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.

Seizure frequency is a key indicator of disease severity and treatment response in epilepsy, yet its molecular determinants remain unclear. We performed proteomic profiling of resected epileptogenic brain tissue from patients, stratified by seizure frequency and by temporal versus extratemporal origin, and functionally validated candidate differentially expressed proteins (DEPs). Seizure foci of high and low frequencies in distinct brain regions displayed region-specific proteomic profiles. However, bioinformatic analyses of both temporal and extratemporal cohorts consistently showed that the down-regulated proteins converge on mitochondrial localization and function. Among these, mitochondrial thymidine kinase 2 (Tk2) exhibited a robust inverse correlation with seizure frequency, a finding confirmed in patient tissues across different frequency groups. Consistently, Tk2 expression was reduced across multiple brain regions in two seizure models induced by pilocarpine or ferric chloride. Mechanistically, loss of Tk2 activated the cGAS-STING pathway, upregulated inflammatory genes, and then increased seizure susceptibility. These findings identify Tk2 as a mitochondrial kinase that couples energetic failure to neuroinflammation, and provide a mechanistic basis for targeting the Tk2-mitochondria-inflammation axis in epilepsy.

Humans develop shared concepts of others' emotions to support adaptive social functioning, yet how these concepts are dynamically represented in major depressive disorder (MDD) during naturalistic movie viewing is not yet fully established. Using functional MRI, we examined patients with MDD (n = 55) and healthy controls (HCs; n = 62) as they freely viewed movie clips depicting happy and sad emotions. Neural similarity was quantified with inter-subject correlation at whole-brain, network, and regional levels, and its association with emotional traits was assessed using inter-subject representational similarity analysis. Compared with HCs, patients with MDD showed significantly reduced whole-brain similarity, particularly during sad contexts. Network analyses revealed that HCs exhibited increased similarity in the limbic network during sadness, reflecting a shared "sadness resonance," whereas patients with higher depressive severity showed widespread disruptions across visual, limbic, dorsal attention, and default mode networks. At the regional level, similarity in the inferior temporal gyrus and lateral occipital cortex was closely linked to individual differences in emotional awareness, with pronounced context- and region-specificity. These findings highlight neural decoupling and heterogeneity as core features of MDD and provide new evidence for potential biomarkers to inform risk assessment and personalized interventions.

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.

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.

The community detection-based method for measuring brain network switching rate is inherently limited in characterizing the complex dynamics of neural processes. This constraint fundamentally originates from its exclusive reliance on pairwise connectivity metrics and inability to capture higher-order interactions. To address this limitation, we propose a hypergraph-based framework for quantifying node switching rate, which provides a conceptual shift in perspective for evaluating brain flexibility. This method models brain regions as nodes and depicts multibody interactions via hyperedges, thereby enabling explicit characterization of higher-order functional dynamics. The core idea lies in calculating the sum of absolute differences in hyperedge types between consecutive time windows, normalized to yield the node switching rate. Additionally, we apply the proposed method to 25 seizure events from 11 patients with temporal lobe epilepsy and compare it with the community detection algorithm. Main results include: (i) The hypergraph-based switching rates are significantly elevated during the ictal phase compared to preictal and postictal phases. (ii) In the hypergraph-based method, the seizure onset zone (SOZ) exhibits intensified dynamic fluctuations in the preictal phase, whereas the propagation zone (PZ) demonstrates heightened switching activity during the ictal phase. Notably, all brain zones, including the non-involved zone (NIZ), show increased functional state transitions during seizures. (iii) The hypergraph-based method outperforms the community detection-based measure in capturing temporal complexity and interactions between brain regions. Collectively, the proposed method provides a novel perspective for investigating brain dynamics and demonstrates potential for addressing epilepsy and other neurological disorders.

Aberrant dynamic shifts in brain states are a hallmark of cognitive and behavioral dysfunctions in major depressive disorder (MDD), yet the underlying mechanisms of these disturbances remain elusive. Leveraging network control theory of morphological networks, we characterized aberrant brain dynamics and energy deficits of MDD patients in two independent cohorts. MDD patients exhibited reduced dynamic stability, characterized by elevated intra-state transitions and diminished inter-state transitions, which were associated with impaired control energy. Region-specific deficits of energy regulation capacity were observed in key nodes of the default mode and limbic networks, including the posterior cingulate cortex and temporal pole, which correlated with cognition and clinical symptoms in MDD patients. MDD-related energy inefficiency was related to multiscale energy architectures at cellular, molecular, and biological levels, including mitochondrial morphologies and functions, energy metabolism pathways, and brain metabolic patterns. Additionally, we demonstrated an association between energy demands and cortical dynamics, indicating a disrupted energy-dependent neurophysiological activity in MDD patients. Together, these results identified the energetic fundamentals underlying pathological brain-state transitions in MDD patients. Identifying energy-vulnerable nodes from a controllability perspective may therefore provide valuable targets for restoring normative neural dynamics in MDD.

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.

Structural covariance refers to the concurrent changes in one morphological measure between two brain regions. Structural covariance of cortical morphological measures such as cortical thickness (CT), surface area (SA), and cortical volume (CV) have been applied to identify brain structural differences between patients with neuropsychiatric disorders and healthy controls. However, the precise relationships between structural covariance patterns of different cortical measures remain largely unknown. Here, we optimized the preprocessing and calculation approaches of structural covariances and investigated both global (whole-brain-level) and regional (brain-region-level) structural covariance similarities between CT, SA, and CV in 35,580 individuals. We found that Pearson correlation outperformed partial correlation due to generating fewer negative correlations of uncertain biological significance and principal component regression outperformed the regressions of total intracranial volume and respective global measures in removing global effects and reducing negative correlations. We observed that both global and regional covariance similarities of SA-CV were much higher than those of CT-CV and CT-SA, although they were influenced by the selection of atlases and covariance values. We also found age and sex effects on structural covariances and age effects on covariance similarities. The higher SA-CV covariance similarities than CT-CV indicates that SA contributes more to CV covariance than CT, although CV is derived from both CT and SA. The lack of CT-SA covariance similarities suggests that CT and SA have different covariance patterns and should be used in combination in structural covariance studies.

Alzheimer’s disease (AD) is marked by disrupted brain network connectivity, which impairs functional hierarchy and contributes to cognitive decline. Two fundamental questions, however, remain open: what specific changes occur in the hierarchical organization of the AD brain, and whether rectifying these changes can restore cognitive function. To answer these, we first analyzed individualized functional hierarchical architecture across three large, independent fMRI datasets (MCADI, N = 711; ADNI, N = 621; OASIS-3, N = 506). We identified a reproducible pattern of hierarchical remodeling in AD, characterized by expansion of the dorsal attention network A and shrinkage of the control network A, with spatial variability shaped by underlying brain tissue properties. To evaluate the therapeutic relevance of this signature, we conducted a randomized controlled trial of transcranial alternating current stimulation (tACS; N=44). Targeted stimulation selectively reversed the remodeling trajectory, suppressing dorsal attention network expansion and countering control network contraction. These network improvements persisted for three months and were accompanied by sustained cognitive gains, with 80% of participants showing measurable improvement. Our results reveal a functional hierarchical signature of AD and establish its potential as a novel interventional target, while also providing mechanistic insights into the action of non-invasive neuromodulation.

Alterations in brain network centrality are key features of Alzheimer’s disease (AD) and may offer insights into the disruption of network organization underlying cognitive decline. We introduce a novel centrality metric, DomiRank, to characterize dominance-driven connectivity patterns in the human brain network, using a multi-center MRI dataset comprising 809 participants. Compared with conventional metrics, DomiRank centrality showed greater sensitivity in detecting AD-related network disruptions, particularly within the cingulate gyrus, precuneus, and subcortical hubs such as the basal ganglia—regions critical for cognition. Regional DomiRank alterations were significantly correlated with clinical cognitive scores, indicating their potential relevance to disease severity. Gene enrichment analysis revealed that areas with reduced DomiRank centrality were enriched for genes involved in synaptic signaling and neuronal communication, suggesting molecular mechanisms underlying network vulnerability. These findings highlight DomiRank centrality as a promising biomarker for characterizing network disorganization in AD, linking changes in brain connectivity with underlying molecular processes.

Brain atrophy emerges as a distinctive hallmark in various neurodegenerative diseases, demonstrating a progressive trajectory across diverse disease stages and concurrently manifesting in tandem with a discernible decline in cognitive abilities. Understanding the individualized patterns of brain atrophy is critical for precision medicine and the prognosis of neurodegenerative diseases. However, it is difficult to obtain longitudinal data to compare changes before and after the onset of diseases. In this study, we present a deep disentangled generative model (DDGM) for capturing individualized atrophy patterns via disentangling patient images into "realistic" healthy counterfactual images and abnormal residual maps. The proposed DDGM consists of four modules: normal MRI synthesis, residual map synthesis, input reconstruction module, and mutual information neural estimator (MINE). The MINE and adversarial learning strategy together ensure independence between disease-related features and features shared by both disease and healthy controls. In addition, we proposed a comprehensive evaluation of the effectiveness of synthetic pseudo-healthy images, focusing on both their healthiness and subject identity. The results indicated that the proposed DDGM effectively preserves these characteristics in the synthesized pseudo-healthy images, outperforming existing methods. The proposed method demonstrates robust generalization capabilities across two independent datasets from different races and sites. Analysis of the disease residual/saliency maps revealed specific atrophy patterns associated with Alzheimer's disease (AD), particularly in the hippocampus and amygdala regions. These accurate individualized atrophy patterns enhance the performance of AD classification tasks, resulting in an improvement in classification accuracy to 92.50 $\pm$ 2.70%.

The human cortex exhibits remarkable morphometric similarity between regions; however, the form and extent of lifespan network remodeling remain unknown. Here, we show the spatiotemporal maturation of morphometric brain networks, using multimodal neuroimaging data from 33,937 healthy participants aged 0-80 years. Global architecture matures from birth to early adulthood through enhanced modularity and small worldness. Early development features cytoarchitecturally distinct remodeling: sensory cortices exhibit increased morphometric differentiation, paralimbic cortices show increased morphometric similarity, and association cortices retain stable hub roles. Morphology-function coupling peaks in early adolescence and then decreases, supporting protracted functional maturation. These growth patterns of morphometric networks are correlated with gene expression related to synaptic signaling, neurodevelopment, and metabolism. Normative models based on morphometric networks identify person-specific, connectivity-phenotypic deviations in 1,202 patients with brain disorders. These data provide a blueprint for elucidating the principle of cortical network reconfiguration and a benchmark for quantifying interindividual network variations.

Generally, epilepsy is considered as abnormally enhanced neuronal excitability and synchronization. So far, previous studies on the synchronization of epileptic brain networks mainly focused on the synchronization strength, but the synchronization stability has not yet been explored as deserved. In this paper, we propose a novel idea to construct a hypergraph brain network (HGBN) based on phase synchronization. Furthermore, we apply the synchronization stability framework of the nonlinear coupled oscillation dynamic model (generalized Kuramoto model) to investigate the HGBNs of epilepsy patients. Specifically, the synchronization stability of the epileptic brain is quantified by calculating the eigenvalue spectrum of the higher-order Laplacian matrix in HGBN. Results show that synchronization stability decreased slightly in the early stages of seizure but increased significantly prior to seizure termination. This indicates that an emergency self-regulation mechanism of the brain may facilitate the termination of seizures. Moreover, the variation in synchronization stability during epileptic seizures may be induced by the topological changes of epileptogenic zones (EZs) in HGBN. Finally, we verify that the higher-order interactions improve the synchronization stability of HGBN. This study proves the validity of the synchronization stability framework with the nonlinear coupled oscillation dynamical model in HGBN, emphasizing the importance of higher-order interactions and the influence of EZs on the termination of epileptic seizures.

BACKGROUND:Convergent dynamic functional connectivity studies have demonstrated their potential as a hallmark for capturing the impairments in brain function associated with Alzheimer's disease (AD) and mild cognitive impairment (MCI). However, our understanding of whole-brain dynamic patterns remains limited, which hampers understanding of cognitive impairment and symptomatology in AD and MCI. METHODS:An energy-landscape analysis was conducted to investigate brain dynamics across 7 large-scale networks in 516 normal control participants (NCs), 404 patients with AD, and 441 participants with MCI from a multicenter cohort. RESULTS:This method identified major brain states and quantified their size, duration, and transitions. In AD and MCI, transitions between these major states were excessively frequent, state durations were abnormal, and brain state sizes were enlarged. Furthermore, direct transitions between major states were significantly negatively correlated with cognitive ability and structural characteristics. CONCLUSIONS:This study has revealed aberrant brain dynamics in large-scale networks among patients compared with NCs, suggesting that patients experience less stable states and more frequent transitions. The brain dynamic-cognition and dynamic-structure associations indicate that the dynamics of brain states could serve as a critical biological endophenotype of AD. These findings provide new insights into understanding and addressing brain network dynamics in AD and MCI.

BACKGROUND:Convergent studies have demonstrated that the topological structure of the brain network undergoes significant alterations in Alzheimer's Disease (AD). However, the underlying mechanisms driving these topological changes remain unclear. Network motifs, as fundamental components of brain networks, provide valuable insights into how disconnections may lead to alterations in the macroscale topological structure. METHODS:This study focuses on undirected triangle motifs within the brain network, identifying 20 unique undirected triangle motifs based on edge strength and length derived from the regional radiomics similarity network (R2SN). To comprehensively capture the distribution of these motifs, we introduce a measure named Principal Motif Value (PMV) of the 20 motifs using principal component analysis (PCA). RESULTS:Our findings reveal significant spatial heterogeneity in PMV across different brain regions. In addition, we identify reproducible alterations of PMV in AD, which were observed through three independent datasets, particularly in the inferior temporal gyrus, middle temporal gyrus, and parahippocampal gyrus. Notably, PMV demonstrates significant correlations with clinical manifestations and neurological features. Finally, we elucidate that alterations in PMV are associated with gene expression related to neuronal systems and synapsis features. CONCLUSION:These findings provide novel insights into the relationship between disconnection and the topological structure of the brain network in AD.

In neuroscience, phase synchronization (PS) is a crucial mechanism that facilitates information processing and transmission between different brain regions. Specifically, global phase synchronization (GPS) characterizes the degree of PS among multivariate neural signals. In recent years, several GPS methods have been proposed. However, they primarily focus on the collective synchronization behavior of multivariate neural signals, while neglecting the structural difference between oscillator networks. Therefore, in this paper, we introduce a method named total correlation-based synchronization (TCS) to quantify GPS intensity by examining network organization. To evaluate the performance of TCS, we conducted simulations using the Rössler model and compared it to three existing methods: circular omega complexity, hyper-torus synchrony, and symbolic phase difference and permutation entropy. The results indicate that TCS outperforms the other methods at distinguishing the GPS intensity between networks with similar structures. And it offers insight into the separation and integration behavior of signals during synchronization. Furthermore, to validate this method with experimental data, TCS was applied to analyze the GPS variation of multichannel stereo-electroencephalography (SEEG) signals recorded from onset zones of patients with temporal lobe epilepsy. It was observed that the termination of seizures was associated with the increased GPS and the integration of brain regions. Taken together, TCS offers an alternative way to measure GPS of multivariate signals, which may shed new lights on the mechanism of brain functions and neurological disorders, such as learning, memory, epilepsy, and Alzheimer's disease.