Non-suicidal self-injury (NSSI) is highly prevalent in adolescents with major depressive disorder (MDD), elevating suicide risk and indicating poor prognosis. While neuroimaging studies have examined NSSI and MDD separately, systematic reviews focusing on major depressive disorder with non-suicidal self-injury (nsMDD) remain scarce. In accordance with PRISMA guidelines, this study systematically reviewed 24 neuroimaging studies (2013 - 2025) involving 2,379 participants (mean age ≤23 years) from PubMed, PsycInfo, and Embase. Using the Newcastle-Ottawa scale for quality assessment, we found 20 functional magnetic resonance imaging (resting-state fMRI and task fMRI) and 4 structural magnetic resonance imaging (MRI) studies. Key findings identified distinct neural signatures specific to nsMDD that differentiate it from depression without NSSI, including: suppression of the frontal gyrus, reduced volume of the putamen, abnormal activation of the lingual gyrus, midline cortical structures, and the prefrontal-limbic-mesencephalic circuit. In terms of neural networks, the default mode network exhibits enhanced connectivity with other neural networks, while the frontoparietal network shows abnormal suppression. Despite limitations including cross-sectional designs, gender imbalance, and scarce multimodal data, these findings provide a basis for targeted intervention that require longitudinal validation of their clinical utility.
Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disorder characterized by progressive motor neuron degeneration. Fused in sarcoma (FUS)-associated juvenile ALS (jALS) represents a distinct and aggressive subgroup with rapid deterioration and poor prognosis. Certain FUS mutations have been associated with comorbid intellectual disability, suggesting neurodevelopmental involvement. We compared FUS-jALS with adult-onset FUS-ALS cases (aALS) to evaluate the association between premorbid cognitive impairment, genetic and clinical features incorporating neuroimaging data. Patients with genetically confirmed FUS-ALS were classified as jALS (onset < 25 years) or aALS (onset ≥ 25 years). Neuropsychological assessment used Mehrfachwahl-Wortschatz-Test (MWT) for verbal IQ, and the Edinburgh Cognitive and Behavioral ALS Screen (ECAS), with cognitive impairment classified according to Strong criteria. Volumetric analysis was conducted on structural MRI and FDG-PET data. All three jALS (P525L [n = 2], H517_Q519del [n = 1]) showed rapid progression with early severe clinical events. Neuropsychological assessment revealed global cognitive deficits (ALS-ci) with widespread dysfunction beyond typical ALS-specific patterns and reduced verbal IQ, pointing towards premorbid cognitive impairment. aALS demonstrated slower progression and were predominantly cognitively unimpaired (ALS-ni) or showed an ALS-specific impairment. Neuroimaging revealed distinct patterns: jALS cases demonstrated posterior cortical atrophy and hypometabolism on FDG-PET, while aALS showed largely preserved brain volumes and limbic-subcortical hypometabolism. Specific FUS mutations (P525L, H517_Q519del) predispose to jALS with severe progression and premorbid cognitive impairments, supporting a genotype-phenotype association. Posterior cortical findings suggest neurodevelopmental delay rather than disease-related neurodegeneration. Genetic FUS screening may be warranted in patients with intellectual disability and motor signs, given emerging targeted therapies.
Depression has been associated with magnetic resonance imaging (MRI) measures of larger white matter hyperintensity (WMH) volumes and smaller cerebral grey matter volumes (GMV) in predominantly White samples. Recent findings suggest that some race/ethnicity groups may experience more severe health consequences due to depression when it is present compared with experience of depression in White samples. We investigated the association of depressive symptoms with WMH and GMV by race/ethnicity, using a continuous measure of depressive symptoms to more expansively capture participants' experience of depression versus clinical diagnosis alone. A diverse sample of older, northern California Kaiser Healthy Aging and Diverse Life Experiences and Study of Healthy Aging in African Americans participants (n = 550) underwent MRI neuroimaging 2017-2022. Baseline depressive symptoms were measured in 2017 using the NIH PROMIS toolbox. We conducted a literature-informed Bayesian analysis, stratified by race/ethnicity, to examine the association between baseline depressive symptoms and post-baseline WMH and GMV. Our sample was 14% Asian, 54% Black, 16% Latino, and 17% White. Overall, we did not observe an association between depressive symptoms and log(WMH) (0.06, 95% Credible Interval [CrI]: -0.09,0.22) or GMV (-0.74, 95% CrI: -1.62,0.16). In stratified analyses, 1 SD higher depressive symptoms were associated with larger log(WMH) volume (0.21, 95%CrI: 0.002,0.42) among Black participants and smaller GMV among Latino participants (-2.92, 95% CrI: -4.31,-1.52). Unexpectedly, depressive symptoms were associated with larger GMV in Asian participants (2.41, 95% CrI: 0.87,3.95). More severe depressive symptoms were associated with MRI markers of brain aging among Black and Latino participants.
Treatment resistant schizophrenia (TRS) is a major challenge in psychiatry, and its management remains an unmet need. Given the relatively high prevalence of resistance to first line antipsychotics (35%) and to clozapine (55%) as a last resort agent, electroconvulsive therapy (ECT) represents an important alternative modality for management of psychosis in TRS and is usually used only after non-response to clozapine, despite evidence suggesting earlier intervention with ECT may be more beneficial in TRS. Here, we discuss recent developments in the use of machine learning models and radiomics to employ neuroimaging markers for predicting ECT treatment response in patients with TRS. The orbitofrontal gyrus, temporal lobe, and limbic structures are regions implicated in the neuropathology of schizophrenia and areas of interest that overlap between these machine learning models. The successful development of these experimental models represents a significant step towards the development of clinical tools that may one day guide treatment decisions for patients identified to have TRS, matching them to effective treatments such as ECT. These experimental models may assist in the development of more personalized treatment protocols, which in turn may decrease duration of untreated psychosis and result in better outcomes for patients with TRS.
Mathematics knowledge is crucial for success in our society. Among the various mathematical areas, rational number knowledge is particularly significant for academic achievement. However, rational number knowledge is challenging, and many children and adults struggle with tasks involving fraction and decimal magnitude processing. In this study, we conducted a systematic review of functional magnetic resonance imaging (fMRI) studies to identify brain regions associated with fractions and decimals. Additionally, we synthesized fraction studies in an activation likelihood estimation (ALE) meta-analysis. A descriptive synthesis of studies showed that fractions and decimals are both associated with activation in the intraparietal sulcus and frontal regions. However, multivariate findings suggest that their neural representation is dissimilar. In addition, the ALE meta-analysis indicated that the IPS, the middle frontal gyrus, the middle temporal gyrus, and the precuneus are associated with fraction processing. These regions have been associated with higher order cognition, including inhibitory control and working memory, and nonsymbolic magnitude processing in previous studies. We discuss current limitations of the field, such as the small number of studies targeting decimal processing, suggesting directions for future studies.
Essential hypertension (EH) remains a leading modifiable risk factor for cardiovascular morbidity and mortality worldwide. Electroacupuncture (EA), combining traditional acupuncture with pulsed electrical stimulation at specific acupoints, has attracted growing attention as a potential adjunctive strategy. A structured narrative literature search was conducted across PubMed, Web of Science, and CNKI databases up to January 2026. Search terms combined electroacupuncture-related terms with hypertension-, mechanism-, clinical trial-, and neuroimaging-related terms. Eligible studies included preclinical hypertension models, selected mechanistic studies from related cardiovascular models, completed clinical studies, published clinical trial protocols, and neuroimaging investigations relevant to EA and blood pressure regulation. Evidence sources were categorized as direct EH-specific evidence, indirect mechanistic evidence, adjunctive clinical evidence, or protocol-based evidence. No formal systematic review methodology was applied; this review should be interpreted as an integrative narrative synthesis. Current evidence suggests that EA may influence blood pressure regulation through multiple preclinically supported mechanisms, including modulation of central sympathetic outflow, autonomic rebalancing, anti-inflammatory signaling, endothelial protection, and attenuation of myocardial remodeling. However, direct human evidence for EA in EH remains sparse. The current clinical literature consists mainly of one stage 1 hypertension study, adjunctive-therapy evidence, and published protocols with pending results, rather than completed, definitive EA-specific randomized trials that establish clinical efficacy. EA remains a biologically plausible but clinically unconfirmed adjunctive approach for EH. Its clinical efficacy, optimal parameters, and target populations require confirmation in completed, adequately powered, sham-controlled multicenter randomized trials.
Post-stroke vascular dementia (VaD) affects 20-30% of ischemic stroke survivors within the first year, yet existing prediction models lack comprehensive integration of novel blood biomarkers and validated risk stratification strategies. This study aimed to develop and temporally validate a risk-stratified prediction model incorporating clinical, neuroimaging, and serum biomarker variables. This retrospective cohort study comprised a development cohort (n = 998, 2020-2022) and temporal validation cohort (n = 249, 2023-2024). Consecutive acute ischemic stroke patients aged ≥ 18 years with available baseline magnetic resonance imaging (MRI) and 12-month follow-up were included. Candidate predictors encompassed 25 variables: demographics, vascular risk factors, stroke severity assessed by the National Institutes of Health Stroke Scale (NIHSS), neuroimaging markers (Fazekas white matter hyperintensity score, brain atrophy index [BAI]), and serum biomarkers including neurofilament light chain (NFL) and glial fibrillary acidic protein (GFAP) measured by single-molecule array (Simoa). The primary outcome was incident VaD diagnosed by NINDS-AIREN criteria at 12 months. Of 1247 included patients, 354 (28.4%) developed VaD. Least absolute shrinkage and selection operator (LASSO) selected 8 predictors: age, education level, NIHSS score, Fazekas score ≥ 2, BAI, previous stroke, plasma NFL, and plasma GFAP. In the development cohort, the model demonstrated excellent discrimination (C-statistic 0.89, 95% CI 0.86-0.92) and good calibration. Bootstrap validation yielded optimism-corrected C-statistic 0.88. In temporal validation, performance remained robust (C-statistic 0.85, 95% CI 0.81-0.89). Risk stratification revealed distinct cognitive trajectories: high-risk patients (25% of cohort) exhibited 67.3% VaD incidence and steep cognitive decline (mean Montreal Cognitive Assessment [MoCA] change -6.8 points), capturing 59.3% of all VaD cases. This biomarker-enhanced prediction model demonstrates excellent discrimination and calibration for post-stroke VaD. Risk stratification effectively identifies high-risk patients for targeted interventions, providing a practical tool for precision-based clinical management.
Cerebral noradrenergic activity modulates physiological functions of behaviour, cognition, movement, arousal and sleep. This review aims to provide an accurate summary of the current knowledge on the involvement of the noradrenergic system in Parkinson's Disease (PD) and its clinical correlations based on neuroimaging studies. Studies in PD highlight neuromelanin MRI signal loss in the locus coeruleus (LC), and positron emission tomography shows noradrenergic denervation across subcortical and cortical areas. More severe phenotypes of PD, manifesting with cognitive decline, apathy, REM sleep behaviour disorder and autonomic dysfunction, are associated with more severe noradrenergic dysfunction. Conversely more preserved noradrenergic transmission is common in tremulous PD. Furthermore, noradrenergic dysfunction, is also involved in transient motor manifestations such as tremor and freezing of gait. Recent neuroimaging advances greatly expanded the knowledge about noradrenergic dysfunction pathophysiology in PD. However, pharmacological treatment of its several associated manifestations is still lacking and needs further investigation.
Nonprimary maternal cytomegalovirus (CMV) infections, resulting from reactivation or reinfection in seropositive women, are increasingly recognized as contributors to congenital CMV (cCMV) disease. However, their clinical impact compared with primary infections remains insufficiently characterized. We conducted a retrospective cohort study of symptomatic cCMV identified from a single-center series (2005-2022) and a multicenter registry (2023-2025). Infants were included if they had a positive urine CMV polymerase chain reaction within the first 3 weeks of life and met criteria for symptomatic infection based on central nervous system involvement. Maternal infection type (primary vs. nonprimary) was determined by serologic testing. Clinical characteristics, neuroimaging findings and auditory outcomes were compared between groups. We identified 360 symptomatic infants; maternal infection type was unknown in 55, leaving 305 for analysis [243 (79.7%) primary; 62 (20.3%) nonprimary]. The proportion of nonprimary infections among symptomatic cases increased from 11.4% in 2005-2009 to 37.3% in 2020-2025 (odds ratio: 4.77; 95% confidence interval: 1.63-13.92; P = 0.004). Absolute counts mirrored these trends: nonprimary 5 of 44 in 2005-2009 to 22 of 59 in 2020-2025. Clinical manifestations, including neuroimaging and hearing outcomes, were broadly similar between groups. In the era of maternal screening and interventions focused on primary CMV infection, nonprimary infections represent a rising share of symptomatic cCMV and a relevant clinical burden. These findings highlight the importance of recognizing nonprimary infections in clinical care and public health planning and support the need to reassess current strategies for maternal counseling and neonatal care.
To investigate volumetric alterations of the basal ganglia and thalamus in patients with Parkinson's disease (PD) and to evaluate the relationships between regional brain volumes, motor symptom severity, and cognitive performance. 60 patients with PD (20 females, 40 males) and 51 age-matched healthy control participants (26 females, 25 males) were included. Patients with PD were classified according to initial motor phenotype (tremor-dominant vs. bradykinetic-rigid) and cognitive status based on Mini-Mental State Examination (MMSE) scores (21-26 indicating cognitive impairment; ≥27 indicating normal cognition). High-resolution three-dimensional T1-weighted SPGR MRI data were analyzed using the VolBrain automated segmentation pipeline to obtain volumetric measurements of the putamen, caudate nucleus, globus pallidus, and thalamus. All volumetric measures were normalized to total intracranial volume (TIV). Associations between regional brain volumes and Unified Parkinson's Disease Rating Scale (UPDRS) sub-scores were assessed. The mean age of the PD group was 61.2 ± 11.2 years, compared with 60.4 ± 12.0 years in the control group. Right (p = 0.001), left (p = 0.002), and total (p = 0.004) putamen volumes were significantly reduced in patients with PD relative to controls. No significant differences were observed in thalamic volumes between groups (p > 0.05). Within the PD cohort, individuals with MMSE-defined cognitive impairment exhibited significantly smaller thalamic volumes than cognitively normal patients (right: p = 0.002; left: p = 0.001; total: p = 0.001). UPDRS-III scores showed significant negative correlations with putamen volumes (right: r = -0.42, p = 0.001; left: r = -0.43, p = 0.001; total: r = -0.42, p = 0.001), while UPDRS-I scores were negatively correlated with thalamic volumes (right: r = -0.31, p = 0.016; left: r = -0.29, p = 0.041; total: r = -0.30, p = 0.015). Putamen volume reduction is associated with motor dysfunction in PD, whereas thalamic volumetric changes are more closely related to cognitive impairment. TIV-normalized automated MRI volumetry may provide complementary neuroimaging biomarkers for assessing disease severity and monitoring clinical progression. · Basal ganglia and thalamic volumes show distinct patterns in Parkinson's disease.. · Putamen volume reduction is associated with motor symptom severity.. · Thalamic volume changes are associated with cognitive impairment in PD.. · TIV-normalized VolBrain-based volumetric MRI analysis may support clinical disease assessment.. · Yıldız H, Atalay B, özdilek b. Basal Ganglia and Thalamic Volumes as MRI Markers of Motor and Cognitive Dysfunction in Parkinson's Disease. Rofo 2026; DOI 10.1055/a-2871-1475. Untersuchung volumetrischer Veränderungen der Basalganglien und des Thalamus bei Patienten mit Morbus Parkinson sowie Bewertung der Zusammenhänge zwischen regionalen Hirnvolumina, dem Schweregrad der motorischen Symptome und der kognitiven Leistungsfähigkeit.Es wurden 60 Patienten mit Parkinson (20 Frauen, 40 Männer) und 51 altersgleiche gesunde Kontrollteilnehmer (26 Frauen, 25 Männer) in die Studie aufgenommen. Die Parkinson-Patienten wurden nach ihrem initialen motorischen Phänotyp (tremordominant vs. bradykinetisch-rigid) und ihrem kognitiven Status basierend auf den Ergebnissen der Mini- Mental State Examination (MMSE) (21–26 Punkte: kognitive Beeinträchtigung; ≥ 27 Punkte: normale kognitive Leistung) klassifiziert. Hochauflösende dreidimensionale T1-gewichtete SPGR-MRT-Daten wurden mithilfe der automatisierten Segmentierungspipeline VolBrain analysiert, um volumetrische Messungen des Putamens, des Nucleus caudatus, des Globus pallidus und des Thalamus zu erhalten. Alle volumetrischen Messwerte wurden auf das gesamte intrakranielle Volumen (TIV) normalisiert. Es wurden Korrelationen zwischen den regionalen Hirnvolumina und den Subscores der Unified Parkinson’s Disease Rating Scale (UPDRS) untersucht.Das Durchschnittsalter der Gruppe mit Parkinson betrug 61,2 ± 11,2 Jahre, im Vergleich zu 60,4 ± 12,0 Jahre in der Kontrollgruppe. Das Volumina des rechten (p=0,001), linken (p=0,002) und des gesamten (p=0,004) Putamens war bei Parkinson-Patienten im Vergleich zur Kontrollgruppe signifikant reduziert. Es wurden keine signifikanten Unterschiede im Thalamusvolumen zwischen den Gruppen beobachtet (p>0,05). Innerhalb der Parkinson-Kohorte wiesen Personen mit einer gemäß MMSE definierten kognitiven Beeinträchtigung signifikant geringere Thalamusvolumina auf als kognitiv unbeeinträchtigte Patienten (rechts: p=0,002; links: p=0,001; insgesamt: p=0,001). Die UPDRS-III-Scores zeigten signifikante negative Korrelationen mit dem Putamenvolumen (rechts: r=–0,42; p=0,001; links: r=–0,43; p=0,001; gesamt: r=–0,42; p=0,001), während die UPDRS-I-Scores negativ mit dem Thalamusvolumen korrelierten (rechts: r=–0,31; p=0,016; links: r=–0,29; p=0,041; gesamt: r=–0,30; p=0,015).Eine Verringerung des Putamenvolumens ist bei der Parkinson-Krankheit mit motorischen Funktionsstörungen assoziiert, während thalamische Volumenänderungen enger mit kognitiven Beeinträchtigungen zusammenhängen. Die auf das TIV-normalisierte automatisierte MRT-Volumetrie könnte ergänzende Neuroimaging-Biomarker zur Beurteilung des Schweregrads der Erkrankung und zur Überwachung des klinischen Verlaufs bereitstellen. · Die Volumina der Basalganglien und des Thalamus zeigen bei Morbus Parkinson unterschiedliche Muster.. · Eine Verringerung des Putamenvolumens ist dem Schweregrad der motorischen Symptome assoziiert.. · Thalamische Volumenveränderungen sind mit kognitiven Beeinträchtigungen assoziiert.. · Eine TIV-normalisierte, auf VolBrain basierende volumetrische MRT-Analyse kann die klinische Beurteilung der Erkrankung unterstützen..
Ultra-low-field (ULF) MRI currently lacks population-representative spatial priors, limiting robust alignment, normalization, and cross-study comparability. Our aim is to provide an open, standardized ULF brain template resource, comprising data and code, that enables reproducible spatial analysis and method development across ULF studies. We present group-average brain templates generated from 64 mT MRI scans of 100 healthy adults, encompassing both T1- and T2-weighted contrasts and covering the full adult lifespan. Participants were stratified into three age groups to support both age-specific and population-level analyses. The preprocessing and registration pipeline employed established open-source neuroimaging tools and iterative averaging to enhance anatomical correspondence and consistency. Data were acquired across multiple scanner software versions, and potential intensity variability was evaluated and mitigated through standardized preprocessing. The resource includes population and age-specific templates with accompanying example segmentations and complete scripts for full reproducibility. Derived template data and code are openly available on Zenodo and GitHub in accordance with FAIR principles. This resource provides ULF-specific spatial priors to support normalization, registration benchmarking, and method development within comparable acquisition settings.
Diffusion tensor image analysis along the perivascular space (DTI-ALPS) was originally developed and defined as a noninvasive diffusion MRI method intended to evaluate glymphatic function and is now widely used in neuroimaging research. However, accumulating evidence suggests that the biological meaning of the ALPS index is more complex than initially assumed as a simple marker of glymphatic function. The ALPS index does not directly measure whole-brain fluid transport but rather reflects localized directional diffusivity based on the Brownian motion of water molecules. Its value is strongly influenced by white matter microstructure, including fiber orientation, crossing fibers, extracellular geometry, and age-related diffusivity changes. In addition, because the ALPS index is a ratio-based measure derived from directional diffusivity components, its alterations may arise from different combinations of numerator and denominator diffusivity changes, indicating that similar ALPS index reductions may reflect distinct underlying microstructural mechanisms. White matter microstructure may represent not merely a confounding factor, but a structural substrate guiding interstitial fluid transport itself. White matter hydraulic permeability exhibits strong anisotropy, and brain fluid transport may occur preferentially along white matter tracts. Thus, the structure dependence of the ALPS index may reflect physiologically relevant interactions between white matter architecture and interstitial fluid dynamics rather than simple measurement bias. In this review, we propose redefining the ALPS index not as a direct marker of glymphatic function, but as a "spatially fixed-point biomarker" evaluating directional diffusivity within anatomically defined regions. Within this framework, the ALPS index can be understood as a composite biomarker reflecting interactions among white matter microstructure, extracellular environment, vascular geometry, and neurofluid-related tissue environment. Because the ALPS index has been associated with aging, sleep, and various diseases, it may function less as a disease-specific marker and more as an adjunctive imaging marker relevant to brain health assessment and the brain tissue environment.
Alzheimer's disease (AD) is incurable and increasingly attributed to gene-environment interactions. Microplastics (MPs) are omnipresent in the human food chain, yet their impact on neurodegeneration is largely unknown. Here we show that chronic oral exposure to 2-µm amine-modified polystyrene microparticles accelerates cognitive decline, amplifies Aβ deposition, gliosis, and synaptic loss, and cripples autophagic flux in 5XFAD mice through the gut-brain axis. MPs accumulate in the gut, breach the epithelial barrier, and selectively expand the taurine-depleting pathobiont Bilophila, while suppressing taurine-synthesizing commensals. Untargeted metabolomics reveal a systemic taurine deficit that precedes and predicts exacerbated Aβ deposition, gliosis, synaptic loss, and autophagic blockade in 5XFAD mice. Antibiotic-mediated microbiota ablation and fecal microbiota transplantation (FMT) demonstrate that the neurotoxic phenotype is fully microbiota-dependent. Restoring taurine level rebalances microglial homeostasis, reinstates autophagic flux, and rescues memory deficits in MPs-treated 5XFAD mice. Translational validation using Alzheimer's Disease Neuroimaging Initiative (ADNI) plasma shows taurine is significantly lower in AD patients versus cognitively normal controls and inversely correlates with cognitive decline. Our findings identify MPs-induced gut-microbiota dysbiosis as a modifiable environmental driver of AD pathogenesis and establish taurine supplementation as a readily translatable intervention that simultaneously fortifies the intestinal barrier and neutralizes microbiota-mediated neurodegeneration.
This guideline summarizes evidence-based post-cardiac arrest care following the return of spontaneous circulation (ROSC) in adults, incorporating updates from the 2025 Korean Guidelines for Cardiopulmonary Resuscitation and contemporary international evidence. Recommendations were informed by recent randomized controlled trials and systematic reviews, with an emphasis on patient-centered outcomes and practical clinical applications. After ROSC, early evaluation should focus on identifying reversible causes. A 12-lead electrocardiogram should be obtained promptly, with echocardiography and whole-body computed tomography performed when clinically indicated to assess cardiac function and detect noncardiac or occult etiologies. Respiratory management aims to minimize secondary brain injury by preventing hypoxemia and hyperoxemia. High inspired oxygen concentrations may be used initially, followed by titration to an appropriate oxygen saturation level once reliable measurements are available, and ventilation should target normocapnia. Hemodynamic management prioritizes adequate organ perfusion and prompt treatment of shock, including active correction of hypotension. Routine immediate coronary angiography is not recommended in patients without ST-segment elevation. However, urgent angiography is indicated in those with ST-segment elevation, cardiogenic shock, or a high likelihood of ongoing myocardial ischemia. In comatose survivors, temperature control is essential. The selected target temperature should be maintained for at least 24 hours, with active fever prevention for 36 to 72 hours. Additional intensive care unit management includes glucose control and seizure monitoring. Routine prophylactic antibiotics or anticonvulsants are not recommended. Neuroprognostication should use a multimodal approach after confounders, such as sedation and temperature management, are addressed, integrating clinical examination, electrophysiology, biomarkers, and neuroimaging to support individualized decision-making.
The brain and body undergo coordinated changes throughout the life span, yet studies of aging have traditionally examined these systems as separate entities. Here we ask how brain health relates to aging and peripheral biomarkers of metabolic and vascular function, including body mass index, blood pressure, and blood biochemistry. We use multivariate pattern learning to identify generalizable patterns of covariance between multi-modal neuroimaging data (structural, functional, diffusion, and arterial spin labeling MRI), demographic, and physiological markers in two large-scale deeply phenotyped datasets: the Human Connectome Project-Aging and UK Biobank. This data-driven approach isolates two principal axes of brain-body associations in both biological sexes. The first axis is driven by the dominant contribution of age. Across multiple brain measures, aging is associated with loss of brain structural integrity and cerebral vascular dysfunction. The second axis is driven by metabolic features, characterized by low high-density lipoprotein cholesterol, elevated body mass index, blood pressure, glycosylated hemoglobin, insulin, glucose, and alanine aminotransferase that predominantly converge on reduced cerebral perfusion. Importantly, the aging and the metabolic axes are independent of each other, meaning that age and metabolic dysfunction have separable influences on the brain. Finally, we show that deviations from a healthy metabolic profile are linked to cognitive deficits, particularly in females. Our study contributes to development of comprehensive translatable biomarkers for brain health assessment, and highlights the importance of metabolic health as a determinant of brain health in aging population.
Autism is a lifelong neurodevelopmental condition, yet aging trajectories in autistic adults remain poorly understood. We conducted a systematic review of studies examining age-related cognitive, neural, and physical health outcomes in autistic adults to clarify emerging patterns and identify priority areas for future research. A systematic search of PubMed, PsycINFO, and Web of Science identified 56 eligible studies that included autistic adults and provided either longitudinal data or explicit age-by-diagnosis comparisons. Findings were synthesized separately by domain. Current evidence does not support globally accelerated or attenuated aging in autism. Most objective cognitive measures showed comparable age-related patterns across autistic and non-autistic adults, although subjective cognitive complaints were consistently elevated. Neuroimaging findings were similarly mixed. Most studies reported no structural or functional differences, though some found evidence of vulnerability in white matter microstructure. Physical health studies more consistently identified elevated rates of neurodegenerative diseases, sensory impairments, and musculoskeletal conditions, but comparable or lower rates for cancer. Cardiovascular findings were complex, with lower hypertension rates but higher rates of severe outcomes such as heart failure and stroke, suggesting gaps in early detection and preventive care. Rather than generalized accelerated aging, findings point toward targeted vulnerabilities in autism. Subjective cognitive complaints, white matter integrity, and elevated neurodegeneration rates represent the clearest targets for future surveillance. Critically, existing research almost exclusively involves autistic adults without intellectual disability, limiting generalizability. Advancing the field requires longitudinal designs, more inclusive samples, and a shift from group-level comparisons toward understanding within-group heterogeneity and individual risk profiles.
Accurately predicting the progression of neurodegenerative diseases and identifying key brain regions influencing the disease course are critical research topics in neuroimaging. Self-Attention Graph Pooling (SAGPooling) can be used to enhance prediction efficiency and accuracy. The atlas-constrained spatial-informational SAGPooling graph neural network (ACSI-SGNN) model is proposed to enable precise prediction of patients' cognitive resilience and identification of brain regions closely associated with resilience. Considering the complex structure and functionality of brain networks, frequency-domain Granger causality is first utilized to calculate the functional states representing neuronal activity within specific frequency bands of raw fMRI signals and the information flow pathways between brain regions. These were integrated with spatial encoding based on the shortest path to construct a brain network model. The resulting brain network is used for feature aggregation in SGNN, leveraging the ROI selection pooling layer in the model to highlight brain regions. For the cognitive resilience three-class classification task, the proposed ACSI-SGNN model achieved an ACC of 87.50%, an F1 score of 87.68%, a precision of 89.58%, and a recall of 88.33% on the ADNI dataset and an ACC of 88.24%, an F1 score of 86.71%, a precision of 89.87%, and a recall of 83.33% on the HABS dataset, respectively. Results indicate the effectiveness of the proposed method in identifying brain aging rates and neurodegenerative disease progression. Our approach outperformed other methods across three evaluation metrics and identified key brain regions highly consistent with those previously reported as critical to cognitive resilience in neuroimaging studies.
Endocasts are casts of the internal surface of skull bones created by the brain and surrounding tissues. These fossil phantoms provide the most direct evidence of the brains of extinct organisms, and are one of the many puzzle pieces that help paleoneurologists reconstruct brain evolution. The recent discovery of a small-brained, recently-extinct human species, Homo naledi, creates a timely opportunity to review what endocasts can and cannot tell us about ancient brains. We first review published evidence about the brain and behavior of H. naledi, including the suggestion that the species may have practiced mortuary behaviors over 230,000 years ago. We next use geometric morphometric methods to reconstruct and the most complete H. naledi endocast and compare it to those of modern humans and Pleistocene hominins. Our results corroborate previous evidence that the brain of H. naledi presents a unique combination of ancestral and modern human-like characteristics, specifically displaying a derived frontal lobe while retaining ancestral sizes, morphology, and cerebro-cerebellar proportions. Finally, we discuss these results in the context of recent paleoanthropological data, advances in neuroimaging, and theoretical frameworks linking brain morphology, structure, and function.