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BACKGROUND: Contemporary surgical practices for traumatic brain injury (TBI) remain unclear. We describe the clinical profile of an 18-centre US TBI cohort with cranial surgery. METHODS: The prospective, observational Transforming Research and Clinical Knowledge in Traumatic Brain Injury Study (2014-2018; ClinicalTrials.gov #NCT02119182) enrolled subjects who presented to trauma centre and received head computed tomography within 24-h (h) post-TBI. We performed a secondary data analysis in subjects aged ≥17-years with hospitalisation. Clinical characteristics, surgery type/timing, hospital and six-month outcomes were reported. FINDINGS: Of 2032 subjects (age: mean = 41.4-years, range = 17-89-years; male = 71% female = 29%), 260 underwent cranial surgery, comprising 65% decompressive craniectomy, 23% craniotomy, 12% other surgery. Subjects with surgery (vs. without surgery) presented with worse neurological injury (median Glasgow Coma Scale = 6 vs. 15; midline shift ≥5 mm: 48% vs. 2%; cisternal effacement: 61% vs. 4%; p

Inquiries into properties of brain structure and function have progressed due to developments in magnetic resonance imaging (MRI). To sustain progress in investigating and quantifying neuroanatomical details in vivo, the reliability and validity of brain measurements are paramount. Quality control (QC) is a set of procedures for mitigating errors and ensuring the validity and reliability of brain measurements. Despite its importance, there is little guidance on best QC practices and reporting procedures. The study of hippocampal subfields in vivo is a critical case for QC because of their small size, inter-dependent boundary definitions, and common artifacts in the MRI data used for subfield measurements. We addressed this gap by surveying the broader scientific community studying hippocampal subfields on their views and approaches to QC. We received responses from 37 investigators spanning 10 countries, covering different career stages, and studying both healthy and pathological development and aging. In this sample, 81% of researchers considered QC to be very important or important, and 19% viewed it as fairly important. Despite this, only 46% of researchers reported on their QC processes in prior publications. In many instances, lack of reporting appeared due to ambiguous guidance on relevant details and guidance for reporting, rather than absence of QC. Here, we provide recommendations for correcting errors to maximize reliability and minimize bias. We also summarize threats to segmentation accuracy, review common QC methods, and make recommendations for best practices and reporting in publications. Implementing the recommended QC practices will collectively improve inferences to the larger population, as well as have implications for clinical practice and public health.

IntroductionCardiovascular health is important for brain aging, yet its role in the clinical manifestation of autosomal dominant or atypical forms of dementia has not been fully elucidated. We examined relationships between Life's Simple 7 (LS7) and clinical trajectories in individuals with autosomal dominant frontotemporal lobar degeneration (FTLD).MethodsTwo hundred forty-seven adults carrying FTLD pathogenic genetic variants (53% asymptomatic) and 189 non-carrier controls completed baseline LS7, and longitudinal neuroimaging and neuropsychological testing.ResultsAmong variant carriers, higher baseline LS7 is associated with slower accumulation of frontal white matter hyperintensities (WMHs), as well as slower memory and language declines. Higher baseline LS7 associated with larger baseline frontotemporal volume, but not frontotemporal volume trajectories.DiscussionBetter baseline cardiovascular health related to slower cognitive decline and accumulation of frontal WMHs in autosomal dominant FTLD. Optimizing cardiovascular health may be an important modifiable approach to bolster cognitive health and brain integrity in FTLD.HighlightsBetter cardiovascular health associates with slower cognitive decline in frontotemporal lobar degeneration (FTLD). Lifestyle relates to the accumulation of frontal white matter hyperintensities in FTLD. More optimal cardiovascular health associates with greater baseline frontotemporal lobe volume. Optimized cardiovascular health relates to more favorable outcomes in genetic dementia.

The magnetic resonance imaging (MRI) Core has been operating since Alzheimer's Disease Neuroimaging Initiative's (ADNI) inception, providing 20 years of data including reliable, multi-platform standardized protocols, carefully curated image data, and quantitative measures provided by expert investigators. The overarching purposes of the MRI Core include: (1) optimizing and standardizing MRI acquisition methods, which have been adopted by many multicenter studies and trials worldwide and (2) providing curated images and numeric summary values from relevant MRI sequences/contrasts to the scientific community. Over time, ADNI MRI has become increasingly complex. To remain technically current, the ADNI MRI protocol has changed substantially over the past two decades. The ADNI 4 protocol contains nine different imaging types (e.g., three dimensional [3D] T1-weighted and fluid-attenuated inversion recovery [FLAIR]). Our view is that the ADNI MRI data are a greatly underutilized resource. The purpose of this paper is to educate the scientific community on ADNI MRI methods and content to promote greater awareness, accessibility, and use. HIGHLIGHTS: The MRI Core provides multi-platform standardized protocols, carefully curated image data, and quantitative analysis by expert groups. The ADNI MRI protocol has undergone major changes over the past two decades to remain technically current. As of April 25, 2024, the following numbers of image series are available: 17,141 3D T1w; 6877 FLAIR; 3140 T2/PD; 6623 GRE; 3237 dMRI; 2846 ASL; 2968 TF-fMRI; and 2861 HighResHippo (see Table 1 for abbreviations). As of April 25, 2024, the following numbers of quantitative analyses are available: FreeSurfer 10,997; BSI 6120; tensor based morphometry (TBM) and TBM-SYN 12,019; WMH 9944; dMRI 1913; ASL 925; TF-fMRI NFQ 2992; and medial temporal subregion volumes 2726 (see Table 4 for abbreviations). ADNI MRI is an underutilized resource that could be more useful to the research community.

The Alzheimers Disease Neuroimaging Initiative (ADNI) PET Core has evolved over time, beginning with positron emission tomography (PET) imaging of a subsample of participants with [18F]fluorodeoxyglucose (FDG)-PET, adding tracers for measurement of β-amyloid, followed by tau tracers. This review examines the evolution of the ADNI PET Core, the novel aspects of PET imaging in each stage of ADNI, and gives an accounting of PET images available in the ADNI database. The ADNI PET Core has been and continues to be a rich resource that provides quantitative PET data and preprocessed PET images to the scientific community, allowing interrogation of both basic and clinically relevant questions. By standardizing methods across different PET scanners and multiple PET tracers, the Core has demonstrated the feasibility of large-scale, multi-center PET studies. Data managed and disseminated by the PET Core has been critical to defining pathophysiological models of Alzheimers disease (AD) and helped to drive methods used in modern therapeutic trials. HIGHLIGHTS: The ADNI PET Core began with FDG-PET and now includes three amyloid and three tau PET ligands. The PET Core has standardized acquisition and analysis of multitracer PET images. The ADNI PET Core helped to develop methods that have facilitated clinical trials in AD.

INTRODUCTION: We evaluated preliminary feasibility of a digital, culturally-informed approach to recruit and screen participants for the Alzheimers Disease Neuroimaging Initiative (ADNI4). METHODS: Participants were recruited using digital advertising and completed digital surveys (e.g., demographics, medical exclusion criteria, 12-item Everyday Cognition Scale [ECog-12]), Novoic Storyteller speech-based cognitive test). Completion rates and assessment performance were compared between underrepresented populations (URPs: individuals from ethnoculturally minoritized or low education backgrounds) and non-URPs. RESULTS: Of 3099 participants who provided contact information, 654 enrolled in the cohort, and 595 completed at least one assessment. Two hundred forty-seven participants were from URPs. Of those enrolled, 465 met ADNI4 inclusion criteria and 237 evidenced possible cognitive impairment from ECog-12 or Storyteller performance. URPs had lower ECog and Storyteller completion rates. Scores varied by ethnocultural group and educational level. DISCUSSION: Preliminary results demonstrate digital recruitment and screening assessment of an older diverse cohort, including those with possible cognitive impairment, are feasible. Improving engagement and achieving educational diversity are key challenges. HIGHLIGHTS: A total of 654 participants enrolled in a digital cohort to facilitate ADNI4 recruitment. Culturally-informed digital ads aided enrollment of underrepresented populations. From those enrolled, 42% were from underrepresented ethnocultural and educational groups. Digital screening tools indicate > 50% of participants likely cognitively impaired. Completion rates and assessment performance vary by ethnocultural group and education.

INTRODUCTION: Speech-based testing shows promise for sensitive and scalable objective screening for Alzheimers disease (AD), but research to date offers limited evidence of generalizability. METHODS: Data were taken from the AMYPRED (Amyloid Prediction in Early Stage Alzheimers Disease from Acoustic and Linguistic Patterns of Speech) studies (N = 101, N = 46 mild cognitive impairment [MCI]) and Alzheimers Disease Neuroimaging Initiative 4 (ADNI4) remote digital (N = 426, N = 58 self-reported MCI, mild AD or dementia) and in-clinic (N = 57, N = 13 MCI) cohorts, in which participants provided audio-recorded responses to automated remote story recall tasks in the Storyteller test battery. Text similarity, lexical, temporal, and acoustic speech feature sets were extracted. Models predicting early AD were developed in AMYPRED and tested out of sample in the demographically more diverse cohorts in ADNI4 (> 33% from historically underrepresented populations). RESULTS: Speech models generalized well to unseen data in ADNI4 remote and in-clinic cohorts. The best-performing models evaluated text-based metrics (text similarity, lexical features: area under the curve 0.71-0.84 across cohorts). DISCUSSION: Speech-based predictions of early AD from Storyteller generalize across diverse samples. HIGHLIGHTS: The Storyteller speech-based test is an objective digital prescreener for Alzheimers Disease Neuroimaging Initiative 4 (ADNI4). Speech-based models predictive of Alzheimers disease (AD) were developed in the AMYPRED (Amyloid Prediction in Early Stage Alzheimers Disease from Acoustic and Linguistic Patterns of Speech) sample (N = 101). Models were tested out of sample in ADNI4 in-clinic (N = 57) and remote (N = 426) cohorts. Models showed good generalization out of sample. Models evaluating text matching and lexical features were most predictive of early AD.

Formal thought disorder (FTD) is a clinical key factor in schizophrenia, but the neurobiological underpinnings remain unclear. In particular, the relationship between FTD symptom dimensions and patterns of regional brain volume loss in schizophrenia remains to be established in large cohorts. Even less is known about the cellular basis of FTD. Our study addresses these major obstacles by enrolling a large multi-site cohort acquired by the ENIGMA Schizophrenia Working Group (752 schizophrenia patients and 1256 controls), to unravel the neuroanatomy of FTD in schizophrenia and using virtual histology tools on implicated brain regions to investigate the cellular basis. Based on the findings of previous clinical and neuroimaging studies, we decided to separately explore positive, negative and total formal thought disorder. We used virtual histology tools to relate brain structural changes associated with FTD to cellular distributions in cortical regions. We identified distinct neural networks positive and negative FTD. Both networks encompassed fronto-occipito-amygdalar brain regions, but positive and negative FTD demonstrated a dissociation: negative FTD showed a relative sparing of orbitofrontal cortical thickness, while positive FTD also affected lateral temporal cortices. Virtual histology identified distinct transcriptomic fingerprints associated for both symptom dimensions. Negative FTD was linked to neuronal and astrocyte fingerprints, while positive FTD also showed associations with microglial cell types. These results provide an important step towards linking FTD to brain structural changes and their cellular underpinnings, providing an avenue for a better mechanistic understanding of this syndrome.

Phase four of the Alzheimers Disease Neuroimaging Initiative (ADNI4) magnetic resonance imaging (MRI) protocols aim to maintain longitudinal consistency across two decades of data acquisition, while adopting new technologies. Here we describe and justify the studys design and targeted biomarkers. The ADNI4 MRI protocol includes nine MRI sequences. Some sequences require the latest hardware and software system upgrades and are continuously rolled out as they become available at each site. The main sequence additions/changes in ADNI4 are: (1) compressed sensing (CS) T1-weighting, (2) pseudo-continuous arterial spin labeling (ASL) on all three vendors (GE, Siemens, Philips), (3) multiple-post-labeling-delay ASL, (4) 1 mm3 isotropic 3D fluid-attenuated inversion recovery, and (5) CS 3D T2-weighted. ADNI4 aims to help the neuroimaging community extract valuable imaging biomarkers and provide a database to test the impact of advanced imaging strategies on diagnostic accuracy and disease sensitivity among individuals lying on the cognitively normal to impaired spectrum. HIGHLIGHTS: A summary of MRI protocols for phase four of the Alzheimers Disease Neuroimaging Initiative (ADNI 4). The design and justification for the ADNI 4 MRI protocols. Compressed sensing and multi-band advances have been applied to improve scan time. ADNI4 protocols aim to streamline safety screening and therapy monitoring. The ADNI4 database will be a valuable test bed for academic research.

IntroductionAlzheimer's disease (AD) pathology is defined by β-amyloid (Aβ) plaques and neurofibrillary tau, but Lewy bodies (LBs; ?-synuclein aggregates) are a common co-pathology for which effective biomarkers are needed.MethodsA validated α-synuclein Seed Amplification Assay (SAA) was used on recent cerebrospinal fluid (CSF) samples from 1638 Alzheimer's Disease Neuroimaging Initiative (ADNI) participants, 78 with LB-pathology confirmation at autopsy. We compared SAA outcomes with neuropathology, Aβ and tau biomarkers, risk-factors, genetics, and cognitive trajectories.ResultsSAA showed 79% sensitivity and 97% specificity for LB pathology, with superior performance in identifying neocortical (100%) compared to limbic (57%) and amygdala-predominant (60%) LB-pathology. SAA+ rate was 22%, increasing with disease stage and age. Higher Aβ burden but lower CSF p-tau181 associated with higher SAA+ rates, especially in dementia. SAA+ affected cognitive impairment in MCI and Early-AD who were already AD biomarker positive.DiscussionSAA is a sensitive, specific marker for LB-pathology. Its increase in prevalence with age and AD stages, and its association with AD biomarkers, highlights the clinical importance of α-synuclein co-pathology in understanding AD's nature and progression.HighlightsSAA shows 79% sensitivity, 97% specificity for LB-pathology detection in AD. SAA positivity prevalence increases with disease stage and age. Higher Aβ burden, lower CSF p-tau181 linked with higher SAA+ rates in dementia. SAA+ impacts cognitive impairment in early disease stages. Study underpins need for wider LB-pathology screening in AD treatment.

The human brain undergoes rapid development during the first years of life. Beginning in utero, a wide array of biological, social, and environmental factors can have lasting impacts on brain structure and function. To understand how prenatal and early life experiences alter neurodevelopmental trajectories and shape health outcomes, several NIH Institutes, Centers, and Offices collaborated to support and launch the HEALthy Brain and Child Development (HBCD) Study. The HBCD Study is a multi-site prospective longitudinal cohort study, that will examine human brain, cognitive, behavioral, social, and emotional development beginning prenatally and planned through early childhood. Influenced by the success of the ongoing Adolescent Brain Cognitive DevelopmentSM Study (ABCD Study®) and in partnership with the NIH Helping to End Addiction Long-term® Initiative, or NIH HEAL Initiative®, the HBCD Study aims to establish a diverse cohort of over 7000 pregnant participants to understand how early life experiences, including prenatal exposure to addictive substances and adverse social environments as well as their interactions with an individuals genes, can affect neurodevelopmental trajectories and outcomes. Knowledge gained from the HBCD Study will help identify targets for early interventions and inform policies that promote resilience and mitigate the neurodevelopmental effects of adverse childhood experiences and environments.

Machine learning can be used to define subtypes of psychiatric conditions based on shared biological foundations of mental disorders. Here we analyzed cross-sectional brain images from 4,222 individuals with schizophrenia and 7038 healthy subjects pooled across 41 international cohorts from the ENIGMA, non-ENIGMA cohorts and public datasets. Using the Subtype and Stage Inference (SuStaIn) algorithm, we identify two distinct neurostructural subgroups by mapping the spatial and temporal trajectory of gray matter change in schizophrenia. Subgroup 1 was characterized by an early cortical-predominant loss with enlarged striatum, whereas subgroup 2 displayed an early subcortical-predominant loss in the hippocampus, striatum and other subcortical regions. We confirmed the reproducibility of the two neurostructural subtypes across various sample sites, including Europe, North America and East Asia. This imaging-based taxonomy holds the potential to identify individuals with shared neurobiological attributes, thereby suggesting the viability of redefining existing disorder constructs based on biological factors.

Structural neuroimaging data have been used to compute an estimate of the biological age of the brain (brain-age) which has been associated with other biologically and behaviorally meaningful measures of brain development and aging. The ongoing research interest in brain-age has highlighted the need for robust and publicly available brain-age models pre-trained on data from large samples of healthy individuals. To address this need we have previously released a developmental brain-age model. Here we expand this work to develop, empirically validate, and disseminate a pre-trained brain-age model to cover most of the human lifespan. To achieve this, we selected the best-performing model after systematically examining the impact of seven site harmonization strategies, age range, and sample size on brain-age prediction in a discovery sample of brain morphometric measures from 35,683 healthy individuals (age range: 5-90 years; 53.59% female). The pre-trained models were tested for cross-dataset generalizability in an independent sample comprising 2101 healthy individuals (age range: 8-80 years; 55.35% female) and for longitudinal consistency in a further sample comprising 377 healthy individuals (age range: 9-25 years; 49.87% female). This empirical examination yielded the following findings: (1) the accuracy of age prediction from morphometry data was higher when no site harmonization was applied; (2) dividing the discovery sample into two age-bins (5-40 and 40-90 years) provided a better balance between model accuracy and explained age variance than other alternatives; (3) model accuracy for brain-age prediction plateaued at a sample size exceeding 1600 participants. These findings have been incorporated into CentileBrain (https://centilebrain.org/#/brainAGE2), an open-science, web-based platform for individualized neuroimaging metrics.

Machine learning approaches using structural magnetic resonance imaging (sMRI) can be informative for disease classification, although their ability to predict psychosis is largely unknown. We created a model with individuals at CHR who developed psychosis later (CHR-PS+) from healthy controls (HCs) that can differentiate each other. We also evaluated whether we could distinguish CHR-PS+ individuals from those who did not develop psychosis later (CHR-PS-) and those with uncertain follow-up status (CHR-UNK). T1-weighted structural brain MRI scans from 1165 individuals at CHR (CHR-PS+, n = 144; CHR-PS-, n = 793; and CHR-UNK, n = 228), and 1029 HCs, were obtained from 21 sites. We used ComBat to harmonize measures of subcortical volume, cortical thickness and surface area data and corrected for non-linear effects of age and sex using a general additive model. CHR-PS+ (n = 120) and HC (n = 799) data from 20 sites served as a training dataset, which we used to build a classifier. The remaining samples were used external validation datasets to evaluate classifier performance (test, independent confirmatory, and independent group [CHR-PS- and CHR-UNK] datasets). The accuracy of the classifier on the training and independent confirmatory datasets was 85% and 73% respectively. Regional cortical surface area measures-including those from the right superior frontal, right superior temporal, and bilateral insular cortices strongly contributed to classifying CHR-PS+ from HC. CHR-PS- and CHR-UNK individuals were more likely to be classified as HC compared to CHR-PS+ (classification rate to HC: CHR-PS+, 30%; CHR-PS-, 73%; CHR-UNK, 80%). We used multisite sMRI to train a classifier to predict psychosis onset in CHR individuals, and it showed promise predicting CHR-PS+ in an independent sample. The results suggest that when considering adolescent brain development, baseline MRI scans for CHR individuals may be helpful to identify their prognosis. Future prospective studies are required about whether the classifier could be actually helpful in the clinical settings.

Biomedical research on the brain has led to many discoveries and developments, such as understanding human consciousness and the mind and overcoming brain diseases. However, historical biomedical research on the brain has unique characteristics that differ from those of conventional biomedical research. For example, there are different scientific interpretations due to the high complexity of the brain and insufficient intercommunication between researchers of different disciplines owing to the limited conceptual and technical overlap of distinct backgrounds. Therefore, the development of biomedical research on the brain has been slower than that in other areas. Brain biomedical research has recently undergone a paradigm shift, and conducting patient-centered, large-scale brain biomedical research has become possible using emerging high-throughput analysis tools. Neuroimaging, multiomics, and artificial intelligence technology are the main drivers of this new approach, foreshadowing dramatic advances in translational research. In addition, emerging interdisciplinary cooperative studies provide insights into how unresolved questions in biomedicine can be addressed. This review presents the in-depth aspects of conventional biomedical research and discusses the future of biomedical research on the brain.

BackgroundWhole brain delineation (WBD) is utilized in neuroimaging analysis for data preprocessing and deriving whole brain image metrics. Current automated WBD techniques for analysis of preclinical brain MRI data show limited accuracy when images present with significant neuropathology and anatomical deformations, such as that resulting from organophosphate intoxication (OPI) and Alzheimer's Disease (AD), and inadequate generalizability.MethodsA modified 2D U-Net framework was employed for WBD of MRI rodent brains, consisting of 27 convolutional layers, batch normalization, two dropout layers and data augmentation, after training parameter optimization. A total of 265 T2-weighted 7.0 T MRI scans were utilized for the study, including 125 scans of an OPI rat model for neural network training. For testing and validation, 20 OPI rat scans and 120 scans of an AD rat model were utilized. U-Net performance was evaluated using Dice coefficients (DC) and Hausdorff distances (HD) between the U-Net-generated and manually segmented WBDs.ResultsThe U-Net achieved a DC (median[range]) of 0.984[0.936-0.990] and HD of 1.69[1.01-6.78] mm for OPI rat model scans, and a DC (mean[range]) of 0.975[0.898-0.991] and HD of 1.49[0.86-3.89] for the AD rat model scans.Comparison with existing methodsThe proposed approach is fully automated and robust across two rat strains and longitudinal brain changes with a computational speed of 8 seconds/scan, overcoming limitations of manual segmentation.ConclusionsThe modified 2D U-Net provided a fully automated, efficient, and generalizable segmentation approach that achieved high accuracy across two disparate rat models of neurological diseases.

Higher heart rate variability (HRV) at rest is associated with better emotion regulation ability. While the neurovisceral integration model explains this by postulating that HRV can index how the brain adaptively modulates responses to emotional stimuli, neuroimaging studies directly supporting this idea are scarce. We examined the neural correlates of regulating negative and positive emotion in relation to resting HRV based on the neuroimaging and heart rate data of one hundred young adults. The results showed that those with higher HRV better recruit the medial prefrontal cortex while intensifying positive compared to negative emotion. We also examined how individual differences in resting HRV are associated with adjusting brain activity to repeated emotional stimuli. During repeated viewing of emotional images, subjects with higher resting HRV better reduced activity in the medial prefrontal cortex, posterior cingulate gyrus, and angular gyrus, most of which overlapped with the default mode network. This HRV-DMN association was observed during passively viewing emotional images rather than during actively regulating emotion. While the regulating trials can better detect task-induced changes, the viewing trials might approximate resting state, better revealing individual differences. These findings suggest two possibilities: people with higher resting HRV might have a tendency to spontaneously engage with emotion regulation or possess a trait helping emotional arousal fade away.

Adolescent traumatic brain injury (TBI) has long-term effects on brain functioning and behavior, impacting neural activity under cognitive load, especially in the reward network. Adolescent TBI is also linked to risk-taking behaviors including alcohol misuse. It remains unclear how TBI and neural functioning interact to predict alcohol experimentation during adolescence. Using Adolescent Brain Cognitive Development (ABCD) study data, this project examined if TBI at ages 9-10 predicts increased odds of alcohol sipping at ages 11-13 and if this association is moderated by neural activity during the Emotional EN-Back working memory task at ages 11-13. Logistic regression analyses showed that neural activity in regions of the fronto-basal ganglia network predicted increased odds of sipping alcohol by ages 11-13 (p

Most neuroimaging studies linking regional brain volumes with cognition correct for total intracranial volume (ICV), but methods used for this correction differ across studies. It is unknown whether different ICV correction methods yield consistent results. Using a brain-wide association approach in the MRI substudy of UK Biobank (N = 41,964; mean age = 64.5 years), we used regression models to estimate the associations of 58 regional brain volumetric measures with eight cognitive outcomes, comparing no correction and four ICV correction approaches. Approaches evaluated included: no correction; dividing regional volumes by ICV (proportional approach); including ICV as a covariate in the regression (adjustment approach); and regressing the regional volumes against ICV in different normative samples and using calculated residuals to determine associations (residual approach). We used Spearman-rank correlations and two consistency measures to quantify the extent to which associations were inconsistent across ICV correction approaches for each possible brain region and cognitive outcome pair across 2320 regression models. When the association between brain volume and cognitive performance was close to null, all approaches produced similar estimates close to the null. When associations between a regional volume and cognitive test were not null, the adjustment and residual approaches typically produced similar estimates, but these estimates were inconsistent with results from the crude and proportional approaches. For example, when using the crude approach, an increase of 0.114 (95% confidence interval [CI]: 0.103-0.125) in fluid intelligence was associated with each unit increase in hippocampal volume. However, when using the adjustment approach, the increase was 0.055 (95% CI: 0.043-0.068), while the proportional approach showed a decrease of -0.025 (95% CI: -0.035 to -0.014). Different commonly used methods to correct for ICV yielded inconsistent results. The proportional method diverges notably from other methods and results were sometimes biologically implausible. A simple regression adjustment for ICV produced biologically plausible associations.

IntroductionEarly risk stratification for clinical dementia could lead to preventive therapies. We identified and validated a magnetic resonance imaging (MRI) signature for Alzheimer's disease (AD) and related dementias (ARDR).MethodsAn MRI ADRD signature was derived from cortical thickness maps in Framingham Heart Study (FHS) participants with AD dementia and matched controls. The signature was related to the risk of ADRD and cognitive function in FHS. Results were replicated in the University of California Davis Alzheimer's Disease Research Center (UCD-ADRC) cohort.ResultsParticipants in the bottom quartile of the signature had more than three times increased risk for ADRD compared to those in the upper three quartiles (P

IntroductionAssociations of cerebellar atrophy with specific neuropathologies in Alzheimer's disease and related dementias (ADRD) have not been systematically analyzed. This study examined cerebellar gray matter volume across major pathological subtypes of ADRD.MethodsCerebellar gray matter volume was examined using voxel-based morphometry in 309 autopsy-proven ADRD cases and 80 healthy controls. ADRD subtypes included AD, mixed Lewy body disease and AD (LBD-AD), and frontotemporal lobar degeneration (FTLD). Clinical function was assessed using the Clinical Dementia Rating (CDR) scale.ResultsDistinct patterns of cerebellar atrophy were observed in all ADRD subtypes. Significant cerebellar gray matter changes appeared in the early stages of most subtypes and the very early stages of AD, LBD-AD, FTLD-TDP type A, and progressive supranuclear palsy. Cortical atrophy positively predicted cerebellar atrophy across all subtypes.DiscussionOur findings establish pathology-specific profiles of cerebellar atrophy in ADRD and propose cerebellar neuroimaging as a non-invasive biomarker for differential diagnosis and disease monitoring.HighlightsCerebellar atrophy was examined in 309 patients with autopsy-proven neurodegeneration. Distinct patterns of cerebellar atrophy are found in all pathological subtypes of Alzheimer's disease and related dementias (ADRD). Cerebellar atrophy is seen in early-stage (Clinical Dementia Rating [CDR] ≤1) AD, Lewy body dementia (LBD), frontotemporal lobar degeneration with tau-positive inclusion (FTLD-tau), and FTLD-transactive response DNA binding protein (FTLD-TDP). Cortical atrophy positively predicts cerebellar atrophy across all neuropathologies.

Diffusion MRI (dMRI) can be used to probe microstructural properties of brain tissue and holds great promise as a means to non-invasively map Alzheimer's disease (AD) pathology. Few studies have evaluated multi-shell dMRI models such as neurite orientation dispersion and density imaging (NODDI) and mean apparent propagator (MAP)-MRI in cortical gray matter where many of the earliest histopathological changes occur in AD. Here, we investigated the relationship between CSF pTau181 and Aβ1-42 burden and regional cortical NODDI and MAP-MRI indices in 46 cognitively unimpaired individuals, 18 with mild cognitive impairment, and two with dementia (mean age: 71.8 ± 6.2 years) from the Alzheimer's Disease Neuroimaging Initiative. We compared findings to more conventional cortical thickness measures. Lower CSF Aβ1-42 and higher pTau181 were associated with cortical dMRI measures reflecting less hindered or restricted diffusion and greater diffusivity. Cortical dMRI measures, but not cortical thickness measures, were more widely associated with Aβ1-42 than pTau181 and better distinguished Aβ+ from Aβ- participants than pTau+ from pTau- participants. dMRI associations mediated the relationship between CSF markers and delayed logical memory performance, commonly impaired in early AD. dMRI metrics sensitive to early AD pathogenesis and microstructural damage may be better measures of subtle neurodegeneration in comparison to standard cortical thickness and help to elucidate mechanisms underlying cognitive decline.

BACKGROUND: Adult patients with mild traumatic brain injury (mTBI) exhibit distinct phenotypes of emotional and cognitive functioning identified by latent profile analysis of clinical neuropsychological assessments. When discerned early after injury, these latent clinical profiles have been found to improve prediction of long-term outcomes from mTBI. The present study hypothesized that white matter (WM) microstructure is better preserved in an emotionally resilient mTBI phenotype compared with a neuropsychiatrically distressed mTBI phenotype. METHODS: The present study used diffusion magnetic resonance imaging to investigate and compare WM microstructure in major association, projection, and commissural tracts between the two phenotypes and over time. Diffusion magnetic resonance images from 172 patients with mTBI were analyzed to compute individual diffusion tensor imaging maps at 2 weeks and 6 months after injury. RESULTS: By comparing the diffusion tensor imaging parameters between the two phenotypes at global, regional, and voxel levels, emotionally resilient patients were shown to have higher axial diffusivity compared with neuropsychiatrically distressed patients early after mTBI. Longitudinal analysis revealed greater compromise of WM microstructure in neuropsychiatrically distressed patients, with greater decrease of global axial diffusivity and more widespread decrease of regional axial diffusivity during the first 6 months after injury compared with emotionally resilient patients. CONCLUSIONS: These results provide neuroimaging evidence of WM microstructural differences underpinning mTBI phenotypes identified from neuropsychological assessments and show differing longitudinal trajectories of these biological effects. These findings suggest that diffusion magnetic resonance imaging can provide short- and long-term imaging biomarkers of resilience.

The linear mixed-effects model (LME) is a versatile approach to account for dependence among observations. Many large-scale neuroimaging datasets with complex designs have increased the need for LME; however LME has seldom been used in whole-brain imaging analyses due to its heavy computational requirements. In this paper, we introduce a fast and efficient mixed-effects algorithm (FEMA) that makes whole-brain vertex-wise, voxel-wise, and connectome-wide LME analyses in large samples possible. We validate FEMA with extensive simulations, showing that the estimates of the fixed effects are equivalent to standard maximum likelihood estimates but obtained with orders of magnitude improvement in computational speed. We demonstrate the applicability of FEMA by studying the cross-sectional and longitudinal effects of age on region-of-interest level and vertex-wise cortical thickness, as well as connectome-wide functional connectivity values derived from resting state functional MRI, using longitudinal imaging data from the Adolescent Brain Cognitive DevelopmentSM Study release 4.0. Our analyses reveal distinct spatial patterns for the annualized changes in vertex-wise cortical thickness and connectome-wide connectivity values in early adolescence, highlighting a critical time of brain maturation. The simulations and application to real data show that FEMA enables advanced investigation of the relationships between large numbers of neuroimaging metrics and variables of interest while considering complex study designs, including repeated measures and family structures, in a fast and efficient manner. The source code for FEMA is available via: https://github.com/cmig-research-group/cmig_tools/.

PURPOSE: To investigate the impact of reduced injected doses on the quantitative and qualitative assessment of the amyloid PET tracers [18F]flutemetamol and [18F]florbetaben. METHODS: Cognitively impaired and unimpaired individuals (N = 250, 36% Aβ-positive) were included and injected with [18F]flutemetamol (N = 175) or [18F]florbetaben (N = 75). PET scans were acquired in list-mode (90-110 min post-injection) and reduced-dose images were simulated to generate images of 75, 50, 25, 12.5 and 5% of the original injected dose. Images were reconstructed using vendor-provided reconstruction tools and visually assessed for Aβ-pathology. SUVRs were calculated for a global cortical and three smaller regions using a cerebellar cortex reference tissue, and Centiloid was computed. Absolute and percentage differences in SUVR and CL were calculated between dose levels, and the ability to discriminate between Aβ- and Aβ + scans was evaluated using ROC analyses. Finally, intra-reader agreement between the reduced dose and 100% images was evaluated. RESULTS: At 5% injected dose, change in SUVR was 3.72% and 3.12%, with absolute change in Centiloid 3.35CL and 4.62CL, for [18F]flutemetamol and [18F]florbetaben, respectively. At 12.5% injected dose, percentage change in SUVR and absolute change in Centiloid were  80% for both tracers. CONCLUSION: This proof-of-concept study showed that for both [18F]flutemetamol and [18F]florbetaben, adequate quantitative and qualitative assessments can be obtained at 12.5% of the original injected dose. However, decisions to reduce the injected dose should be made considering the specific clinical or research circumstances.

IntroductionAdverse childhood experiences (ACEs) are linked to higher rates of psychiatric disorders in adults. Previous neuroimaging studies with small samples have shown associations between ACEs and alterations in brain volume, connectivity, and blood flow. However, no study has explored these associations in a large clinical population to identify brain regions that may mediate the relationship between ACEs and psychiatric diagnoses. This study aims to evaluate how patient-reported ACEs are associated with brain function in adults, across diagnoses.MethodsWe analyzed 7,275 adults using HMPAO SPECT scans at rest and during a continuous performance task (CPT). We assessed the impact of ACEs on brain function across psychiatric diagnoses and performed mediation analyses where brain functional regions of interest acted as mediators between patient-reported ACEs and specific psychiatric diagnoses. We further evaluated the risk of being diagnosed with specific classes of mental illnesses as a function of increasing ACEs and identified which specific ACE questions were statistically related to each diagnosis in this cohort.ResultsIncreased ACEs were associated with higher activity in cognitive control and default mode networks and decreased activity in the dorsal striatum and cerebellum. Higher ACEs increased the risk of anxiety-related disorders, substance abuse, and depression. Several brain regions were identified as potential mediators between ACEs and adult psychiatric diagnoses.DiscussionThis study, utilizing a large clinical cohort, provides new insights into the neurobiological mechanisms linking ACEs to adult psychiatric conditions. The findings suggest that specific brain regions mediate the effects of ACEs on the risk of developing mental health disorders, highlighting potential targets for therapeutic interventions.

BackgroundPlasma neurofilament light chain (NfL) is a promising biomarker of neurodegeneration with potential clinical utility in monitoring the progression of neurodegenerative diseases. However, the cross-sectional associations of plasma NfL with measures of cognition and brain have been inconsistent in community-dwelling populations.MethodsWe examined these associations in a large community-dwelling sample of early old age men (N = 969, mean age = 67.57 years, range = 61-73 years), who are either cognitively unimpaired (CU) or with mild cognitive impairment (MCI). Specifically, we investigated five cognitive domains (executive function, episodic memory, verbal fluency, processing speed, visual-spatial ability), as well as neuroimaging measures of gray and white matter.ResultsAfter adjusting for age, health status, and young adult general cognitive ability, plasma NfL level was only significantly associated with processing speed and white matter hyperintensity (WMH) volume, but not with other cognitive or neuroimaging measures. The association with processing speed was driven by individuals with MCI, as it was not detected in CU individuals.ConclusionsThese results suggest that in early old age men without dementia, plasma NfL does not appear to be sensitive to cross-sectional individual differences in most domains of cognition or neuroimaging measures of gray and white matter. The revealed plasma NfL associations were limited to WMH for all participants and processing speed only within the MCI cohort. Importantly, considering cognitive status in community-based samples will better inform the interpretation of the relationships of plasma NfL with cognition and brain and may help resolve mixed findings in the literature.

While continued cannabis use and misuse in individuals with schizophrenia is associated with a variety of negative outcomes, individuals with a history of use tend to show higher cognitive performance compared to non-users. While this is replicated in the literature, few studies have used task-based functional magnetic resonance imaging (fMRI) to evaluate whether the brain networks underpinning these cognitive features are similarly impacted. Forty-eight first-episode individuals with schizophrenia (FES) with a history of cannabis use (FES + CAN), 28 FES individuals with no history of cannabis use (FES-CAN), and 59 controls (CON) performed the AX-Continuous Performance Task during fMRI. FES+CAN showed higher cognitive control performance (d'-context) compared to FES-CAN (P

There is a dearth of statistical models that adequately capture the total signal attributed to whole-brain imaging features. The total signal is often widely distributed across the brain, with individual imaging features exhibiting small effect sizes for predicting neurobehavioral phenotypes. The challenge of capturing the total signal is compounded by the distribution of neurobehavioral data, particularly responses to psychological questionnaires, which often feature zero-inflated, highly skewed outcomes. To close this gap, we have developed a novel Variational Bayes algorithm that characterizes the total signal captured by whole-brain imaging features for zero-inflated outcomes. Our zero-inflated variance (ZIV) estimator estimates the fraction of variance explained (FVE) and the proportion of non-null effects (PNN) from large-scale imaging data. In simulations, ZIV demonstrates superior performance over other linear models. When applied to data from the Adolescent Brain Cognitive DevelopmentSM (ABCD) Study, we found that whole-brain imaging features contribute to a larger FVE for externalizing behaviors compared to internalizing behaviors. Moreover, focusing on features contributing to the PNN, ZIV estimator localized key neurocircuitry associated with neurobehavioral traits. To the best of our knowledge, the ZIV estimator is the first specialized method for analyzing zero-inflated neuroimaging data, enhancing future studies on brain-behavior relationships and improving the understanding of neurobehavioral disorders.

The brain is a complex dynamic system whose current state is inextricably coupled to awareness of past, current, and anticipated future threats and opportunities that continually affect awareness and behavioral goals and decisions. Brain activity is driven on multiple time scales by an ever-evolving flow of sensory, proprioceptive, and idiothetic experience. Neuroimaging experiments seek to isolate and focus on some aspect of these complex dynamics to better understand how human experience, cognition, behavior, and health are supported by brain activity. Here we consider an event-related data modeling approach that seeks to parse experience and behavior into a set of time-delimited events. We distinguish between event processes themselves, that unfold through time, and event markers that record the experiment timeline latencies of event onset, offset, and any other event phase transitions. Precise descriptions of experiment events (sensory, motor, or other) allow participant experience and behavior to be interpreted in the context either of the event itself or of all or any experiment events. We discuss how events in neuroimaging experiments have been, are currently, and should best be identified and represented with emphasis on the importance of modeling both events and event context for meaningful interpretation of relationships between brain dynamics, experience, and behavior. We show how text annotation of time series neuroimaging data using the system of Hierarchical Event Descriptors (HED; https://www.hedtags.org) can more adequately model the roles of both events and their ever-evolving context than current data annotation practice and can thereby facilitate data analysis, meta-analysis, and mega-analysis. Finally, we discuss ways in which the HED system must continue to expand to serve the evolving needs of neuroimaging research.

PurposeWe evaluated the impact of partial volume correction (PVC) methods on the quantification of longitudinal [18F]GTP1 tau positron-emission tomography (PET) in Alzheimer's disease and the suitability of describing the tau pathology burden temporal trajectories using linear mixed-effects models (LMEM).MethodsWe applied van Cittert iterative deconvolution (VC), 2-compartment, and 3-compartment, and the geometric transfer matrix plus region-based voxelwise methods to data acquired in an Alzheimer's disease natural history study over 18 months at a single imaging site. We determined the optimal PVC method by comparing the standardized uptake value ratio change (%ΔSUVR) between diagnostic and tau burden-level groups and the longitudinal repeatability derived from the LMEM. The performance of LMEM analysis for calculating %ΔSUVR was evaluated in a natural history study and in a multisite clinical trial of semorinemab in prodromal to mild Alzheimer's disease by comparing results to traditional per-visit estimates.ResultsThe VC, 2-compartment, and 3-compartment PVC methods had similar performance, whereas region-based voxelwise overcorrected regions with a higher tau burden. The lowest within-subject variability and acceptable group separation scores were observed without PVC. The LMEM-derived %ΔSUVR values were similar to the per-visit estimates with lower variability.ConclusionThe results indicate that the tested PVC methods do not offer a clear advantage or improvement over non-PVC images for the quantification of longitudinal [18F]GTP1 PET data. LMEM offers a robust framework for the longitudinal tau PET quantification with low longitudinal test-retest variability.Clinical trial registrationNCT02640092 and NCT03289143.

BACKGROUND: Loneliness has been declared an epidemic associated with negative physical, mental, and cognitive health outcomes such as increased dementia risk. Less is known about the relationship between loneliness and advanced neuroimaging correlates of Alzheimers disease (AD). OBJECTIVE: To assess whether loneliness was associated with advanced neuroimaging markers of AD using neuroimaging data from Framingham Heart Study (FHS) participants without dementia. METHODS: In this cross-sectional observational analysis, we used functional connectivity MRI (fcMRI), amyloid-β (Aβ) PET, and tau PET imaging data collected between 2016 and 2019 on eligible FHS cohort participants. Loneliness was defined as feeling lonely at least one day in the past week. The primary fcMRI marker was Default Mode Network intra-network connectivity. The primary PET imaging markers were Aβ deposition in precuneal and FLR (frontal, lateral parietal and lateral temporal, retrosplenial) regions, and tau deposition in the amygdala, entorhinal, and rhinal regions. RESULTS: Of 381 participants (mean age 58 [SD 10]) who met inclusion criteria for fcMRI analysis, 5% were classified as lonely (17/381). No association was observed between loneliness status and network changes. Of 424 participants (mean age 58 [SD = 10]) meeting inclusion criteria for PET analyses, 5% (21/424) were lonely; no associations were observed between loneliness and either Aβ or tau deposition in primary regions of interest. CONCLUSIONS: In this cross-sectional study, there were no observable associations between loneliness and select fcMRI, Aβ PET, and tau PET neuroimaging markers of AD risk. These findings merit further investigation in prospective studies of community-based cohorts.

Novel brain clearing methods revolutionize imaging by increasing visualization throughout the brain at high resolution. However, combining the standard tool of immunostaining targets of interest with clearing methods has lagged behind. We integrate whole-mount immunostaining with PEGASOS tissue clearing, referred to as iPEGASOS (immunostaining-compatible PEGASOS), to address the challenge of signal quenching during clearing processes. iPEGASOS effectively enhances molecular-genetically targeted fluorescent signals that are otherwise compromised during conventional clearing procedures. Additionally, we demonstrate the utility of iPEGASOS for visualizing neurochemical markers or viral labels to augment visualization that transgenic mouse lines cannot provide. Our study encompasses three distinct applications, each showcasing the versatility and efficacy of this approach. We employ whole-mount immunostaining to enhance molecular signals in transgenic reporter mouse lines to visualize the whole-brain spatial distribution of specific cellular populations. We also significantly improve the visualization of neural circuit connections by enhancing signals from viral tracers injected into the brain. Last, we show immunostaining without genetic markers to selectively label beta-amyloid deposits in a mouse model of Alzheimers disease, facilitating the comprehensive whole-brain study of pathological features.

Alzheimer's disease (AD) is a neurodegenerative disease that commonly causes dementia. Identifying biomarkers for the early detection of AD is an emerging need, as brain dysfunction begins two decades before the onset of clinical symptoms. To this end, we reanalyzed untargeted metabolomic mass spectrometry data from 905 patients enrolled in the AD Neuroimaging Initiative (ADNI) cohort using MS-DIAL, with 1,304,633 spectra of 39,108 unique biomolecules. Metabolic profiles of 93 hydrophilic metabolites were determined. Additionally, we integrated targeted lipidomic data (4873 samples from 1524 patients) to explore candidate biomarkers for predicting progressive mild cognitive impairment (pMCI) in patients diagnosed with AD within two years using the baseline metabolome. Patients with lower ergothioneine levels had a 12% higher rate of AD progression with the significance of P = 0.012 (Wald test). Furthermore, an increase in ganglioside (GM3) and decrease in plasmalogen lipids, many of which are associated with apolipoprotein E polymorphism, were confirmed in AD patients, and the higher levels of lysophosphatidylcholine (18:1) and GM3 d18:1/20:0 showed 19% and 17% higher rates of AD progression, respectively (Wald test: P = 3.9 × 10-8 and 4.3 × 10-7). Palmitoleamide, oleamide, diacylglycerols, and ether lipids were also identified as significantly altered metabolites at baseline in patients with pMCI. The integrated analysis of metabolites and genomics data showed that combining information on metabolites and genotypes enhances the predictive performance of AD progression, suggesting that metabolomics is essential to complement genomic data. In conclusion, the reanalysis of multiomics data provides new insights to detect early development of AD pathology and to partially understand metabolic changes in age-related onset of AD.

Over the past two decades Biomedical Engineering has emerged as a major discipline that bridges societal needs of human health care with the development of novel technologies. Every medical institution is now equipped at varying degrees of sophistication with the ability to monitor human health in both non-invasive and invasive modes. The multiple scales at which human physiology can be interrogated provide a profound perspective on health and disease. We are at the nexus of creating avatars (herein defined as an extension of digital twins) of human patho/physiology to serve as paradigms for interrogation and potential intervention. Motivated by the emergence of these new capabilities, the IEEE Engineering in Medicine and Biology Society, the Departments of Biomedical Engineering at Johns Hopkins University and Bioengineering at University of California at San Diego sponsored an interdisciplinary workshop to define the grand challenges that face biomedical engineering and the mechanisms to address these challenges. The Workshop identified five grand challenges with cross-cutting themes and provided a roadmap for new technologies, identified new training needs, and defined the types of interdisciplinary teams needed for addressing these challenges. The themes presented in this paper include: 1) accumedicine through creation of avatars of cells, tissues, organs and whole human; 2) development of smart and responsive devices for human function augmentation; 3) exocortical technologies to understand brain function and treat neuropathologies; 4) the development of approaches to harness the human immune system for health and wellness; and 5) new strategies to engineer genomes and cells.

BackgroundPrior studies of Alzheimer's disease (AD) biomarker disclosure have answered important questions about individuals' safety after learning and comprehending their amyloid PET results; however, these studies have typically employed highly structured disclosure protocols and focused on the psychological impact of disclosure (e.g., anxiety, depression, and suicidality) in homogeneous populations. More work is needed to develop flexible disclosure protocols and study outcomes in ethnoculturally representative samples.MethodsThe Alzheimer's Disease Neuroimaging Initiative (ADNI) is formally incorporating amyloid PET disclosure into the newest protocol (ADNI-4). Participants across the cognitive spectrum who wish to know their amyloid PET results may learn them. The pragmatic disclosure process spans four timepoints: (1) a pre-disclosure visit, (2) the PET scan and its read, (3) a disclosure visit, and (4) a post-disclosure check-in. This process applies to all participants, with slight modifications to account for their cognitive status. In designing this process, special emphasis was placed on utilizing investigator discretion. Participant measures include perceived risk of dementia, purpose in life, and disclosure satisfaction. Investigator assessment of the disclosure visit (e.g., challenges encountered, topics discussed, etc.) is also included.ResultsData collection is ongoing. Results will allow for more robust characterization of the impact of learning amyloid PET results on individuals and describe the perspectives of investigators.ConclusionThe pragmatic design of the disclosure process in ADNI-4 coupled with the novel participant and investigator data will inform future disclosure practices. This is especially important as disclosure of biomarker results expands in research and care.

BackgroundAPOE4 is the strongest genetic risk factor for sporadic Alzheimer's disease (AD), whereas APOE2 confers protection. However, effects of APOE on neurodegeneration in cognitively intact individuals, and how these associations evolve with cognitive decline, are unclear. Furthermore, few studies have evaluated whether effects of APOE on neurodegenerative changes are modified by other AD key risk factors including age and sex.MethodsParticipants included older adults (57% women; 77 ± 7 years) from the Rancho Bernardo Study of Health Aging and the University of California San Diego Alzheimer's Disease Research Center, including 192 cognitively normal (CN) individuals and 33 with mild cognitive impairment. Participants underwent diffusion MRI, and multicompartment restriction spectrum imaging (RSI) metrics were computed in white matter, gray matter, and subcortical regions of interest. Participants were classified as APOE4 carriers, APOE2 carriers, and APOE3 homozygotes. Analysis of covariance among CN (adjusting for age, sex, and scanner) assessed differences in brain microstructure by APOE, as well as interactions between APOE and sex. Analyses across all participants examined interactions between APOE4 and cognitive status. Linear regressions assessed APOE by age interactions.ResultsAmong CN, APOE4 carriers showed lower entorhinal cortex neurite density than non-carriers, whereas APOE2 carriers showed lower cingulum neurite density than non-carriers. Differences in entorhinal microstructure by APOE4 and in entorhinal and cingulum microstructure by APOE2 were present for women only. Age correlated with lower entorhinal restricted isotropic diffusion among APOE4 non-carriers, whereas age correlated with lower putamen restricted isotropic diffusion among APOE4 carriers. Differences in microstructure between cognitively normal and impaired participants were stronger for APOE4-carriers in medial temporal regions, thalamus, and global gray matter, but stronger for non-carriers in caudate.ConclusionsThe entorhinal cortex may be an early target of neurodegenerative changes associated with APOE4 in presymptomatic individuals, whereas APOE2 may support beneficial white matter and entorhinal microstructure, with potential sex differences that warrant further investigation. APOE modifies microstructural patterns associated with aging and cognitive impairment, which may advance the development of biomarkers to distinguish microstructural changes characteristic of normal brain aging, APOE-dependent pathways, and non-AD etiologies.

The human brain grows quickly during infancy and early childhood, but factors influencing brain maturation in this period remain poorly understood. To address this gap, we harmonized data from eight diverse cohorts, creating one of the largest pediatric neuroimaging datasets to date focused on birth to 6 years of age. We mapped the developmental trajectory of intracranial and subcortical volumes in ∼2,000 children and studied how sociodemographic factors and adverse birth outcomes influence brain structure and cognition. The amygdala was the first subcortical volume to mature, whereas the thalamus exhibited protracted development. Males had larger brain volumes than females, and children born preterm or with low birthweight showed catch-up growth with age. Socioeconomic factors exerted region- and time-specific effects. Regarding cognition, males scored lower than females; preterm birth affected all developmental areas tested, and socioeconomic factors affected visual reception and receptive language. Brain-cognition correlations revealed region-specific associations.

The Alzheimer's Disease Neuroimaging Initiative (ADNI) Private Partners Scientific Board (PPSB) encompasses members from industry, biotechnology, diagnostic, and non-profit organizations that have until recently been managed by the Foundation for the National Institutes of Health (FNIH) and provided financial and scientific support to ADNI programs. In this article, we review some of the major activities undertaken by the PPSB, focusing on those supporting the most recently completed National Institute on Aging grant, ADNI3, and the impact it has had on streamlining biomarker discovery and validation in Alzheimer's disease. We also provide a perspective on the gaps that may be filled with future PPSB activities as part of ADNI4 and beyond. HIGHLIGHTS: The Private Partners Scientific board (PPSB) continues to play a key role in enabling several Alzheimer's Disease Neuroimaging Initiative (ADNI) activities. PPSB working groups have led landscape assessments to provide valuable feedback on new technologies, platforms, and methods that may be taken up by ADNI in current or future iterations.

BACKGROUND: Existing neuroimaging techniques, including magnetic resonance imaging, computed tomography, and high-resolution ultrasound, lack the capability to provide real-time intraoperative positioning images to surgeons. However, the clinical implementation of near-infrared fluorescence imaging technology has made it possible to directly visualize surgical target areas, offering a novel solution for real-time nerve identification during surgery. OBJECTIVE: To provide a summary and overview of the research progress in near-infrared fluorescence imaging technology for intraoperative neuroimaging. METHODS: The first author used the computer to search for the documents published from January 2010 to July 2023 in WanFang, CNKI, and PubMed with the key words of “near-infrared fluorescence imaging, optical imaging, nerve imaging” in Chinese and English. A few classic old documents were also included. Initial screening was performed by reading the titles and abstracts; duplicate, low-quality, and irrelevant content documents were excluded. A total of 69 articles were finally included for review. RESULTS AND CONCLUSION: Near-infrared fluorescence imaging guided by indocyanine green has been clinically used to identify and locate tubular organs such as blood vessels, ureters, and bile ducts, as well as various tumors during surgery. This technique is currently considered a well-established imaging method in precision surgery. In the field of intraoperative neurofluorescence imaging, indocyanine green is currently the only near-infrared fluorescent dye used in clinical research. The ideal neuroimaging agent should possess certain characteristics, including easy administration in the perioperative period, logD between 0.5 and 3 at pH=7.4, molecular mass below 500 Da, excitation and emission wavelengths within the near-infrared window, long-term retention in nerve tissue, high signal-to-background ratio, and high safety. In the future, the development of near-infrared neurofluorescence imaging agents should focus on synthesizing complexes of indocyanine green and neural-specific targets. This technology not only enables intraoperative neurofluorescence imaging, but also holds promising prospects for in-situ monitoring of nerve regeneration and diagnosis of neurological diseases. [ABSTRACT FROM AUTHOR]

This review systematically analyzes potential biomarker candidates for post‐traumatic epilepsy (PTE) in humans who have experienced moderate to severe traumatic brain injury (TBI). Focusing on biomarkers across biofluid‐based protein, genetic, neuroimaging, and neurophysiological categories, this review distinguishes between TBI patients who develop PTE and those who do not. The review adheres to established methodologies outlined in the Cochrane Handbook for Systematic Reviews of Interventions. Data presentation follows the Meta‐analyses of Observational Studies (MOOSE) and Preferred Reporting Items for Systematic Reviews and Meta‐Analyses (PRISMA) guidelines. Medline, Embase, and Web of Science were systematically searched and yielded 7538 records, of which 18 met inclusion criteria (moderate–severe TBI in humans, follow‐up of at least 6 months, and no prior history of epilepsy). The review aggregates data from 15 cohort and 3 case–control studies (risk of bias was assessed using the Newcastle‐Ottawa Scale). Statistically significant biomarkers were identified, with neurophysiological biomarkers showing the strongest effect size in a two‐study meta‐analysis. PTE, a severe long‐term outcome of TBI affecting 2% to 53% of individuals with TBI, lacks validated biomarkers for forecasting development, crucial for designing preventive clinical trials. A multimodal approach, integrating biofluid‐based protein, genetic, neuroimaging, and neurophysiological data, offers a promising strategy to enhance the predictability of PTE development and, potentially, its treatment. The study's protocol is registered in the International Prospective Register of Systematic Reviews PROSPERO (Registration ID: CRD42023470245). [ABSTRACT FROM AUTHOR]

Background Objective Methods Results Conclusion Understanding the neural mechanisms underlying migraine and other primary headache disorders is critical for the development of long‐term cures. Magnetoencephalography (MEG), an imaging modality that measures neuronal currents and cortical excitability with high temporal and superior spatial resolution, has been increasingly used in neurological research. Initial MEG studies showed promise in directly recording cortical spreading depression—a cortical correlate of migraine with aura. However, lately MEG technology has highly evolved with greater potential to reveal underlying pathophysiology of migraine and primary headache disorders, and aid in the identification of biomarkers.To systematically review the use of MEG in migraine and other primary headache disorders and summarize findings.We conducted a systematic search and selection of MEG studies in migraine and primary headache disorders from inception until June 8, 2023, in Medline, Embase, Cochrane, and Scopus databases. Peer‐reviewed English articles reporting the use of MEG for clinical or research purposes in migraine and primary headache disorders were selected.We found 560 articles and included 38 in this review after screening. Twelve studies investigated resting‐state, while others investigated a sensory modality using an evoked or event‐related paradigm with a total of 35 cohort and 3 case studies. Thirty‐two studies focused exclusively on migraine, while the rest reported other primary headache disorders.The findings show an evolution of MEG from a 7‐ to a 306‐channel system and analysis evolving from sensor‐level evoked responses to more advanced source‐level connectivity measures. A relatively few MEG studies portrayed migraine and primary headache disorders as a sensory abnormality, especially of the visual system. We found heterogeneity in the datasets, data reporting standards (due to constantly evolving MEG technology and analysis methods), and patient characteristics. Studies were inadequately powered and there was no evidence of blinding procedures to avoid selection bias in case–control studies, which could have led to false‐positive findings. More studies are needed to investigate the affective–cognitive aspects that exacerbate pain and disability in migraine and primary headache disorders. [ABSTRACT FROM AUTHOR]

Video presentation has become ubiquitous in paradigms investigating the neural and behavioral responses to observed actions. In spite of the great interest in uncovering the processing of observed bodily movements and actions in neuroscience and cognitive science, at present, no standardized set of video stimuli for action observation research in neuroimaging settings exists. To facilitate future action observation research, we developed an open-access database of 135 high-definition videos of a male actor performing object-oriented actions. Actions from 3 categories: kinematically natural and goal-intact (Normal), kinematically unnatural and goal-intact (How), or kinematically natural and goal-violating (What), directed toward 15 different objects were filmed from 3 angles. Psychometric evaluation of the database revealed high video recognition accuracy (Mean accuracy = 88.61 %) and substantial inter-rater agreement (Fleiss' Kappa = 0.702), establishing excellent validity and reliability. Videos' exact timing of motion onset was identified using a custom motion detection frame-differencing procedure. Based on its outcome, the videos were edited to assure that motion begins at the second frame of each video. The videos' timing of category recognition was also identified using a novel behavioral up-down staircase procedure. The identified timings can be incorporated in future experimental designs to counteract jittered stimulus onsets, thus vastly improving the sensitivity of neuroimaging experiments. All videos, their psychometric evaluations, and the timing of their frame of category recognition, as well as our custom programs for performing these evaluations on our, or on other similar video databases, are available at the Open Science Framework (https://osf.io/zexc4/). [ABSTRACT FROM AUTHOR]

Background: Resting-state functional magnetic resonance imaging (rs-fMRI) reveals diverse neural activity patterns in cervical spondylosis (CS) patients. However, the reported results are inconsistent. Therefore, our objective was to conduct a meta-analysis to synthesize the findings from existing rs-fMRI studies and identify consistent patterns of neural brain activity alterations in patients with CS. Materials and methods: A systematic search was conducted across PubMed, Web of Knowledge, Embase, Google Scholar, and CNKI for rs-fMRI studies that compared CS patients with healthy controls (HCs), up to January 28, 2024. Significant cluster coordinates were extracted for comprehensive analysis. Results: We included 16 studies involving 554 CS patients and 488 HCs. CS patients demonstrated decreased brain function in the right superior temporal gyrus and left postcentral gyrus, and increased function in the left superior frontal gyrus. Jackknife sensitivity analysis validated the robustness of these findings, and Egger's test confirmed the absence of significant publication bias (p > 0.05). Meta-regression showed no significant impact of age or disease duration differences on the results. Conclusion: This meta-analysis confirms consistent alterations in specific brain regions in CS patients, highlighting the potential of rs-fMRI to refine diagnostic and therapeutic strategies. Systematic review registration: https://www.crd.york.ac.uk/prospero/ , identifier CRD42024496263. [ABSTRACT FROM AUTHOR]

The isocitrate dehydrogenase (IDH) mutation status is one of the most important markers according to the 2021 WHO classification of CNS tumors. Preoperatively, this information is usually obtained based on invasive biopsies, contrast-enhanced MR images or PET images generated using radioactive tracers. However, the completely non-invasive determination of IDH mutation status using routinely acquired preoperative native CT images has hardly been investigated to date. In our study, we show that radiomics-based machine learning allows to determine IDH mutation status based on preoperative native CT images both with very high accuracy and completely non-invasively. Based on independent test data, we are able to correctly identify 91.1% of cases with an IDH mutation. Our final model, containing only six features, exhibits a high area under the curve of 0.847 and an excellent area under the precision-recall curve of 0.945. In the future, such models may be used for a completely non-invasive prediction of important genetic markers, potentially allowing treating physicians to reduce the number of biopsies and speed up further treatment planning. [ABSTRACT FROM AUTHOR]

Aim Methods Results Conclusion Immune dysregulation and delayed onset of sleep wake cycling (SWC) are associated with worse outcome in neonatal encephalopathy (NE), however the association between sleep and immune dysfunction in NE remains unclear. Aimed to evaluate association of sleep and systemic inflammation with outcomes in NE.Amplitude‐integrated electroencephalography (aEEG) recordings were collected on infants undergoing therapeutic hypothermia (TH). Duration to onset of (SWC) and sleep quality (SQ) were examined. Blood samples collected during the first 2 days of life. Thirteen pro‐ and anti‐inflammatory serum cytokines were quantified. Adverse outcome defined as death or abnormal MRI brain.Earlier onset of SWC and better SQ had less adverse outcomes. SQ provided better prognostic value and showed better interobserver agreement compared to duration to SWC. Better SQ associated with lower cytokines EPO and interleukin (IL)‐1β. In infants with unfavourable outcome, shorter duration to SWC was associated with higher EPO and better SQ was associated with lower TNF‐α.Earlier onset of SWC or better SQ showed less systemic inflammation and fewer adverse outcomes. SQ during TH provided better prognostic information than time of onset of SWC. Modulation of circadian rhythm in infants with NE may have an immunomodulatory role, leading to improved outcomes. [ABSTRACT FROM AUTHOR]