Background Transcranial magnetic stimulation (TMS) is an effective therapy for patients with Alzheimer’s Disease (AD), potentially modulating aberrant functional connectivity. Electroencephalography (EEG) microstates represent transient large-scale resting networks and have emerged as candidate markers for AD. However, their modulation by repetitive TMS (rTMS) and intermittent theta burst stimulation (iTBS) protocols remains to be elucidated. Methods Resting-state EEG was recorded from 28 AD patients at baseline and following the 1st, 7th, and 14th sessions of rTMS or iTBS treatment. Polarity-insensitive modified k-means clustering was used to segment EEGs into constituent microstates, which were then subjected to source localization to identify their corresponding cortical regions. Longitudinal changes within subjects in clinical status and microstate parameters (duration, occurrence, coverage, transition probability) were evaluated with one-way repeated-measures analysis of variance, and differences between rTMS and iTBS groups were assessed using t-tests. Results Four microstates (MS A-D) were identified from EEG data. rTMS predominantly induced sustained suppression of MS-B, whereas iTBS elicited early-phase increases in MS-C activity. Clinical symptoms improvement following TMS correlated with increased MS-C and decreased MS-B activity. Source localization revealed rTMS predominantly modulated MS-B generators in the occipital cortex (impacting visual and dorsal attention networks), while iTBS preferentially engaged MS-C generators in the parietal cortex (affecting sensorimotor and frontoparietal networks). Conclusions We identified distinct EEG microstates and their underlying cortical generators associated with clinical improvement in AD following treatment with rTMS and iTBS protocols. The results demonstrate protocol-specific spatiotemporal modulation profiles and temporal dynamics, highlighting differential neural mechanisms of rTMS and iTBS.

This study investigated the effects of repetitive transcranial magnetic stimulation (rTMS) on theta and alpha band dynamics within brain networks in Alzheimer’s disease (AD) patients. We analyzed cognitive task state data during rTMS, focusing specifically on changes across the encoding, maintenance, and retrieval phases of working memory (WM). Dynamic functional connectivity (dFC) matrices were used to analyze graph-theoretic topological properties of brain networks. Furthermore, we examined the correlation between alterations in the topological properties of these dynamic brain networks and changes in WM performance metrics as well as clinical scale scores. Based on our results, we provide a comprehensive discussion on how rTMS influences the dynamic characteristics of WM brain networks in AD patients. This work offers a theoretical foundation for understanding the mechanisms by which rTMS may improve WM performance in AD, highlighting its potential therapeutic implications.

Precision medicine for Alzheimer’s disease (AD) requires the development of a robust management framework grounded in individualized disease staging systems. To date, only a limited number of studies have supplemented the existing AD staging systems. This retrospective study included 7491 MRI examinations from five independent cohorts. We used a novel pseudo-healthy synthesis method to capture individualized brain atrophy patterns. An individualized brain atrophy score (BAS) was computed from the 30 regions with the most severe brain atrophy and used to stratify participants into distinct disease stages. The Jenks natural breaks optimization method was used to determine an optimal number of disease stages based on the individual BAS. BAS exhibited a strong biological basis and revealed a synergistic relationship among biomarker-based staging systems. Four stages were delineated based on the BAS for participants with MCI and clinically diagnosed AD. Stage I showed a slight cognitive decline with only mild hippocampal atrophy evident. Stage II showed mild cognitive decline and mild brain atrophy and shrinkage, extending to the temporal and parietal lobes. Stage III showed moderate cognitive decline and more severe brain atrophy in the temporal lobe, amygdala, hippocampus, parietal lobe, and frontal lobe. Stage IV showed severe mental impairment and diffuse atrophy across the whole brain. The disease stages are associated with dementia severity and abnormalities in AD biomarkers, such as cerebrospinal fluid (CSF) Aβ1–42, CSF total tau, CSF p-tau181, and cognitive scores. Furthermore, those MCI participants at higher disease stages at baseline have a higher risk of progressing to clinically diagnosed AD dementia even under the A/T-negative status. The individualized staging system can accurately assess disease severity, enabling risk stratification at ultra-early pathological stages and facilitating precise AD management.

The diagnostic criteria for Alzheimer's disease (AD) has undergone the fundamental shift, moving from clinical symptom-based approach to the focus on precise pathological changes. This paper focuses on the diagnostic advances and future prospects of cerebrospinal fluid biomarkers, blood biomarkers, and molecular imaging biomarkers for Alzheimer's disease, aiming to provide guidance for the early and accurate diagnosis of the disease.

Alzheimer's disease (AD) is associated with impaired connectivity in critical functional networks. This study investigated the effects of 20 Hz transcranial magnetic stimulation (TMS) on brain network mechanisms in 25 patients with AD, including 17 in the TMS group and 8 in the sham group. We analyzed resting-state functional magnetic resonance imaging data, using the amplitude of low-frequency fluctuations (ALFF) and fractional ALFF (fALFF) to quantify neural activity and identify regions of interest. Subsequently, changes in static and dynamic functional connectivity were analyzed based on these regions. The results showed that: (1)In the TMS group, significant increases in ALFF/fALFF were observed specifically in the right dorsolateral superior frontal gyrus (SFGdor.R) and the left anterior cingulate gyrus (ACG.L); (2)Enhanced static functional connectivity between the SFGdor.R and the right middle temporal gyrus was positively correlated with improvements in Montreal Cognitive Assessment scores, while reduced static functional connectivity between the ACG.L and the left inferior temporal gyrus was associated with gains in Boston Naming Test scores; (3)Improvements in both Montreal Cognitive Assessment scores and Mini Mental State Examination scores were linked to decreased dynamic functional connectivity variability between the ACG.L and the middle occipital gyrus. These findings suggest that TMS improves cognitive and behavioral performance in patients with AD through multiscale regulatory effects, and that this improvement may be associated with alterations in functional integration among brain regions as well as reduced variability of abnormal network dynamics, providing new insights into the mechanism of action of TMS in AD.

‌ Background ‌: While structural connectome analysis enables preoperative mapping of glioblastoma (GBM) infiltration, mass effect-induced distortion compromises the accuracy of peritumoral tract assessment. We aimed to investigate fiber disruption characteristics and predict short-term progression based on structural connectivity features after eliminating mass effect. Methods : We retrospectively analyzed 113 GBM patients with ≥ 90% resection and 65 healthy controls. Diffusion tensor imaging (DTI) data were processed to construct structural connectomes, which were segmented into three compartments relative to the resection cavity: Tumor disrupted cerebral regions, anatomically confined to FLAIR hyperintense areas and direct fiber disruption; Distant disrupted cerebral regions, outside FLAIR hyperintense areas but exhibiting direct fiber disruption; Indirect disrupted cerebral regions, remote from FLAIR lesions with indirect fiber disruption. The patterns of differential disruption across compartments and progression timelines were quantified, along with their correlations to the Karnofsky performance status (KPS). The Area Under the Curve (AUC) evaluated how well disrupted fibers predict progression time. Patients with fiber disruption counts exceeding the Youden index were classified as high-risk versus low-risk for progression, validated by Kaplan-Meier analysis and Chi-square test. Structural connectivity disruption were used to predict short-term progression via Cox regression. Results : After eliminating mass effects, widespread structural connectome disruption was observed. Among 49 within 1-year progressers, tumor-disrupted regions showed more severe fiber disruption than later-progressing patients (F = 32.5, P < 0.001). Fiber disruption in tumor-disrupted compartment negatively correlated with pre-radiotherapy KPS score (r=-0.349, P < 0.001), and best predicted progression time (AUC = 0.803, P < 0.001). High-risk patients progressed faster (10 months) than low-risk patients (15 months) ( P < 0.001). 81% of low-risk and 71% of high-risk patients were correctly identified (χ²=30.29, P < 0.001). Incorporating structural connectivity disruption significantly improved multivariable Cox regression performance over clinical/imaging variables alone ( P < 0.001). Conclusions : Structural connectivity quantitatively maps postoperative regional cerebral disruption in GBM. Fiber disruption within the tumor-disrupted compartment may identify patients for short-term progression.

INTRODUCTION: Mild cognitive impairment (MCI), a prodromal stage of Alzheimer's disease (AD), shows pronounced clinical heterogeneity poorly explained by pathology burden, representing a gap complicating prognosis. As the brain operates as a complex network for information integration, we hypothesized that connectome architecture mediates the link between AD pathology and clinical expression. METHODS: We developed a framework integrating structural and functional connectomes from multi-center cohorts, performing connectome-based subtyping in MCI, with analyses of upstream pathology, downstream phenotypes, and transcriptomic associations. RESULTS: This approach identified an "MCI-compromised" (MCI-C) subgroup characterized by extensive structural-functional connectomic disruption and an "MCI-preserved" (MCI-P) subgroup with relatively preserved connectome integrity. Despite comparable pathology, MCI-C demonstrated more severe neurodegeneration, accelerated cognitive decline, and elevated progression risk. Multiscale analyses linked these patterns to transcriptomic profiles of mitochondrial, synaptic, and neuroimmune processes. DISCUSSION: These findings demonstrate that the connectome acts as a critical mediator, rather than a passive endophenotype, shaping AD clinical expression.

While resting-state brain dysfunctions have been extensively investigated in Alzheimer's disease (AD), the dynamic alterations of functional systems remain poorly understood. We employed co-activation pattern (CAP) analysis to characterize the functional-state alterations in 243 participants using resting-state fMRI data and applied graph theory analysis to estimate corresponding topological properties. The CAP analysis identified five distinct brain states across groups: State 1 (limbic network dominated), State 2 (dorsal attention network (DAN) and central executive network dominated), State 3 (default mode network and central executive network dominated), State 4 (somatomotor network and ventral attention network dominated), and State 5 (DAN, sensorimotor, and visual networks dominated). Compared to cognitively unimpaired individuals, State 3 demonstrated significantly reduced persistence and resilience in both mild cognitive impairment (MCI) and AD groups. Additionally, both clinical groups (MCI and AD) exhibited decreased transitions from State 2 to State 5 and reduced self-transitions within State 3. Graph theory analysis revealed that compared to cognitively unimpaired individuals, MCI and AD individuals had increased node degree centrality and node efficiency, alongside decreased node local efficiency in regions within the default mode network (DAN) and visual network, which corresponded well with CAP analysis results. Our findings provide a multiscale framework linking dynamic state instability to static network reorganization, advancing understanding of the dynamic functional alterations underlying cognitive decline in AD spectrum disorders.

The human cortical functional hierarchy, spanning from primary sensorimotor to transmodal association regions, represents a fundamental principle of brain organisation. Here, we show lifespan changes in the sensorimotor-association (S-A) gradient in the cortical functional hierarchy using multimodal neuroimaging data from 33,247 participants aged 32 postmenstrual weeks to 80 years. We identify three critical neurodevelopmental milestones: initiation (third trimester to perinatal period), establishment (infancy to early childhood), and expansion-stabilisation (late childhood to adulthood). Pronounced gradient changes are predominantly observed during the first decade, with continued refinement extending into mid-adulthood. Spatiotemporally heterogeneous growth patterns in functional gradients align with evolutionary hierarchies, segregation-integration dynamics, structural maturation, and cognitive spectrum development, proceeding along a dominant S-A growth axis. These findings establish a unified neurodevelopmental framework that links connectome gradient dynamics to multifaceted functional and structural properties, advancing our understanding of cortical hierarchy maturation across the lifespan.

Biological sex fundamentally shapes human brain organization, but sex-specific normative neuroanatomical trajectories across the lifespan remain largely uncharted. Here, we constructed independent, sex-specific lifespan brain charts using structural neuroimaging data from 59,915 healthy individuals (29,760 males and 30,155 females) ranging in age from 266 postconception days to 100 years. By examining 296 structural phenotypes across global, cortical, and subcortical measures, these models revealed widespread sex differences in maturational timing, with males reaching peak milestones later than females. These trajectories demonstrate that sex differences evolve dynamically, with phenotype-specific windows of emergence and maximal separation. Compared with conventional sex-pooled references, sex-specific models achieved superior predictive accuracy and reduced misestimation of individual deviations in healthy populations. Across five neuropsychiatric conditions, sex-specific models improved the detection of extreme deviations and revealed both shared and sex-dependent patterns of disorder-related neuroanatomical abnormalities. These sex-specific charts establish tailored normative references for assessing brain development, ageing, and disease.

While Transcranial Magnetic Stimulation (TMS) shows therapeutic potential for Alzheimer’s Disease (AD), its mechanisms of action remain unclear. This study investigated TMS-induced brain network restructuring in 17 AD patients by analyzing resting-state fMRI before and after 20 Hz treatment, identifying regions of interest (ROIs) based on ALFF and fALFF changes. Subsequently, functional connectivity (FC) analyses were conducted based on these ROIs. At a global level, topological properties and FC between sub-networks were also analyzed. Our findings revealed increased ALFF and fALFF in the right dorsolateral prefrontal gyrus and the left anterior cingulate gyrus. Using the right dorsolateral prefrontal gyrus as a seed, we observed enhanced FC with the right middle temporal gyrus, which was related to improved cognitive performance. Similarly, using the left anterior cingulate gyrus as a seed, we found decreased FC with the left inferior temporal gyrus, correlating with improvements in semantic understanding. From a global perspective, AD patients exhibited a significant increase in clustering coefficient and strengthened FC between the dorsal and ventral attention networks (DAN/VAN). These results suggest that TMS may regulate cross-regional functional collaboration, enhance the integration of key subnetworks, and promote comprehensive improvement in patients’ cognitive abilities.

Oxytocin (OT) is a neuropeptide widely implicated in emotional regulation and social cognition. However, its effects on dynamic brain connectivity remain poorly understood. In this study, we applied co-activation pattern (CAP) analysis to resting-state fMRI data to examine how a single intranasal dose of OT modulates whole-brain functional dynamics. Participants included healthy young (18-31 years) and older (63-81 years) adults, with analyses conducted at both the group level and across age subgroups. OT significantly altered temporal properties of brain states, including increased frequency, in-degree, and out-degree in multiple CAPs, indicating enhanced network flexibility and switching. Notably, OT modulated states involving the amygdala, medial prefrontal cortex, and salience network, regions critical for emotion regulation, and increased self-transition probabilities, suggesting greater within-state stability. Age-stratified analysis revealed differential sensitivity: young adults exhibited more pronounced modulation and greater dynamic flexibility, while older adults showed more sustained engagement with emotion-related states. Importantly, only in the elderly OT and combined young subgroups did time spent in these states significantly correlate with cognitive performance on the Digit Symbol Substitution Test, suggesting that OT-enhanced engagement in these networks supports compensatory mechanisms during aging. No such correlations were found in young participants or in either age group under placebo, highlighting the specificity of oxytocin's functional relevance in older adults. Meta-analytic decoding using Neurosynth confirmed that OT-modulated regions are closely associated with emotion, memory, and social cognition. These findings demonstrate that OT shapes transient brain dynamics in age- and function-specific ways. CAP analysis provides a powerful approach for capturing such neuromodulatory effects.

Objective.High accuracy in medical classification tasks does not ensure that neural networks reason in ways consistent with clinical or neurobiological understanding. This study examines whether a Transformer-based model trained on resting-state electroencephalography (EEG) infers cognitive impairment through physiologically meaningful mechanisms.Approach.A lightweight Transformer was trained on resting-state EEG to detect mild cognitive impairment. The model's probabilistic outputs were interpreted as continuous cognitive risk scores. Knowledge distillation and spatial perturbation analyses were performed to identify the electrophysiological features and cortical regions underlying the model's predictions.Main results.The model achieved an average accuracy of 75.4% in five-fold cross-validation, and generalized to Alzheimer's disease cohorts and an external clinical center. The derived risk scores correlated with Montreal Cognitive Assessment subdomains, particularly memory, language and orientation. Key drivers included increased autocorrelation, reduced Lempel-Ziv complexity and changes in power spectral density. Perturbation analyses highlighted strong contributions from the insular cortex and the transverse temporal regions.Significance.The model's decision process reflects physiologically and anatomically interpretable patterns consistent with clinical reasoning, supporting EEG-based modeling as an objective tool for quantifying cognitive function.

The intersection of neuroscience and artificial intelligence (AI) offers new avenues for understanding and replicating human creativity. However, current AI systems struggle to capture the depth, emotion, and cognitive complexity inherent in human artistic expression. In response, we propose MindCanvas, an AI framework that integrates fMRI with diffusion models to generate artwork directly from neural signals. MindCanvas decodes brain activity, reconstructs mental images, and refines them through text prompts, producing not only the final artwork but also a dynamic video that captures the strokeby-stroke creative process. Two user studies demonstrated the effectiveness of the model in translating neural activity into visually compelling and cognitively resonant artworks. By maintaining coherence between neural signals and artistic output, MindCanvas addresses key limitations in existing AI art systems, offering a novel approach that mirrors the evolving and emotional nature of human creativity. Our results underscore the potential of merging neuroscience and AI to create art that transcends technical inputs, moving toward a deeper, more holistic representation of human creativity.

Background: Poststroke cognitive impairment (PSCI) is a leading cause of long-term disability. Immunosenescence, marked by thymic involution and T-cell aging, drives chronic neuroinflammation. The gut microbiota regulates thymic T-cell differentiation and peripheral expansion, while dysbiosis accelerates thymic atrophy and T-cell aging, forming a “gut–thymus–T-cell aging” axis. Whether this axis affects brain injury and cognition after stroke is unknown. We hypothesize that targeted microbiome modulation can restore gut–thymus–brain homeostasis, preserve thymic function, and delay immunosenescence, thereby reducing brain injury and improving cognition. Methods: Clinical study: 151 stroke patients (>3 months) were assessed with MMSE and MoCA. Flow cytometry, ELISA, and 16S rDNA sequencing evaluated T-cell aging, thymic function, intestinal permeability, and microbiota composition. Animal study: Male 6–8-month-old mice underwent photothrombotic MCAO and received intragastric Faecalibacterium prausnitzii (Fp) or vehicle. Neurological and cognitive functions were tested. Fecal 16S rDNA sequencing and plasma metabolomics were performed. RT-qPCR, immunofluorescence, histology, and RNA-seq analyzed thymus and brain. Results: Clinically, PSCI patients showed greater T-cell aging, thymic involution, and intestinal permeability versus non-PSCI, with Fp abundance positively correlating with thymic and cognitive function. In mice, Fp improved neurological and cognitive performance, preserved intestinal architecture, reduced inflammation, restored thymocyte counts and output, improved cortex–medulla ratio, and increased naïve T cells. In the brain, Fp reduced neuroinflammation and alleviated synaptic loss. Microbiota analysis revealed increased beneficial species and improved GMHI. Metabolomics showed reduced proinflammatory metabolites (prostaglandin 2, tremetone, ibotenic acid) and increased neuroprotective tryptophan derivatives. Thymic RNA-seq indicated regulation of MAPK, IL-17, T-cell receptor, and senescence pathways; brain RNA-seq revealed suppression of innate immunity and glial activation with enhanced synaptic maturation. Conclusion: This integrated clinical–preclinical study demonstrates that microbiome-based restoration of the gut–thymus–brain axis via Fp preserves thymic function, counteracts immunosenescence, and promotes cognitive recovery after stroke. These findings position targeted microbiota interventions as a promising therapeutic avenue for PSCI.

The cerebellar role in various cognitive functions other than motor coordination has been gradually recognized, while its functional architecture and interaction with white matter functional networks (WM-FNs) remain unclear. The study combined resting-state functional connectivity and K-means clustering methods to obtain nine WM-FNs and seven gray matter functional networks (GM-FNs) by using the test-retest neuroimaging dataset collected from human connectome project. Subsequently, adopting winner-take-all algorithm and two distinct connectivity-based parcellations, we identified two parcellation maps of the cerebellum that corresponded to WM- and GM-FNs, respectively. We observed the cerebellar parcellations with unique spatial distribution pattern, which corresponds to white matter and gray matter functional systems. Additionally, the distinct WM-FNs exhibited high functional connectivity with GM-FNs, which corresponded to the overlapping maps between their cerebellar sub-regions well, indicating that intrinsic functional connectivity has an important constraint on the topological structure of cerebellum. Our finding created a new cerebellar parcellation atlas, advancing the mechanisms’ understanding of interaction between cerebellar organization and functional systems.

Cerebral asymmetry is a core principle of human brain organization, showing dynamic changes across the lifespan and alterations in brain disorders. However, it remains unclear whether lifespan trajectories of asymmetry differ across populations. We compared lifespan structural asymmetry normative charts of 221 cerebral imaging phenotypes from 43,037 Chinese and 56,339 Western participants aged 0–100 years. The two populations showed distinct lifespan asymmetry patterns in 26.2% of the phenotypes. Chinese-minus-Western asymmetry difference curves displayed distinct patterns across brain phenotypes: rightward (45.7%), leftward (26.2%), rightward-to-leftward (11.8%), leftward-to-rightward (10.0%), and unclassified (6.3%). Population-matched normative models outperformed population-unmatched normative models in capturing normal asymmetry variability among healthy individuals and in detecting abnormal asymmetry deviations in patients with Alzheimer’s disease, mild cognitive impairment, schizophrenia, and major depressive disorder. These findings indicate that population mismatch can bias chart-based individual-level asymmetry assessment and underscore the need for population-representative brain asymmetry normative charts.

Purpose To examine common patterns among different computer-aided diagnosis (CAD) models for Alzheimer disease (AD) using structural MRI data and to characterize the clinical and imaging features associated with their misclassifications. Materials and Methods This retrospective study used 3258 baseline structural MRI scans from five multisite datasets and two multidisease datasets collected between September 2005 and December 2019. The 3D Nested Hierarchical Transformer (3DNesT) model and other CAD techniques were used for AD classification using 10-fold cross-validation and cross-dataset validation. Subgroup analysis of CAD-misclassified individuals compared clinical and neuroimaging biomarkers using independent t tests with Bonferroni correction. Results This study included 1391 patients with AD (mean age, 72.1 years ± 9.2 [SD]; 757 female), 205 with other neurodegenerative diseases (mean age, 64.9 years ± 9.9; 117 male), and 1662 healthy controls (mean age, 70.6 years ± 7.6; 935 female). The 3DNesT model achieved 90.0% ± 2.3 cross-validation accuracy and 82.2%, 90.1%, and 91.6% accuracy in three external datasets. Further analysis suggested that the false-negative subgroup (n = 223) exhibited minimal atrophy and better cognitive performance on the Mini-Mental State Examination (MMSE) than the true-positive subgroup (MMSE score in false-negative subgroup, 21.4 ± 4.4; true-positive subgroup, 19.7 ± 5.7; P value family-wise error [PFWE] < .001), despite displaying similar levels of amyloid β (false-negative subgroup, 705.9 pg/mL; true-positive subgroup, 665.7 pg/mL; PFWE = .99) and tau (false-negative subgroup, 352.4 pg/mL; true-positive subgroup, 371.0 pg/mL; PFWE = .99) burden. Conclusion A subgroup of patients with false-negative classification for Alzheimer disease exhibited atypical structural MRI patterns and clinical measures, fundamentally limiting the diagnostic performance of CAD models based solely on structural MRI. Keywords: MR Imaging, Dementia, Computer Applications-3D, Alzheimer's Disease, Computer-aided Diagnosis, Misclassification, Atypical AD Supplemental material is available for this article. © RSNA, 2025 See also commentary by Nasrallah in this issue.