices, certain bleeding or neurological disorders, or pre-existing neurodegenerative cognitive disease will be excluded.

All three groups will follow the same treatment schedule: interventions once daily, five days per week, for four weeks. The synchronous group will receive scalp acupuncture while taVNS is applied during the needle-retention period. Control Group 1 will receive scalp acupuncture first and taVNS afterward, while Control Group 2 will receive taVNS before scalp acupuncture. The two sequential groups will have at least three hours between treatments. Conventional care will include management of blood pressure, blood glucose, lipids and atherosclerotic plaques, as well as therapeutic exercise, occupational therapy, physical-agent modalities and 30-minute sessions of individualized cognitive training. Because all three groups receive both active treatments, the design can compare the temporal sequence of delivery but cannot determine whether the combination is better than either intervention alone.

For taVNS, a single electrode will be placed in the left auricular concha, an area supplied by the auricular branch of the vagus nerve. Stimulation will be continuous at 25 Hz, with a pulse width of 200 microseconds and a current amplitude of 1.0 to 5.0 milliamperes adjusted daily to just below the participant’s pain threshold. Each session will last 30 minutes. Scalp acupuncture will target Baihui, Shenting and Sishencong. Disposable needles measuring 0.35 by 40 millimeters will be inserted obliquely at 15 to 30 degrees to a depth of 20 to 25 millimeters, reaching the subgaleal space. The needles will remain in place for 30 minutes, with two minutes of twirling every 10 minutes at 150 twists per minute. The acupuncture sensation known as de qi, described in the protocol as soreness, numbness, distension or heaviness, will be sought and recorded. A dedicated team of certified acupuncturists and rehabilitation therapists will provide the treatments, and an independent monitor will randomly check about 10% of sessions for adherence.

The primary outcome will be the change in MoCA score between baseline and the end of treatment at week 4. The MoCA covers visuospatial skills, executive function, memory, language, attention and concentration, calculation, abstract thinking and orientation, with a maximum score of 30. To reduce practice effects from repeated testing, alternate validated equivalent Chinese versions will be used at the assessment time points. Secondary outcomes include the MMSE for global cognitive status and the Modified Barthel Index for activities of daily living. The Barthel Index measures bowel and bladder control, dressing, toileting, feeding, bathing, grooming, transfers, walking and stair climbing; higher scores indicate greater independence. Assessments will occur before treatment, at the end of the four-week intervention period and at an eight-week follow-up, allowing the protocol to capture both short-term change and a short post-treatment observation period.

Alongside behavioral scales, the study will collect resting-state functional magnetic resonance imaging, auditory event-related potentials and transcranial Doppler ultrasonography. Resting-state fMRI will examine functional connectivity, amplitude of low-frequency fluctuation and regional homogeneity, providing exploratory measures of brain-network activity. Event-related potentials will record the P300 component during a target-tone counting task, with amplitude and latency extracted from a predefined 250-to-500-millisecond window. The protocol will require at least 30 valid target trials after artifact rejection and will maintain electrode impedance below 5 kilohms. Transcranial Doppler ultrasound will measure systolic, end-diastolic and mean flow velocities in the bilateral middle cerebral arteries. The researchers describe these imaging, electrophysiological and hemodynamic measures as exploratory mechanistic endpoints, whereas the trial is powered specifically for the clinical MoCA comparison.

The statistical plan will use mixed-effects models for repeated measurements as the primary confirmatory analysis, following the intention-to-treat principle. Models will include group, time and their interaction as fixed effects, with baseline MoCA score as a covariate. The principal contrasts are adjusted differences at week 4 between the synchronous group and each sequential group, with Holm correction for the two comparisons. The likelihood-based model will handle incomplete data under a missing-at-random assumption, while multiple imputation by chained equations will be used as a sensitivity analysis. Baseline neurological severity and affected hemisphere will also be examined in additional sensitivity analyses. The sample-size calculation was based on an expected MoCA change of 4.9 points in the synchronous group and 1.5 points in each sequential group, a difference of 3.4 points; the trial is planned for 32 participants per group to retain at least 29 completers per group after an assumed 10% attrition rate. These figures are hypotheses for powering the study, not observed treatment effects.

Multimodal machine learning will be used only for hypothesis generation. After normalizing the MoCA and MMSE scores, investigators plan to build an initial pool of roughly 250 candidate features derived principally from regional fMRI measures, P300 parameters and cerebral blood-flow velocities. Support vector machine and random forest models will use the magnitude of cognitive improvement as the prediction target. To reduce the risk of overfitting in a cohort of 96, the complete analytical pipeline will be nested within five-fold cross-validation. Missing-value models will be fitted only on training folds, principal component analysis and recursive feature elimination will remain inside those folds, and hyperparameter tuning will occur exclusively in inner loops. Performance will be evaluated in held-out outer folds using root mean square error and mean absolute error. The authors explicitly state that the machine-learning component is not intended to produce a deployable or fully generalizable clinical prediction system.

Several design limitations could constrain the conclusions. The trial is open-label, and the distinctive nature of acupuncture and electrical stimulation makes participant and practitioner blinding impossible, leaving potential expectation effects despite blinded outcome assessors, imaging staff and statisticians. Recruitment from one hospital and a relatively homogeneous Chinese-speaking population may limit generalizability. The absence of a sham or single-intervention group prevents the trial from separating the effects of combination, the physiological action of each treatment and natural recovery after stroke. Four weeks of treatment also cannot establish long-term benefit, and the multimodal model will need validation in larger, multicenter cohorts. Safety will nevertheless be monitored closely: potential acupuncture events include syncope, hematoma, a stuck or broken needle and subcutaneous bleeding, while taVNS may cause skin irritation, headache or bradycardia. Serious events will be reported immediately to the ethics committee, and affected participants will be withdrawn. The study was approved by the hospital’s ethics committee and prospectively registered with the Chinese Clinical Trial Registry, but its clinical and mechanistic benefits remain untested until the planned outcomes are reported.

Subject of Research: A randomized trial protocol comparing concurrent and sequential neuromodulation timing for post-stroke cognitive impairment.

Article Title: Effects of synchronous versus sequential scalp acupuncture and transcutaneous auricular vagus nerve stimulation on post-stroke cognitive impairment based on multimodal assessments: a single-center randomized controlled trial protocol

Article References: Wei, J., Liu, X., Pan, Z., Wei, C., Wang, R., Wang, X., & Ma, S. (2026). Effects of synchronous versus sequential scalp acupuncture and transcutaneous auricular vagus nerve stimulation on post-stroke cognitive impairment based on multimodal assessments: a single-center randomized controlled trial protocol. Frontiers in Neurology, 17, Article 1931213. https://doi.org/10.3389/fneur.2026.1931213

Image Credits: AI Generated

DOI: 10.3389/fneur.2026.1931213

Keywords: post-stroke cognitive impairment, scalp acupuncture, auricular vagus nerve stimulation, neuromodulation, randomized controlled trial, resting-state fMRI, event-related potentials, cerebral blood flow, multimodal data, Effects, synchronous, versus

The stakes are far higher than cognitive inconvenience alone. A recent meta-analysis of 27 studies encompassing 39,412 patients found that cognitive impairment after stroke was associated with a 59 percent increased risk of recurrent stroke and a twofold increase in mortality. Left untreated, roughly one-third of patients with PSCI progress to severe dementia or disability. The clinical picture spans a spectrum from mild deficits in attention, memory, visuospatial ability, language, and executive function to full post-stroke dementia, often accompanied by psychiatric and behavioral disturbances that erode a patient’s capacity to work, socialize, and live independently. Because these deficits typically persist beyond the acute phase and are formally reassessed at three to six months, with persistence at six months serving as a key diagnostic benchmark, the window for early intervention is narrow and precious.

Current clinical evaluation relies heavily on neuropsychological screening, most commonly the Mini-Mental State Examination (MMSE) and the Montreal Cognitive Assessment (MoCA). The 2023 American Heart Association scientific statement on PSCI clearly recommends simple cognitive screening for all stroke patients, with an initial assessment during the acute phase or before discharge and a follow-up at three to six months. The MMSE, while simple to administer, is limited by sensitivity to age and education effects, whereas the MoCA demonstrates superior reliability, validity, and sensitivity. Complementing these direct assessments, informant-based tools such as the Informant Questionnaire on Cognitive Decline in the Elderly (IQCODE) prove invaluable when patients cannot be reliably tested due to severe motor deficits, aphasia, or reduced consciousness. At the three- to six-month follow-up, the IQCODE achieves a sensitivity of 81 percent and a specificity of 83 percent, comparable to the MoCA, and because it is less influenced by educational level or language barriers, combining the two instruments yields a more accurate and culturally robust identification of PSCI.

Beyond behavioral testing, blood and cerebrospinal fluid biomarkers are emerging as early predictors. Brain-derived neurotrophic factor, protein S100β, soluble intercellular adhesion molecule-1, oxidized low-density lipoprotein, and soluble CD40 ligand all show significant value for early prediction in patients not receiving thrombolytic therapy. Inflammatory markers, including C-reactive protein, erythrocyte sedimentation rate, interleukins, and tumor necrosis factor-α, add further predictive power and hint that anti-inflammatory interventions might one day protect cognition. Genetics compounds the risk: the apolipoprotein E4 (APOE4) allele, long known as a strong risk factor for Alzheimer’s disease, has been identified as an independent risk factor for PSCI in acute ischemic stroke patients. APOE4 accelerates amyloid-β accumulation, promotes tau aggregation, drives neuroinflammation, and alters regional glucose metabolism as measured by FDG-PET, while carriers also exhibit elevated plasma levels of TNFα and interleukin-6, linking vascular and neurodegenerative pathology in a single molecular thread.

Yet it is neuroimaging that has undergone the most dramatic transformation, evolving from simple anatomical localization to a multimodal assessment system that integrates macrostructural, microstructural connectivity, and molecular metabolic information. Where a stroke infarct lands matters enormously. A pooled analysis of individual patient data from 12 acute ischemic stroke cohorts found that infarcts in the left frontotemporal lobe, left thalamus, and right parietal lobe are strongly associated with PSCI, whereas infarcts in the right occipital lobe, brainstem, and cerebellum carry lower risk. Language and verbal memory impairment lateralize predominantly to the left hemisphere, while visuospatial deficits track right parietal damage, consistent with the brain’s asymmetric organization of cognitive functions. Infarct volume adds a second dimension: multivariate analysis indicates that volumes of 0.054 milliliters or greater constitute an independent risk factor, and larger lesions correlate with increased likelihood of involving strategically important regions, meaning location and volume synergistically shape cognitive outcomes.

Cerebral atrophy provides another powerful signal. Medial temporal lobe atrophy, in particular, has attracted intense research attention because prolonged hypoperfusion may drive hippocampal shrinkage and the deposition of tau protein and amyloid precursor protein. A systematic analysis concluded that overall atrophy and medial temporal lobe atrophy are the most consistent predictors of cognitive impairment after stroke, and intriguingly, the timing matters: in the acute and subacute phases, cortical atrophy and white matter lesions do not correlate with PSCI, but by the chronic phase, patients with more severe cortical atrophy show higher overall impairment, with memory and attention deficits more likely to persist. Incorporating the medial temporal lobe atrophy scale into routine stroke MRI evaluation could alert clinicians to looming decline long before symptoms become disabling.

Cerebral small vessel disease (CSVD) represents perhaps the most intricate imaging frontier. White matter lesions, visible as high signal on T2-weighted imaging, arise from chronic small vessel hardening that causes demyelination and gliosis, progressively destabilizing the neural fiber pathways that connect brain regions. In a three-year follow-up of 639 patients with white matter lesions, 90 progressed to dementia and 147 to cognitive dysfunction without dementia. Cerebral microbleeds, whose anatomical distribution varies with underlying etiology, lobar in cerebral amyloid angiopathy versus deep in hypertensive arteriolosclerosis, correlate negatively with cognitive function, and strictly lobar microbleeds or cortical superficial siderosis signal more than a fourfold increase in the odds of amyloid deposition. Perivascular spaces, the interstitial fluid channels that form part of the brain’s glymphatic clearance system, accumulate with age and vascular disease, and higher burden in the centrum semiovale is linked to elevated risk of mild cognitive impairment. Lacunar infarcts, the hallmark of CSVD, damage cognition through both strategic location, such as disruption of the anterior thalamic nuclei within the Papez circuit, and cumulative burden, the so-called lacunar state that dismantles fronto-subcortical networks governing executive function and processing speed. Critically, these markers rarely act alone: the total CSVD score, which integrates white matter hyperintensities, lacunes, microbleeds, and perivascular spaces into a pragmatic 0-to-4 scale, independently predicts poorer global cognition, and scores above 2 forecast long-term cognitive decline and incident dementia.

Cerebral perfusion adds a dynamic layer to this static picture. Chronic hypoperfusion after stroke reduces neuronal excitability and metabolic rate, and even slight impairment of cerebral blood flow regulation can significantly affect brain function. Perfusion deficits in the temporal and frontal lobes, hippocampus, and thalamus correlate with the degree of cognitive dysfunction, and the ischemic penumbra volume, the hypoperfusion intensity ratio, and collateral circulation status all shape final infarct volume and, indirectly, cognitive trajectory. In patients with asymptomatic middle cerebral artery stenosis, poor collateral circulation is frequently comorbid with cognitive domain impairments, whereas good collaterals confer relative protection. A 12-year longitudinal study further revealed that reduced cerebral blood flow is associated with progression of brain atrophy, cementing perfusion as a driver of neurodegeneration rather than a mere bystander.

The most exciting advances, however, lie in multimodal and computational imaging. Diffusion tensor imaging captures damage to white matter tract integrity, while the DTI Analysis along the Perivascular Space (DTI-ALPS) index offers a rapid, contrast-free, noninvasive window into glymphatic system function, the brain’s waste-clearance machinery. Studies show the ALPS index on the lesion side is significantly reduced in ischemic stroke patients, most pronounced in the acute phase with partial recovery over weeks to months, providing a reliable reference for distinguishing early cognitive impairment and predicting unfavorable six-month outcomes. Functional MRI reveals that brain networks in PSCI patients shift toward a suboptimal state with incomplete compensation centered on the frontoparietal network, while combined ASL-fMRI analysis demonstrates altered neurovascular coupling correlated with cognitive scores and white matter lesion burden. Functional near-infrared spectroscopy, portable and motion-robust, detects significantly reduced activation in the left dorsolateral prefrontal cortex of PSCI patients, and its centroid values serve as sensitive severity indicators. Meanwhile, deep learning is automating the entire pipeline: U-Net architectures segment white matter hyperintensities from FLAIR images with high accuracy across multi-center datasets, 3D U-Net models detect microbleeds with performance comparable to expert raters, and interpretable 3D frameworks grade enlarged perivascular spaces in acute stroke cohorts, minimizing inter-rater variability and embedding total CSVD burden scores directly into clinical workflows.

Treatment remains multifaceted rather than curative, anchored in aggressive control of vascular risk factors such as hypertension, diabetes, and hyperlipidemia. No drugs are specifically approved for PSCI, so clinicians borrow from Alzheimer’s protocols, deploying cholinesterase inhibitors like donepezil, calcium antagonists such as nimodipine, and the NMDA receptor antagonist memantine, with traditional Chinese medicines showing promise in regional cohorts that await large-scale international validation. Non-pharmacological approaches, including acupuncture, hyperbaric oxygen therapy, repetitive transcranial magnetic stimulation, and computer-assisted cognitive rehabilitation, are gaining recognition for safety and patient compliance. The review’s authors argue that the next frontier demands longitudinal, multi-timepoint imaging studies at baseline, 3, 6, 12, and 24 months to distinguish individual patterns of cognitive resilience from accelerated decline, and to unravel how acute vascular injury interacts with pre-existing amyloid and tau pathology. As imaging, fluid biomarkers, and artificial intelligence converge, the prospect of dynamic risk stratification and personalized secondary prevention is moving from aspiration toward clinical reality, offering stroke survivors a fighting chance to keep the self they thought they had lost.

Subject of Research: Neuroimaging biomarkers and multimodal imaging advances for post-stroke cognitive impairment

Article Title: Imaging research, diagnosis, and treatment advances of post-stroke cognitive impairment

Article References: Huang, M., Jia, Q., Ouyang, Y., Feng, X., & Ju, R. (2026). Imaging research, diagnosis, and treatment advances of post-stroke cognitive impairment. Frontiers in Neuroimaging, 5, Article 1691870. https://doi.org/10.3389/fnimg.2026.1691870

Image Credits: AI Generated

DOI: 10.3389/fnimg.2026.1691870

Keywords: post-stroke cognitive impairment, neuroimaging, cerebral small vessel disease, stroke, MRI, DTI-ALPS, biomarkers, dementia, perfusion imaging, artificial intelligence, Imaging, research

PSCI is a notoriously difficult target. Its clinical course is heterogeneous, ranging from subtle deficits in attention and executive function to overt dementia, and its trajectory is highly variable from patient to patient. Current prediction relies largely on conventional clinical variables, neuroimaging findings, and bedside cognitive screening instruments, all of which have well-documented weaknesses during the acute phase. Aphasia, sedation, and neurological fluctuation routinely interfere with early cognitive assessment, while existing clinical prediction models built on variables such as NIHSS score, age, diabetes, atrial fibrillation, and homocysteine, though reported to achieve AUCs between 0.77 and 0.90 in development cohorts, still fail to capture the complex pathophysiology underlying cognitive decline. What clinicians lack is an objective, quantifiable biomarker panel that can stratify risk before symptoms emerge.

The logic behind a metabolic approach is compelling. Ischemic stroke triggers a cascade of disrupted energy metabolism, mitochondrial dysfunction, excitotoxicity, oxidative stress, and neuroinflammation, many aspects of which leave fingerprints in circulating metabolite levels. Stroke also provokes broad peripheral changes, including lipid remodeling, amino acid dysregulation, and perturbation of neurotransmitter-related pathways, which can shape cognitive recovery by affecting neuronal integrity, synaptic plasticity, and cerebrovascular health. Previous work has hinted at the potential: elevated serum ratios of quinolinic acid to kynurenic acid have predicted three-month cognitive outcomes, and the Nor-COAST cohort linked neopterin, kynurenine metabolites, and vitamin B6-related indicators to PSCI development. Choline pathway metabolites and homocysteine have also been independently associated with post-stroke cognitive risk. But most of these studies were targeted, focused on one or a few pathways, and rarely extended to building validated prediction models in the acute window.

The new study, led by Xiangwen Hao and Bianying Feng of Shanghai Fourth People’s Hospital with senior authors Li Tian and Qiu Hong Man, took an untargeted approach. The team enrolled 156 patients admitted within twenty-four hours of symptom onset and, after exclusions for pre-existing cognitive impairment, severe aphasia, psychiatric illness, and other factors, assembled a final cohort of 130. Serum collected on admission was subjected to untargeted liquid chromatography-tandem mass spectrometry using both reversed-phase and HILIC separation on a high-resolution Orbitrap platform, with pooled quality-control samples inserted after every ten study samples to monitor instrument stability. After rigorous preprocessing, including variance-stabilizing normalization and random-forest-based batch correction, 806 serum metabolite features were retained for analysis.

Three months later, patients were assessed with the Telephone Montreal Cognitive Assessment, a validated remote screening tool, and classified as PSCI if they scored below the pre-specified cutoff of 19. The cohort split almost evenly: 64 patients developed cognitive impairment and 66 remained cognitively intact. Comparison of the two groups’ admission serum profiles revealed 51 candidate differential metabolites, twenty upregulated and thirty-one downregulated in those who later declined cognitively. The largest fold change belonged to 5-aminopentanoic acid, a lysine degradation intermediate produced both endogenously and by gut bacteria, which was elevated in PSCI patients and negatively correlated with cognitive scores. Strikingly, key intermediates of caffeine catabolism, including 1,3-dimethyluric acid, theophylline, and 3,7-dimethyluric acid, were markedly reduced in the PSCI group, suggesting altered purine metabolism and potentially disrupted adenosine receptor signaling, a pathway implicated in neuronal excitability, neuroinflammation, and synaptic regulation.

Pathway enrichment analysis mapped these differences onto several interconnected metabolic domains rather than a single dominant mechanism. Bile acid metabolites such as taurocholic acid, cholic acid, and glycochenodeoxycholic acid pointed to bile acid biosynthesis and secretion pathways, increasingly recognized as players in gut-liver-brain communication and systemic inflammation. Lipid features, including docosahexaenoic acid, linoleic acid, and multiple glycerophospholipid species, mapped to unsaturated fatty acid and glycerophospholipid metabolism, and correlated positively with cognitive scores, consistent with the known importance of polyunsaturated fatty acids in membrane structure and synaptic function. Amino acid-related pathways, including arginine and proline metabolism, rounded out the picture. Notably, docosahexaenoic acid and two ether-linked phospholipid species correlated positively with three-month cognitive scores, while the purine metabolite 7-methylguanosine and the indole compound indole-4-carboxaldehyde correlated negatively, tying purine, tryptophan-derived, and lipid metabolic signals directly to cognitive performance.

To move beyond single metabolites, the researchers applied multiscale embedded correlation network analysis across all 806 metabolites spanning thirty biochemical categories. The result was striking: 15,120 metabolite pairs showed significantly different correlation patterns between the two groups. Nearly half of these involved outright reversals, with 3,807 pairs flipping from positive correlations in cognitively intact patients to negative correlations in PSCI patients and 3,671 showing the reverse transition. Others involved newly forged strong correlations or the dissolution of existing ones. The magnitude of this correlation rewiring suggests that patients who later developed cognitive impairment experienced a fundamentally reorganized acute-phase metabolic network, encompassing amino acid handling, lipid metabolism, inflammatory responses, and microbiota-associated features, rather than isolated changes in individual compounds.

The centerpiece of the study is its prediction model. Using bootstrap-LASSO stability selection across 1,000 iterations, with a selection-frequency threshold of 75 percent chosen through sensitivity analysis, the team retained six metabolites: 6-hydroxymellein, 21-deoxycortisol, inosine, 2-hydroxy-3-methylbutyric acid, isoleucyl-arginine, and propylparaben. Under stratified ten-fold cross-validation, this metabolite-only model achieved an AUC of 0.774, with a sensitivity of 0.609 and a specificity of 0.848 at the optimal cutoff. Within the multivariable model, 21-deoxycortisol, an intermediate of adrenal corticosteroid synthesis, showed the strongest positive association with PSCI risk, with an odds ratio of 2.15, hinting at a role for acute hypothalamic-pituitary-adrenal axis activation, a stress response long linked to hippocampal vulnerability and impaired synaptic plasticity. Elevated inosine and the branched-chain amino acid catabolic intermediate 2-hydroxy-3-methylbutyric acid also carried increased odds of impairment, while the dipeptide isoleucyl-arginine and the preservative-derived propylparaben trended toward protective associations.

Perhaps the most provocative result is what the comparison models revealed. A core clinical-only model built from age, sex, education, admission NIHSS score, body mass index, and prior stroke history managed an AUC of just 0.525, barely better than chance in this cohort. Adding the clinical variables to the metabolites did not help either; the combined model reached an AUC of 0.685, still short of the metabolite-only panel. Calibration analysis showed good agreement between predicted and observed risk, with no significant lack of fit, and decision curve analysis indicated the model would deliver net benefit over both treat-all and treat-none strategies across a broad range of threshold probabilities. The authors note that the six predictors span endogenous steroidogenic, purinergic, and amino acid catabolic pathways alongside dietary and xenobiotic exposure markers, underscoring that the acute metabolic risk signature is multidimensional.

The researchers are careful to frame their findings as an early step rather than a finished clinical tool. The cognitive outcome was assessed by telephone screening rather than a full neuropsychological battery, and the model was validated only internally, without an independent external cohort. Untargeted mass spectrometry features would also need conversion into targeted, reproducible, and clinically feasible assays before any bedside deployment. Still, the study demonstrates that the blood of a newly admitted stroke patient already contains readable information about how the brain will fare over the following months. If external validation confirms the six-metabolite panel, emergency departments could one day use a routine admission blood draw to flag high-risk patients for closer cognitive follow-up and early preventive intervention, turning the first hours after a stroke into a window of opportunity for protecting the mind as well as saving it.

Subject of Research: Acute-phase serum metabolomic signatures for early prediction of post-stroke cognitive impairment after ischemic stroke

Article Title: Acute-phase serum metabolomics signatures for predicting post-stroke cognitive impairment after ischemic stroke: a prospective cohort study

Article References: Hao, X., Feng, B., Zhang, X., Li, Y., Chen, J., Zheng, X., Tian, L., & Man, Q. H. (2026). Acute-phase serum metabolomics signatures for predicting post-stroke cognitive impairment after ischemic stroke: a prospective cohort study. Metabolomics, 22(5), Article 148. https://doi.org/10.1007/s11306-026-02521-6

Image Credits: AI Generated

DOI: 10.1007/s11306-026-02521-6

Keywords: post-stroke cognitive impairment, ischemic stroke, metabolomics, biomarkers, serum metabolites, LC-MS/MS, prediction model, bile acid metabolism, caffeine metabolism, docosahexaenoic acid, Bootstrap-LASSO, risk stratification