Craniomaxillofacial Complex Injuries Sustained During Training in Alpine Environments: Multimodal MRI-Based Analysis of Injury Patterns and Acute-Phase Assessment.

Wilson’s disease (WD) is an inherited disorder of copper metabolism. Multimodal magnetic resonance imaging (MRI) has been reported to provide evidence of the extent and severity of brain lesions. However, there are few studies related to the diagnosis of WD with multimodal MRI. Here, we reported a WD patient who was subjected to Sanger sequencing, conventional MRI, and multimodal MRI examinations, including susceptibility-weighted imaging (SWI) and arterial spin labeling (ASL). Sanger sequencing demonstrated two pathogenic mutations in exon 8 of the ATP7B gene. Slit-lamp examination revealed the presence of Kayser–Fleischer rings in both eyes, as well as low serum ceruloplasmin and high 24-h urinary copper excretion on admission. Although the substantia nigra, red nucleus, and lenticular nucleus on T1-weighted imaging and T2-weighted imaging were normal, SWI and ASL showed hypointensities in these regions. Besides, decreased cerebral blood flow was found in the lenticular nucleus and the head of caudate nucleus. The patient recovered well after 1 year and 9 months of follow-up, with only a Unified Wilson Disease Rating Scale score of 1 for neurological symptom. Brain multimodal MRI provided a thorough insight into the WD, which might make up for the deficiency of conventional MRI.

Multi-modal magnetic resonance imaging (MRI) is increasingly used in neuroscience research, as it allowed the non-invasive investigation of structure and function of the human brain in health and pathology. One of the most important applications of multi-modal MRI is the provision of vital diagnostic data for neurologic and psychiatric disorders. As traditional MRI researches using univariate analyses can only reveal disease-related structural and functional alterations at group level which limited the clinical application, and recent attention has turned toward integrating multi-modal neuroimaging and computer-aided prognosis (CAD) technology, especially machine learning, to assist clinical disease diagnose. Research in this area is growing exponentially, and therefore it is meaningful to review the current and future development of this emerging area. Hence, in this paper, based on our own studies and contributions, we review the recent advances in multi-modal MRI and CAD technologies, and their applications to assist the clinical diagnosis of three common neurologic and psychiatric disorders, namely, Alzheimer’s disease, Attention deficit/hyperactivity disorder and Tourette syndrome. We extracted multi-modal features from structural, diffusion and resting-state functional MRI, then different feature selection methods and classifiers were applied. In addition, we applied different feature fusion schemes (e.g. multiple kernel learning) to combining multi-modal features for classification. Our experiments show that using feature fusion techniques to integrate multi-modal features can yield better classification results for diseases prediction, which may outline some future directions for multi-modal neuroimaging where researchers can design more advanced methods and models for neurologic and psychiatric research.

Machine-learning based prediction of future outcome using multimodal MRI during early childhood

Background:Ongoing efforts have been made to identify new neuroimaging markers to track amyotrophic lateral sclerosis (ALS) progression. This study aimed to explore the monitoring value of multimodal magnetic resonance imaging (MRI) in the disease progression of ALS.Methods:From September 2015 to March 2017, ten patients diagnosed with ALS in Peking Union Medical College Hospital completed head MRI scans at baseline and during follow-up. Multimodal MRI analyses, including gray matter (GM) volume measured by voxel-based morphometry; cerebral blood flow (CBF) evaluated by arterial spin labeling; functional connectivity, including low-frequency fluctuation (fALFF) and regional homogeneity (ReHo), measured by resting-state functional MRI; and integrity of white-matter (WM) fiber tracts evaluated by diffusion tensor imaging, were performed in these patients. Comparisons of imaging metrics were made between baseline and follow-up using paired t-test.Results:In the longitudinal comparisons, the brain structure (GM volume of the right precentral gyri, left postcentral gyri, and right thalami) and perfusion (CBF of the bilateral temporal poles, left precentral gyri, postcentral gyri, and right middle temporal gyri) in both motor and extramotor areas at follow-up were impaired to different extents when compared with those at baseline (all P < 0.05, false discovery rate adjusted). Functional connectivity was increased in the motor areas (fALFF of the right precentral gyri and superior frontal gyri, and ReHo of right precentral gyri) and decreased in the extramotor areas (fALFF of the bilateral middle frontal gyri and ReHo of the right precuneus and cingulate gyri) (all P < 0.001, unadjusted). No significant changes were detected in terms of brain WM measures.Conclusion:Multimodal MRI could be used to monitor short-term brain changes in ALS patients.

Recent years, Alzheimer's disease (AD) has become a significant threat to human health while the accurate screening and diagnosis of AD remain a tough problem. Multimodal Magnetic resonance imaging (MRI) can help to identify the variation of brain function and structure in a non-invasive way. Deep learning, especially the convolutional neural networks (CNN), can be utilized to automatically detect appropriate features for classification, which is well adapted for computer-aided AD screening and identification. This paper proposed a multimodal MRI analytical method based on CNN, which is also suitable for single type MRI data analysis. First, the human brain network connectivity matrix were extracted from multimodal MRI data, used as the input data for CNN. Then a novel CNN framework was proposed to process the network matrix and classify AD, amnestic mild cognitive impairment (aMCI) patients and normal controls (NC). The advantage of this method lies in that we combined multimodal MRI information through CNN convolution kernel, and achieved a higher classification accuracy. In our experiments, the comprehensive classification accuracy of AD, aMCI patients and NC was as high as 92.06% when using multimodal MRI data as input, which is effective enough to provide a reference for multimodal MRI data analysis.

Cerebral hypoperfusion induced by bilateral common carotid artery occlusion (BCCAo) in rodents has been proposed as an experimental model of white matter damage and vascular dementia. However, the histopathological and behavioral alterations reported in this model are variable and a full characterization of the dynamic alterations is not available. Here we implemented a longitudinal multimodal magnetic resonance imaging (MRI) design, including time-of-flight angiography, high resolution T1-weighted images, T2 relaxometry mapping, diffusion tensor imaging, and cerebral blood flow measurements up to 12 weeks after BCCAo or sham-operation in Wistar rats. Changes in MRI were related to behavioral performance in executive function tasks and histopathological alterations in the same animals. MRI frequently (70%) showed various degrees of acute ischemic lesions, ranging from very small to large subcortical infarctions. Independently, delayed MRI changes were also apparent. The patterns of MRI alterations were related to either ischemic necrosis or gliosis. Progressive microstructural changes revealed by diffusion tensor imaging in white matter were confirmed by observation of myelinated fiber degeneration, including severe optic tract degeneration. The latter interfered with the visually cued learning paradigms used to test executive functions. Independently of brain damage, BCCAo induced progressive arteriogenesis in the vertebrobasilar tree, a process that was associated with blood flow recovery after 12 weeks. The structural alterations found in the basilar artery were compatible with compensatory adaptive changes driven by shear stress. In summary, BCCAo in rats induces specific signatures in multimodal MRI that are compatible with various types of histological lesion and with marked adaptive arteriogenesis.

Accelerated multi-modal magnetic resonance (MR) imaging is a new and effective solution for fast MR imaging, providing superior performance in restoring the target modality from its undersampled counterpart with guidance from an auxiliary modality. However, existing works simply combine the auxiliary modality as prior information, lacking in-depth investigations on the potential mechanisms for fusing different modalities. Further, they usually rely on the convolutional neural networks (CNNs), which is limited by the intrinsic locality in capturing the long-distance dependency. To this end, we propose a multi-modal transformer (MTrans), which is capable of transferring multi-scale features from the target modality to the auxiliary modality, for accelerated MR imaging. To capture deep multi-modal information, our MTrans utilizes an improved multi-head attention mechanism, named cross attention module, which absorbs features from the auxiliary modality that contribute to the target modality. Our framework provides three appealing benefits: (i) Our MTrans use an improved transformers for multi-modal MR imaging, affording more global information compared with existing CNN-based methods. (ii) A new cross attention module is proposed to exploit the useful information in each modality at different scales. The small patch in the target modality aims to keep more fine details, the large patch in the auxiliary modality aims to obtain high-level context features from the larger region and supplement the target modality effectively. (iii) We evaluate MTrans with various accelerated multi-modal MR imaging tasks, e.g., MR image reconstruction and super-resolution, where MTrans outperforms state-of-the-art methods on fastMRI and real-world clinical datasets.

Article Figures and data Abstract Editor's evaluation eLife digest Introduction Methods Results Discussion Data availability References Decision letter Author response Article and author information Metrics Abstract Background: Estimates of ‘brain-predicted age’ quantify apparent brain age compared to normative trajectories of neuroimaging features. The brain age gap (BAG) between predicted and chronological age is elevated in symptomatic Alzheimer disease (AD) but has not been well explored in presymptomatic AD. Prior studies have typically modeled BAG with structural MRI, but more recently other modalities, including functional connectivity (FC) and multimodal MRI, have been explored. Methods: We trained three models to predict age from FC, structural (S), or multimodal MRI (S+FC) in 390 amyloid-negative cognitively normal (CN/A−) participants (18–89 years old). In independent samples of 144 CN/A−, 154 CN/A+, and 154 cognitively impaired (CI; CDR > 0) participants, we tested relationships between BAG and AD biomarkers of amyloid and tau, as well as a global cognitive composite. Results: All models predicted age in the control training set, with the multimodal model outperforming the unimodal models. All three BAG estimates were significantly elevated in CI compared to controls. FC-BAG was significantly reduced in CN/A+ participants compared to CN/A−. In CI participants only, elevated S-BAG and S+FC BAG were associated with more advanced AD pathology and lower cognitive performance. Conclusions: Both FC-BAG and S-BAG are elevated in CI participants. However, FC and structural MRI also capture complementary signals. Specifically, FC-BAG may capture a unique biphasic response to presymptomatic AD pathology, while S-BAG may capture pathological progression and cognitive decline in the symptomatic stage. A multimodal age-prediction model improves sensitivity to healthy age differences. Funding: This work was supported by the National Institutes of Health (P01-AG026276, P01- AG03991, P30-AG066444, 5-R01-AG052550, 5-R01-AG057680, 1-R01-AG067505, 1S10RR022984-01A1, and U19-AG032438), the BrightFocus Foundation (A2022014F), and the Alzheimer’s Association (SG-20-690363-DIAN). Editor's evaluation This is a useful study exploring multi-modality brain age (structural plus resting state MRI) in people in the early stages or at risk of Alzheimer's disease. They found solid evidence that people with cognitive impairment had older-appearing brains and that older-appearing brains were related to Alzheimer's risk factors such as amyloid and tau deposition. Their data suggest that the multi-modality brain age model is more accurate than a unimodal structural MRI model. https://doi.org/10.7554/eLife.81869.sa0 Decision letter Reviews on Sciety eLife's review process eLife digest The brains of people with advanced Alzheimer’s disease often look older than expected based on the patients’ actual age. This ‘brain age gap’ (how old a brain appears compared to the person’s chronological age) can be calculated thanks to machine learning algorithms which analyse images of the organ to detect changes related to aging. Traditionally, these models have relied on images of the brain structure, such as the size and thickness of various brain areas; more recent models have started to use activity data, such as how different brain regions work together to form functional networks. While the brain age gap is a useful measure for researchers who investigate aging and disease, it is not yet helpful for clinicians. For example, it is unclear whether the machine learning algorithm could detect changes in the brains of individuals in the initial stages of Alzheimer’s disease, before they start to manifest cognitive symptoms. Millar et al. explored this question by testing whether models which incorporate structural and activity data could be more sensitive to these early changes. Three machine learning algorithms (relying on either structural data, activity data, or combination of both) were used to predict the brain ages of participants with no sign of disease; with biological markers of Alzheimer’s disease but preserved cognitive functions; and with marked cognitive symptoms of the condition. Overall, the combined model was slightly better at predicting the brain age of healthy volunteers, and all three models indicated that patients with dementia had a brain which looked older than normal. For this group, the model based on structural data was also able to make predictions which reflected the severity of cognitive decline. Crucially, the algorithm which used activity data predicted that, in individuals with biological markers of Alzheimer’s disease but no cognitive impairment, the brain looked in fact younger than chronological age. Exactly why this is the case remains unclear, but this signal may be driven by neural processes which unfold in the early stages of the disease. While more research is needed, the work by Millar et al. helps to explore how various types of machine learning models could one day be used to assess and predict brain health. Introduction Alzheimer disease (AD) is marked by structural and functional disruptions in the brain, some of which can be observed through multimodal magnetic resonance imaging (MRI) in preclinical and symptomatic stages of the disease (Frisoni et al., 2010; Brier et al., 2014a). More recently, the ‘brain-predicted age’ framework has emerged as a promising tool for neuroimaging analyses, leveraging recent developments and accessibility of machine-learning techniques, as well as large-scale, publicly available neuroimaging datasets (Cole and Franke, 2017b; Franke and Gaser, 2019). These models are trained to quantify how ‘old’ a brain appears, as compared to a normative sample of training data - typically consisting of cognitively normal participants across the adult lifespan (e.g., Cole et al., 2015). Thus, the framework allows for a residual-based interpretation of the brain age gap (BAG), defined as the difference between model-predicted age and chronological age, as an index of vulnerability and/or resistance to underlying disease pathology. Indeed, several studies have demonstrated that BAG is elevated (i.e. the brain ‘appears older’ than expected) in a host of neurological and psychiatric disorders, including symptomatic AD (Franke et al., 2010; Franke and Gaser, 2012; Gaser et al., 2013), as well as schizophrenia (e.g., Koutsouleris et al., 2014), HIV (e.g., Cole et al., 2017c), and type-2 diabetes (e.g., Franke et al., 2013), and moreover, predicts mortality (Cole et al., 2018). Conversely, lower BAG is associated with lower risk of disease progression (Gaser et al., 2013; Wang et al., 2019; Bocancea et al., 2021). Critically, at least one comparison suggests that BAG exceeds other established MRI (hippocampal volume) and CSF (pTau and Aβ42) biomarkers in sensitivity to AD progression (Gaser et al., 2013). Thus, by summarizing complex, non-linear, highly multivariate patterns of neuroimaging features into a simple, interpretable summary metric, BAG may reflect a comprehensive biomarker of brain health. Several studies have established that symptomatic AD and mild cognitive impairment (MCI) are associated with elevated BAG (Cole and Franke, 2017b; Franke and Gaser, 2019). However, the sensitivity of these model estimates to AD in the presymptmatic stage (i.e. present amyloid pathology in the absence of cognitive decline [Sperling et al., 2011]) is less clear. The development of sensitive, reliable, non-invasive biomarkers of preclinical AD pathology is critical for the assessment of individual AD risk, as well as the evaluation of AD clinical prevention trials. Recent studies have demonstrated that greater BAG is associated with greater amyloid PET burden in a Down syndrome cohort (Cole et al., 2017a) and with greater tau PET burden in sporadic MCI and symptomatic AD (Lee et al., 2022). One approach to maximize sensitivity of BAG to presymptomatic AD pathology may be to train brain age models exclusively on amyloid-negative participants. As undetected AD pathology might influence MRI measures, and thus confound effects otherwise attributed to ‘healthy aging’ (Brier et al., 2014b), including the patterns learned by a traditional brain age model, an alternative model trained on amyloid-negative participants only might be more sensitive to detect presymptomatic AD pathology as deviations in BAG. Indeed, one recent study demonstrated that an amyloid-negative trained brain age model (Ly et al., 2020) is more sensitive to progressive stages of AD than a typical amyloid-insensitive model (Cole et al., 2015). However, this comparison included amyloid-negative and amyloid-positive test samples from two separate cohorts and thus may be driven by cohort, scanner, and/or site differences. To validate the applicability of the brain-predicted age approach to presymptomatic AD, it is important to test a model’s sensitivity to amyloid status, as well as continuous relationships with AD biomarkers, within a single cohort. Another recent comparison demonstrated that both traditional and amyloid-negative trained brain age models were similarly related to molecular AD biomarkers, but that further attempts to ‘disentangle’ AD from brain age by including more advanced AD continuum participants in the training sample significantly reduced relationships between brain age and AD markers (Hwang et al., 2022). Thus, in this study, we will apply the amyloid-negative training approach to a multimodal MRI dataset in order to maximize sensitivity to AD pathology in the presymptomatic stage. Most of the brain-predicted age reports described above focused primarily on structural MRI. However, other studies have successfully modeled brain age using a variety of other modalities, including metabolic PET (Goyal et al., 2019; Lee et al., 2022), diffusion MRI (Cherubini et al., 2016; Petersen et al., 2022), and functional connectivity (FC) (Dosenbach et al., 2010; Liem et al., 2017; Eavani et al., 2018; Nielsen et al., 2019). Integration of multiple neuroimaging modalities may maximize sensitivity of BAG estimates to preclinical AD. Indeed, recent multimodal comparisons suggest that structural MRI and FC capture complementary age-related signals (Eavani et al., 2018; Dunås et al., 2021) and that age prediction may be improved by incorporating multiple modalities (Liem et al., 2017; Engemann et al., 2020). One recent study has shown that BAG estimates from an FC graph theory-based model are significantly elevated in autosomal dominant AD mutation carriers and are positively associated with amyloid PET (Gonneaud et al., 2021). Furthermore, we have recently demonstrated that FC correlation-based BAG estimates are surprisingly reduced in cognitively normal participants with evidence of amyloid pathology and elevated pTau, as well as in cognitively normal APOE ε4 carriers at genetic risk of AD (Millar et al., 2022). Thus, incorporating FC into BAG models may improve sensitivity to early AD. This project aimed to develop multimodal models of brain-predicted age, incorporating both FC and structural MRI. Participants with presymptomatic AD pathology were excluded from the training set to maximize sensitivity. We hypothesized that BAG estimates would be sensitive to the presence of AD biomarkers and early cognitive impairment. We further considered whether estimates were continuously associated with AD biomarkers of amyloid and tau, as well as cognition. We hypothesized that FC and structural MRI would capture complementary signals related to age and AD. Thus, we systematically compared models trained on unimodal FC, structural MRI, and combined modalities to test the added utility of multimodal integration in accurately predicting age and whether each modality captures unique relationships with AD biomarkers and cognition. Methods Participants We formed a training sample of healthy controls spanning the adult lifespan by combining structural and FC-MRI data from three sources, as described previously (Millar et al., 2022): the Charles F. and Joanne Knight AD Research Center (ADRC) at Washington University in St. Louis (WUSTL), healthy controls from studies in the Ances lab at WUSTL (Thomas et al., 2013; Petersen et al., 2021), and mutation-negative controls from the Dominantly Inherited Alzheimer Network (DIAN) study of autosomal dominant AD at multiple international sites including WUSTL (McKay et al., 2022). To minimize the likelihood of undetected AD pathology in our training set, participants over the age of 50 were only included in the training set if they were cognitively normal, as assessed by the Clinical Dementia Rating (CDR 0; Morris, 1993), and had at least one biomarker indicating the absence of amyloid pathology (CN/A−, see below). We excluded 59 participants who did not have available CDR or biomarker measures (see Figure 1—figure supplement 1). As CDR and amyloid biomarkers were not available in the Ances lab controls, we included only participants at or below age 50 from this cohort in the training set. These healthy control participants were randomly divided into a training set (~80%; N=390) and a held-out test set (~20%; N=97), which did not significantly differ in age, sex, education, or race, see Table 1. Table 1 Demographic information of the combined samples. MeasureTraining sets (total N=390)Test sets (total N=97) §Analysis sets (total N=452)Ances Controls(CN/<50)DIAN Controls(CN/A−)Knight ADRC Controls(CN/A−)Ances Controls(CN/<50)DIAN Controls(CN/A−)Knight ADRC Controls(CN/A−)CN/A−CN/A+CIN136120134382633144154154Age (mean, SD)29.92 (9.92)40.02 (10.26)64.97 (10.57)26.68 (7.11)41.46 (12.34)64.73 (10.57)66.93 (8.53)72.56 (7.15)‡75.67 (6.86) ‡CDR (N 0 / N 0.5 / N 1.0 / N 2.0)NA120 / 0 / 0 / 0134 / 0 / 0 / 0NA26 / 0 / 0 / 033 / 0 / 0 / 0144 / 0 / 0 / 0154 / 0 / 0 / 00 / 119 / 35 / 2Amyloid status (N + / N -)NA120 / 0134 / 0NA26 / 033 / 0144 / 00 / 1540 / 57Biomarkers available (N PET / CSF / both)NA30 / 6 / 7911 / 22 / 91NA3 / 1 / 215 / 0 / 2824 / 0 / 12017 / 0 / 13714 / 0 / 43APOE ε4 carrier status (N + / N -)NA76 / 4499 / 34NA19 / 728 / 5115 / 2971 / 83 ‡55 / 98 ‡MMSE (mean, SD)NANA29.26 (1.05)NANA29.45 (0.94)29.13 (1.17)28.97 (1.33)25.37 (3.55) ‡Sex (N female / N male)70 / 6485 / 3584 / 5019 / 1816 / 1022 / 1189 / 5591 / 6368 / 86†Years of education (mean, SD)13.68 (2.16)14.78 (3.04)16.16 (2.43)13.95 (1.99)14.92 (2.83)16.48 (2.43)15.71 (2.65)15.90 (2.64)15.05 (2.97)*Race (N American Indian or Alaska Native)100100000Race (N Asian)112000010Race (N Black)670201707171620Race (N Native Hawaiian or Other Pacifc Islander)200200000Race (N White)57118112172626127137134SiteWUSTLMultiple sitesWUSTLWUSTLMultiple sitesWUSTLWUSTLWUSTLWUSTLScannerSiemens TrioSiemens Trio / VerioSiemens Trio / BiographSiemens TrioSiemens Trio / VerioSiemens Trio / BiographSiemens Trio / BiographSiemens Trio / BiographSiemens Trio / BiographField strength3T3T3T3T3T3T3T3T3T CN = Cognitively Normal, <50 = less than age 50, A− = amyloid negative, A+ = amyloid positive, CI = cognitively Impaired, DIAN = Dominantly Inherited Alzheimer Network, ADRC = Alzheimer Disease Research Center, AD = Alzheimer disease, CDR = Clinical Dementia Rating, MMSE = Mini Mental State Examination, WUSTL = Washington University in St. Louis, T = Tesla. Group differences from the CN/A− analysis set were tested with t tests for continuous variables and χ2 tests for categorical variables. * p < 0.05, ^ p < 0.10. † p < 0.01. ‡ p < 0.001. § Test sets include randomly-selected, non-overlapping subsets of participants drawn from the same studies as the training sets. Finally, independent samples for hypothesis testing included three groups from the Knight ADRC: a randomly selected sample of 144 CN/A− controls who did not overlap with the training or testing sets, 154 CN/A+ participants, and 154 cognitively impaired (CI) participants (CDR > 0 with a biomarker measure consistent with amyloid pathology [see below] and/or a primary diagnosis of AD or uncertain dementia [McKhann et al., 2011]). See Table 1 for demographic details of each sample. All participants provided written informed consent in accordance with the Declaration of Helsinki and their local institutional review board. All procedures were approved by the Human Research Protection Office at WUSTL (IRB ID # 201204041). PET and CSF biomarkers Amyloid burden was imaged with PET using (11 C)-Pittsburgh Compound B (PIB; Klunk et al., 2004) or (18 F)-Florbetapir (AV45; Wong et al., 2010). Regional standard uptake ratios (SUVRs) were modeled from 30 to 60 min after injection for PIB and from 50 to 70 min for AV45, using cerebellar gray as the reference region (Su et al., 2013). Regions of interest were segmented automatically using FreeSurfer 5.3 (Fischl, 2012). Global amyloid burden was defined as the mean of partial-volume-corrected (PVC) SUVRs from bilateral precuneus, superior and rostral middle frontal, lateral and medial orbitofrontal, and superior and middle temporal regions (Su et al., 2013). Amyloid summary SUVRs were harmonized across tracers using a centiloid conversion (Su et al., 2018). Tau deposition was imaged with PET using (18 F)-Flortaucipir (AV-1451; Chien et al., 2013). Regional SUVRs were modeled from 80 to 100 min after injection, using cerebellar gray as the reference region. A tau summary measure was defined in the mean PVC SUVRs from bilateral amygdala, entorhinal, inferior temporal, and lateral occipital regions (Mishra et al., 2017). CSF was collected via lumbar puncture using methods described previously (Fagan et al., 2006). After overnight fasting, 20–30 mL samples of CSF were collected, centrifuged, then aliquoted (500 µL) in polypropylene tubes, and stored at –80°C. CSF amyloid β peptide 42 (Aβ42), Aβ40, and phosphorylated tau-181 (pTau) were measured with automated Lumipulse immunoassays (Fujirebio, Malvern, PA, USA) using a single lot of assays for each analyte. Aβ42 and pTau estimates were each normalized for individual differences in CSF production rates by forming a ratio with Aβ40 as the denominator (Hansson et al., 2019; Guo et al., 2020). As pTau/Aβ40 was highly skewed, we applied a log transformation to these estimates before statistical analysis. Amyloid positivity was defined using previously published cutoffs for PIB (SUVR > 1.42; Vlassenko et al., 2016) or AV45 (SUVR > 1.19; Su et al., 2019). Additionally, the CSF Aβ42/Aβ40 ratio has been shown to be highly concordant with amyloid PET (positivity cutoff < 0.0673; Schindler et al., 2018; Volluz et al., 2021). Thus, participants were defined as amyloid-positive (for CN/A+ and CI groups) if they had either a PIB, AV45, or CSF Aβ42/Aβ40 ratio measure in the positive range. Participants with discordant positivity between PET and CSF estimates were defined as amyloid-positive. Cognitive battery Knight ADRC participants completed a 2 hr battery of cognitive tests. We examined global cognition by forming a composite of tasks across cognitive domains, including processing speed (Trail Making A; Schindler et al., 2018), executive function (Trail Making B; Schindler et al., 2018), semantic fluency (Animal Naming; Armitage, 1946), and episodic memory (Free and Cued Selective Reminding Test free recall score; Goodglass and Kaplan, et al., This composite has recently been used to study individual differences in cognition in the preclinical AD biomarkers and structural MRI et al., 2018), as well as functional MRI measures (Millar et al., 2021). MRI All MRI data were using a scanner, was a variety of models within and across As described previously (Millar et al., 2022), participants in the Knight ADRC and Ances lab studies completed one of two structural MRI by with = or = or = or = of = or 1 or with = = 1 and an imaging with = = = of = for two 6 min of DIAN participants completed a = = = of = et al., 2022). for the DIAN participants across sites and with the difference min of see 1 for summary of structural and functional MRI et al., 2022). FC and features All MRI data were using as described previously et al., 2010; Millar et al., 2022), including and of to a of et al., 2012). to an on independent samples of either younger or CN older was using a of the functional with the and was included in a single that a of the in As described previously et al., Millar et al., 2022), processing was to for Data based on estimates > and/or of > above To further minimize the influence of on FC estimates et al., in all we only included with < and > after data a temporal < < and including from FreeSurfer (Fischl, brain and as well as the of these signals. Finally, data were at data were across within a set of regions of interest in and cerebellar et al., 2020). For each we calculated the of the between all We then used the of each as features for predicting age. site and/or differences between samples might confound neuroimaging we harmonized FC using an approach et al., et al., which has previously been applied to FC data et al., 2018). MRI processing and features All images and structural through a with FreeSurfer 5.3 et al., 2012). processing included of and gray to a and of the based on the et al., 2006). and of and were and by a of trained research to (Su et al., 2013). We then used the thickness estimates from regions et al., with estimates from regions et al., as features for predicting age. We harmonized structural features across sites and using the same approach et al., et al., which has also been applied to structural MRI data et al., 2018). process As described previously (Millar et al., 2022), machine-learning were using the in 2021). We trained two process et al., 2004) each with a function to predict chronological age using harmonized MRI features or in the training set. The was within each model by a of from to using across 100 training The of for each model was found (see Figure 1—figure supplement and was applied for all of that model. All other were set to function = and = in the training set was assessed using via the the of the mean and between chronological age and the age predictions across the We then of the models to predict age in data by the trained models to the held-out test set of healthy controls. Finally, we applied the same models to separate analysis sets of 154 154 CN/A+, and 144 CN/A− controls to test our effects and individual difference models were each with a single model. The multimodal model was by the predictions from each unimodal model as features for training a model (Liem et al., 2017; Engemann et al., Dunås et al., 2021). For each we calculated BAG estimates as the difference between chronological age and age predictions from the unimodal FC model structural model and multimodal model To for observed in models et al., 2018; et al., 2019; et al., we included chronological age as a in all statistical tests of BAG (Cole et al., et al., 2018). However, to estimates of prediction et al., 2021), only age prediction were used for model in the training and test sets. analysis All statistical were in 2020). Demographic differences in the AD samples were tested with t tests for continuous variables and χ2 tests for categorical using CN/A− controls as a reference in brain age model were tested using test of difference between one between age and each model prediction of age. To for age-related in BAG et al., 2018; as previously we for age as a all statistical tests. Group differences in each BAG were tested using an test with t tests on BAG using a for multiple of were tested by of of of were tested with models tested the effects of cognitive impairment (CDR > 0 CDR 0) and amyloid positivity on BAG estimates from each model, for age sex, and years of the influence of on measures et al., 2012; et al., 2012; et al., we also included mean as an of in the FC and S+FC models. We tested continuous relationships with AD biomarkers and cognitive estimates using including the same demographic and the of amyloid biomarkers was reduced in the CN/A− we excluded these participants from models testing continuous amyloid were as Results and Demographic of the training sets, test sets, and analysis sets are in Table 1. CN/A+ participants were older = p < and more to be APOE ε4 carriers = p < than amyloid-negative controls. Furthermore, CI participants were older = p < more = p = more to be APOE ε4 carriers = p < and had years of education = p < and lower MMSE = p < than amyloid-negative controls. of model All models accurately predicted chronological age in the training sets, as

Article Figures and data Abstract Editor's evaluation eLife digest Introduction Methods Results Discussion Data availability References Decision letter Author response Article and author information Metrics Abstract Background: Estimates of ‘brain-predicted age’ quantify apparent brain age compared to normative trajectories of neuroimaging features. The brain age gap (BAG) between predicted and chronological age is elevated in symptomatic Alzheimer disease (AD) but has not been well explored in presymptomatic AD. Prior studies have typically modeled BAG with structural MRI, but more recently other modalities, including functional connectivity (FC) and multimodal MRI, have been explored. Methods: We trained three models to predict age from FC, structural (S), or multimodal MRI (S+FC) in 390 amyloid-negative cognitively normal (CN/A−) participants (18–89 years old). In independent samples of 144 CN/A−, 154 CN/A+, and 154 cognitively impaired (CI; CDR > 0) participants, we tested relationships between BAG and AD biomarkers of amyloid and tau, as well as a global cognitive composite. Results: All models predicted age in the control training set, with the multimodal model outperforming the unimodal models. All three BAG estimates were significantly elevated in CI compared to controls. FC-BAG was significantly reduced in CN/A+ participants compared to CN/A−. In CI participants only, elevated S-BAG and S+FC BAG were associated with more advanced AD pathology and lower cognitive performance. Conclusions: Both FC-BAG and S-BAG are elevated in CI participants. However, FC and structural MRI also capture complementary signals. Specifically, FC-BAG may capture a unique biphasic response to presymptomatic AD pathology, while S-BAG may capture pathological progression and cognitive decline in the symptomatic stage. A multimodal age-prediction model improves sensitivity to healthy age differences. Funding: This work was supported by the National Institutes of Health (P01-AG026276, P01- AG03991, P30-AG066444, 5-R01-AG052550, 5-R01-AG057680, 1-R01-AG067505, 1S10RR022984-01A1, and U19-AG032438), the BrightFocus Foundation (A2022014F), and the Alzheimer’s Association (SG-20-690363-DIAN). Editor's evaluation This is a useful study exploring multi-modality brain age (structural plus resting state MRI) in people in the early stages or at risk of Alzheimer's disease. They found solid evidence that people with cognitive impairment had older-appearing brains and that older-appearing brains were related to Alzheimer's risk factors such as amyloid and tau deposition. Their data suggest that the multi-modality brain age model is more accurate than a unimodal structural MRI model. https://doi.org/10.7554/eLife.81869.sa0 Decision letter Reviews on Sciety eLife's review process eLife digest The brains of people with advanced Alzheimer’s disease often look older than expected based on the patients’ actual age. This ‘brain age gap’ (how old a brain appears compared to the person’s chronological age) can be calculated thanks to machine learning algorithms which analyse images of the organ to detect changes related to aging. Traditionally, these models have relied on images of the brain structure, such as the size and thickness of various brain areas; more recent models have started to use activity data, such as how different brain regions work together to form functional networks. While the brain age gap is a useful measure for researchers who investigate aging and disease, it is not yet helpful for clinicians. For example, it is unclear whether the machine learning algorithm could detect changes in the brains of individuals in the initial stages of Alzheimer’s disease, before they start to manifest cognitive symptoms. Millar et al. explored this question by testing whether models which incorporate structural and activity data could be more sensitive to these early changes. Three machine learning algorithms (relying on either structural data, activity data, or combination of both) were used to predict the brain ages of participants with no sign of disease; with biological markers of Alzheimer’s disease but preserved cognitive functions; and with marked cognitive symptoms of the condition. Overall, the combined model was slightly better at predicting the brain age of healthy volunteers, and all three models indicated that patients with dementia had a brain which looked older than normal. For this group, the model based on structural data was also able to make predictions which reflected the severity of cognitive decline. Crucially, the algorithm which used activity data predicted that, in individuals with biological markers of Alzheimer’s disease but no cognitive impairment, the brain looked in fact younger than chronological age. Exactly why this is the case remains unclear, but this signal may be driven by neural processes which unfold in the early stages of the disease. While more research is needed, the work by Millar et al. helps to explore how various types of machine learning models could one day be used to assess and predict brain health. Introduction Alzheimer disease (AD) is marked by structural and functional disruptions in the brain, some of which can be observed through multimodal magnetic resonance imaging (MRI) in preclinical and symptomatic stages of the disease (Frisoni et al., 2010; Brier et al., 2014a). More recently, the ‘brain-predicted age’ framework has emerged as a promising tool for neuroimaging analyses, leveraging recent developments and accessibility of machine-learning techniques, as well as large-scale, publicly available neuroimaging datasets (Cole and Franke, 2017b; Franke and Gaser, 2019). These models are trained to quantify how ‘old’ a brain appears, as compared to a normative sample of training data - typically consisting of cognitively normal participants across the adult lifespan (e.g., Cole et al., 2015). Thus, the framework allows for a residual-based interpretation of the brain age gap (BAG), defined as the difference between model-predicted age and chronological age, as an index of vulnerability and/or resistance to underlying disease pathology. Indeed, several studies have demonstrated that BAG is elevated (i.e. the brain ‘appears older’ than expected) in a host of neurological and psychiatric disorders, including symptomatic AD (Franke et al., 2010; Franke and Gaser, 2012; Gaser et al., 2013), as well as schizophrenia (e.g., Koutsouleris et al., 2014), HIV (e.g., Cole et al., 2017c), and type-2 diabetes (e.g., Franke et al., 2013), and moreover, predicts mortality (Cole et al., 2018). Conversely, lower BAG is associated with lower risk of disease progression (Gaser et al., 2013; Wang et al., 2019; Bocancea et al., 2021). Critically, at least one comparison suggests that BAG exceeds other established MRI (hippocampal volume) and CSF (pTau and Aβ42) biomarkers in sensitivity to AD progression (Gaser et al., 2013). Thus, by summarizing complex, non-linear, highly multivariate patterns of neuroimaging features into a simple, interpretable summary metric, BAG may reflect a comprehensive biomarker of brain health. Several studies have established that symptomatic AD and mild cognitive impairment (MCI) are associated with elevated BAG (Cole and Franke, 2017b; Franke and Gaser, 2019). However, the sensitivity of these model estimates to AD in the presymptmatic stage (i.e. present amyloid pathology in the absence of cognitive decline [Sperling et al., 2011]) is less clear. The development of sensitive, reliable, non-invasive biomarkers of preclinical AD pathology is critical for the assessment of individual AD risk, as well as the evaluation of AD clinical prevention trials. Recent studies have demonstrated that greater BAG is associated with greater amyloid PET burden in a Down syndrome cohort (Cole et al., 2017a) and with greater tau PET burden in sporadic MCI and symptomatic AD (Lee et al., 2022). One approach to maximize sensitivity of BAG to presymptomatic AD pathology may be to train brain age models exclusively on amyloid-negative participants. As undetected AD pathology might influence MRI measures, and thus confound effects otherwise attributed to ‘healthy aging’ (Brier et al., 2014b), including the patterns learned by a traditional brain age model, an alternative model trained on amyloid-negative participants only might be more sensitive to detect presymptomatic AD pathology as deviations in BAG. Indeed, one recent study demonstrated that an amyloid-negative trained brain age model (Ly et al., 2020) is more sensitive to progressive stages of AD than a typical amyloid-insensitive model (Cole et al., 2015). However, this comparison included amyloid-negative and amyloid-positive test samples from two separate cohorts and thus may be driven by cohort, scanner, and/or site differences. To validate the applicability of the brain-predicted age approach to presymptomatic AD, it is important to test a model’s sensitivity to amyloid status, as well as continuous relationships with AD biomarkers, within a single cohort. Another recent comparison demonstrated that both traditional and amyloid-negative trained brain age models were similarly related to molecular AD biomarkers, but that further attempts to ‘disentangle’ AD from brain age by including more advanced AD continuum participants in the training sample significantly reduced relationships between brain age and AD markers (Hwang et al., 2022). Thus, in this study, we will apply the amyloid-negative training approach to a multimodal MRI dataset in order to maximize sensitivity to AD pathology in the presymptomatic stage. Most of the brain-predicted age reports described above focused primarily on structural MRI. However, other studies have successfully modeled brain age using a variety of other modalities, including metabolic PET (Goyal et al., 2019; Lee et al., 2022), diffusion MRI (Cherubini et al., 2016; Petersen et al., 2022), and functional connectivity (FC) (Dosenbach et al., 2010; Liem et al., 2017; Eavani et al., 2018; Nielsen et al., 2019). Integration of multiple neuroimaging modalities may maximize sensitivity of BAG estimates to preclinical AD. Indeed, recent multimodal comparisons suggest that structural MRI and FC capture complementary age-related signals (Eavani et al., 2018; Dunås et al., 2021) and that age prediction may be improved by incorporating multiple modalities (Liem et al., 2017; Engemann et al., 2020). One recent study has shown that BAG estimates from an FC graph theory-based model are significantly elevated in autosomal dominant AD mutation carriers and are positively associated with amyloid PET (Gonneaud et al., 2021). Furthermore, we have recently demonstrated that FC correlation-based BAG estimates are surprisingly reduced in cognitively normal participants with evidence of amyloid pathology and elevated pTau, as well as in cognitively normal APOE ε4 carriers at genetic risk of AD (Millar et al., 2022). Thus, incorporating FC into BAG models may improve sensitivity to early AD. This project aimed to develop multimodal models of brain-predicted age, incorporating both FC and structural MRI. Participants with presymptomatic AD pathology were excluded from the training set to maximize sensitivity. We hypothesized that BAG estimates would be sensitive to the presence of AD biomarkers and early cognitive impairment. We further considered whether estimates were continuously associated with AD biomarkers of amyloid and tau, as well as cognition. We hypothesized that FC and structural MRI would capture complementary signals related to age and AD. Thus, we systematically compared models trained on unimodal FC, structural MRI, and combined modalities to test the added utility of multimodal integration in accurately predicting age and whether each modality captures unique relationships with AD biomarkers and cognition. Methods Participants We formed a training sample of healthy controls spanning the adult lifespan by combining structural and FC-MRI data from three sources, as described previously (Millar et al., 2022): the Charles F. and Joanne Knight AD Research Center (ADRC) at Washington University in St. Louis (WUSTL), healthy controls from studies in the Ances lab at WUSTL (Thomas et al., 2013; Petersen et al., 2021), and mutation-negative controls from the Dominantly Inherited Alzheimer Network (DIAN) study of autosomal dominant AD at multiple international sites including WUSTL (McKay et al., 2022). To minimize the likelihood of undetected AD pathology in our training set, participants over the age of 50 were only included in the training set if they were cognitively normal, as assessed by the Clinical Dementia Rating (CDR 0; Morris, 1993), and had at least one biomarker indicating the absence of amyloid pathology (CN/A−, see below). We excluded 59 participants who did not have available CDR or biomarker measures (see Figure 1—figure supplement 1). As CDR and amyloid biomarkers were not available in the Ances lab controls, we included only participants at or below age 50 from this cohort in the training set. These healthy control participants were randomly divided into a training set (~80%; N=390) and a held-out test set (~20%; N=97), which did not significantly differ in age, sex, education, or race, see Table 1. Table 1 Demographic information of the combined samples. MeasureTraining sets (total N=390)Test sets (total N=97) §Analysis sets (total N=452)Ances Controls(CN/<50)DIAN Controls(CN/A−)Knight ADRC Controls(CN/A−)Ances Controls(CN/<50)DIAN Controls(CN/A−)Knight ADRC Controls(CN/A−)CN/A−CN/A+CIN136120134382633144154154Age (mean, SD)29.92 (9.92)40.02 (10.26)64.97 (10.57)26.68 (7.11)41.46 (12.34)64.73 (10.57)66.93 (8.53)72.56 (7.15)‡75.67 (6.86) ‡CDR (N 0 / N 0.5 / N 1.0 / N 2.0)NA120 / 0 / 0 / 0134 / 0 / 0 / 0NA26 / 0 / 0 / 033 / 0 / 0 / 0144 / 0 / 0 / 0154 / 0 / 0 / 00 / 119 / 35 / 2Amyloid status (N + / N -)NA120 / 0134 / 0NA26 / 033 / 0144 / 00 / 1540 / 57Biomarkers available (N PET / CSF / both)NA30 / 6 / 7911 / 22 / 91NA3 / 1 / 215 / 0 / 2824 / 0 / 12017 / 0 / 13714 / 0 / 43APOE ε4 carrier status (N + / N -)NA76 / 4499 / 34NA19 / 728 / 5115 / 2971 / 83 ‡55 / 98 ‡MMSE (mean, SD)NANA29.26 (1.05)NANA29.45 (0.94)29.13 (1.17)28.97 (1.33)25.37 (3.55) ‡Sex (N female / N male)70 / 6485 / 3584 / 5019 / 1816 / 1022 / 1189 / 5591 / 6368 / 86†Years of education (mean, SD)13.68 (2.16)14.78 (3.04)16.16 (2.43)13.95 (1.99)14.92 (2.83)16.48 (2.43)15.71 (2.65)15.90 (2.64)15.05 (2.97)*Race (N American Indian or Alaska Native)100100000Race (N Asian)112000010Race (N Black)670201707171620Race (N Native Hawaiian or Other Pacifc Islander)200200000Race (N White)57118112172626127137134SiteWUSTLMultiple sitesWUSTLWUSTLMultiple sitesWUSTLWUSTLWUSTLWUSTLScannerSiemens TrioSiemens Trio / VerioSiemens Trio / BiographSiemens TrioSiemens Trio / VerioSiemens Trio / BiographSiemens Trio / BiographSiemens Trio / BiographSiemens Trio / BiographField strength3T3T3T3T3T3T3T3T3T CN = Cognitively Normal, <50 = less than age 50, A− = amyloid negative, A+ = amyloid positive, CI = cognitively Impaired, DIAN = Dominantly Inherited Alzheimer Network, ADRC = Alzheimer Disease Research Center, AD = Alzheimer disease, CDR = Clinical Dementia Rating, MMSE = Mini Mental State Examination, WUSTL = Washington University in St. Louis, T = Tesla. Group differences from the CN/A− analysis set were tested with t tests for continuous variables and χ2 tests for categorical variables. * p < 0.05, ^ p < 0.10. † p < 0.01. ‡ p < 0.001. § Test sets include randomly-selected, non-overlapping subsets of participants drawn from the same studies as the training sets. Finally, independent samples for hypothesis testing included three groups from the Knight ADRC: a randomly selected sample of 144 CN/A− controls who did not overlap with the training or testing sets, 154 CN/A+ participants, and 154 cognitively impaired (CI) participants (CDR > 0 with a biomarker measure consistent with amyloid pathology [see below] and/or a primary diagnosis of AD or uncertain dementia [McKhann et al., 2011]). See Table 1 for demographic details of each sample. All participants provided written informed consent in accordance with the Declaration of Helsinki and their local institutional review board. All procedures were approved by the Human Research Protection Office at WUSTL (IRB ID # 201204041). PET and CSF biomarkers Amyloid burden was imaged with PET using (11 C)-Pittsburgh Compound B (PIB; Klunk et al., 2004) or (18 F)-Florbetapir (AV45; Wong et al., 2010). Regional standard uptake ratios (SUVRs) were modeled from 30 to 60 min after injection for PIB and from 50 to 70 min for AV45, using cerebellar gray as the reference region (Su et al., 2013). Regions of interest were segmented automatically using FreeSurfer 5.3 (Fischl, 2012). Global amyloid burden was defined as the mean of partial-volume-corrected (PVC) SUVRs from bilateral precuneus, superior and rostral middle frontal, lateral and medial orbitofrontal, and superior and middle temporal regions (Su et al., 2013). Amyloid summary SUVRs were harmonized across tracers using a centiloid conversion (Su et al., 2018). Tau deposition was imaged with PET using (18 F)-Flortaucipir (AV-1451; Chien et al., 2013). Regional SUVRs were modeled from 80 to 100 min after injection, using cerebellar gray as the reference region. A tau summary measure was defined in the mean PVC SUVRs from bilateral amygdala, entorhinal, inferior temporal, and lateral occipital regions (Mishra et al., 2017). CSF was collected via lumbar puncture using methods described previously (Fagan et al., 2006). After overnight fasting, 20–30 mL samples of CSF were collected, centrifuged, then aliquoted (500 µL) in polypropylene tubes, and stored at –80°C. CSF amyloid β peptide 42 (Aβ42), Aβ40, and phosphorylated tau-181 (pTau) were measured with automated Lumipulse immunoassays (Fujirebio, Malvern, PA, USA) using a single lot of assays for each analyte. Aβ42 and pTau estimates were each normalized for individual differences in CSF production rates by forming a ratio with Aβ40 as the denominator (Hansson et al., 2019; Guo et al., 2020). As pTau/Aβ40 was highly skewed, we applied a log transformation to these estimates before statistical analysis. Amyloid positivity was defined using previously published cutoffs for PIB (SUVR > 1.42; Vlassenko et al., 2016) or AV45 (SUVR > 1.19; Su et al., 2019). Additionally, the CSF Aβ42/Aβ40 ratio has been shown to be highly concordant with amyloid PET (positivity cutoff < 0.0673; Schindler et al., 2018; Volluz et al., 2021). Thus, participants were defined as amyloid-positive (for CN/A+ and CI groups) if they had either a PIB, AV45, or CSF Aβ42/Aβ40 ratio measure in the positive range. Participants with discordant positivity between PET and CSF estimates were defined as amyloid-positive. Cognitive battery Knight ADRC participants completed a 2 hr battery of cognitive tests. We examined global cognition by forming a composite of tasks across cognitive domains, including processing speed (Trail Making A; Schindler et al., 2018), executive function (Trail Making B; Schindler et al., 2018), semantic fluency (Animal Naming; Armitage, 1946), and episodic memory (Free and Cued Selective Reminding Test free recall score; Goodglass and Kaplan, et al., This composite has recently been used to study individual differences in cognition in the preclinical AD biomarkers and structural MRI et al., 2018), as well as functional MRI measures (Millar et al., 2021). MRI All MRI data were using a scanner, was a variety of models within and across As described previously (Millar et al., 2022), participants in the Knight ADRC and Ances lab studies completed one of two structural MRI by with = or = or = or = of = or 1 or with = = 1 and an imaging with = = = of = for two 6 min of DIAN participants completed a = = = of = et al., 2022). for the DIAN participants across sites and with the difference min of see 1 for summary of structural and functional MRI et al., 2022). FC and features All MRI data were using as described previously et al., 2010; Millar et al., 2022), including and of to a of et al., 2012). to an on independent samples of either younger or CN older was using a of the functional with the and was included in a single that a of the in As described previously et al., Millar et al., 2022), processing was to for Data based on estimates > and/or of > above To further minimize the influence of on FC estimates et al., in all we only included with < and > after data a temporal < < and including from FreeSurfer (Fischl, brain and as well as the of these signals. Finally, data were at data were across within a set of regions of interest in and cerebellar et al., 2020). For each we calculated the of the between all We then used the of each as features for predicting age. site and/or differences between samples might confound neuroimaging we harmonized FC using an approach et al., et al., which has previously been applied to FC data et al., 2018). MRI processing and features All images and structural through a with FreeSurfer 5.3 et al., 2012). processing included of and gray to a and of the based on the et al., 2006). and of and were and by a of trained research to (Su et al., 2013). We then used the thickness estimates from regions et al., with estimates from regions et al., as features for predicting age. We harmonized structural features across sites and using the same approach et al., et al., which has also been applied to structural MRI data et al., 2018). process As described previously (Millar et al., 2022), machine-learning were using the in 2021). We trained two process et al., 2004) each with a function to predict chronological age using harmonized MRI features or in the training set. The was within each model by a of from to using across 100 training The of for each model was found (see Figure 1—figure supplement and was applied for all of that model. All other were set to function = and = in the training set was assessed using via the the of the mean and between chronological age and the age predictions across the We then of the models to predict age in data by the trained models to the held-out test set of healthy controls. Finally, we applied the same models to separate analysis sets of 154 154 CN/A+, and 144 CN/A− controls to test our effects and individual difference models were each with a single model. The multimodal model was by the predictions from each unimodal model as features for training a model (Liem et al., 2017; Engemann et al., Dunås et al., 2021). For each we calculated BAG estimates as the difference between chronological age and age predictions from the unimodal FC model structural model and multimodal model To for observed in models et al., 2018; et al., 2019; et al., we included chronological age as a in all statistical tests of BAG (Cole et al., et al., 2018). However, to estimates of prediction et al., 2021), only age prediction were used for model in the training and test sets. analysis All statistical were in 2020). Demographic differences in the AD samples were tested with t tests for continuous variables and χ2 tests for categorical using CN/A− controls as a reference in brain age model were tested using test of difference between one between age and each model prediction of age. To for age-related in BAG et al., 2018; as previously we for age as a all statistical tests. Group differences in each BAG were tested using an test with t tests on BAG using a for multiple of were tested by of of of were tested with models tested the effects of cognitive impairment (CDR > 0 CDR 0) and amyloid positivity on BAG estimates from each model, for age sex, and years of the influence of on measures et al., 2012; et al., 2012; et al., we also included mean as an of in the FC and S+FC models. We tested continuous relationships with AD biomarkers and cognitive estimates using including the same demographic and the of amyloid biomarkers was reduced in the CN/A− we excluded these participants from models testing continuous amyloid were as Results and Demographic of the training sets, test sets, and analysis sets are in Table 1. CN/A+ participants were older = p < and more to be APOE ε4 carriers = p < than amyloid-negative controls. Furthermore, CI participants were older = p < more = p = more to be APOE ε4 carriers = p < and had years of education = p < and lower MMSE = p < than amyloid-negative controls. of model All models accurately predicted chronological age in the training sets, as

To investigate the association between cognitive impairment and multimodal magnetic resonance imaging (MRI) markers in patients with cerebral microbleeds (CMBs), and to identify imaging predictors of CMB-related cognitive dysfunction. This retrospective case-control study included 71 patients with CMBs confirmed by MRI between August 2022 and February 2024 and 65 age-matched healthy controls. Cognitive function was assessed using the Montreal Cognitive Assessment (MoCA). All participants underwent multimodal MRI, including susceptibility-weighted imaging (SWI) for CMB detection and diffusion tensor imaging (DTI) for quantitative assessment of fractional anisotropy (FA) and apparent diffusion coefficient (ADC) in predefined brain regions. Spearman correlation analysis was performed to evaluate associations between CMB burden, DTI parameters, and cognitive scores. Multivariate linear regression analysis was used to identify independent risk factors for cognitive impairment. Compared with controls, patients with CMBs had significantly lower MoCA scores and higher CMB burden (both P < 0.05). FA values in the frontal-temporal lobe, parietal lobe, and basal ganglia were significantly reduced, while ADC values were increased (all P < 0.05). CMB number was negatively correlated with MoCA scores (r = -0.643, P < 0.001). Decreased FA and increased ADC in the frontal-temporal lobe and basal ganglia were significantly associated with cognitive decline. Regression analysis showed that CMB burden ≥10, reduced FA, elevated ADC, and basal ganglia involvement were independent risk factors for cognitive impairment. Cognitive impairment in CMB patients is closely associated with lesion burden and microstructural white matter alterations detected by multimodal MRI. These imaging markers may facilitate early risk stratification and targeted monitoring, although longitudinal validation is warranted.

The underlying functional alterations of brain structural changes among patients with empathy impairment following stroke remain unclear. We sought to investigate functional connectivity changes informed by brain structural abnormalities in multimodal magnetic resonance imaging (MRI) among patients with empathy impairment following stroke. We enrolled people who had experienced their first ischemic stroke, along with healthy controls. We assessed empathy 3 months after stroke using the Chinese version of the Empathy Quotient (EQ). During the acute phase, all patients underwent basic magnetic resonance imaging (MRI), followed by multimodal MRI during follow-up. Our MRI analyses encompassed acute infarction segmentation, volumetric brain measurements, regional quantification of diffusion parameters, and both region-of-interest-based and seed-based functional connectivity assessments. We grouped patients based on the severity of their empathy impairment for comparative analysis. We included 84 patients who had stroke and 22 healthy controls. Patients had lower EQ scores than controls. Patients with low empathy had larger left cortical infarcts (odds ratio [OR] 4.082, 95% confidence interval [CI] 1.183-14.088), more pronounced atrophy in the right cingulate cortex (OR 1.248, 95% CI 1.038-1.502), and lower scores on the Montreal Cognitive Assessment (OR 0.873, 95% CI 0.74-0.947). In addition, the cingulate cortex served as the seed in the seed-based analysis, which showed heightened functional connectivity between the anterior cingulate gyrus and the right superior parietal lobule, specifically in the low-empathy group. We did not evaluate the relationship between specific network involvement and empathy impairment among patients following stroke. Among patients with subacute ischemic stroke, reduced empathy was strongly associated with a more severe cognitive profile and atrophy of the right cingulate cortex. Our subsequent structural-informed functional MRI analysis suggests that the enhanced connectivity between the anterior cingulate gyrus and the superior parietal lobule may function as a compensatory mechanism for this atrophy.

The heterogeneity of cerebral small vessel disease (CSVD) with mild cognitive impairment (MCI) presents a challenge for diagnosis and classification. This study aims to propose a multimodal magnetic resonance imaging (MRI)-based machine learning framework to effectively classify MCI and NCI in CSVD patients. We enrolled 165 CSVD patients, categorized into NCI (n = 81) and MCI (n = 84) groups based on neurocognitive assessments. Multimodal MRI data, including T1-weighted, resting-state functional MRI, and diffusion tensor images, were collected. Image preprocessing, feature extraction and selection were applied to obtain MRI features from three modalities. The AutoGluon platform was utilized for model development, and traditional machine learning algorithms were applied for comparison. The models were validated using a validation cohort of 83 CSVD patients, and their performance was assessed via receiver operating characteristic curve analysis. The AutoGluon model to distinguish MCI from NCI based on multimodal MRI features demonstrated high area under the curve (AUC), accuracy, sensitivity, specificity, precision, balanced accuracy, and F1-score in the training cohort (0.926, 88.48%, 88.10%, 88.89%, 89.16%, 88.50%, and 88.63%, respectively) and validation cohort (0.878, 81.93%, 86.36%, 76.92%, 80.85%, 81.64%, and 83.51%, respectively). Other traditional machine learning models had AUCs of 0.755–0.831, and their prediction accuracies were significantly lower than that of AutoGluon model (P < 0.001). Our study provides a multimodal MRI-based machine learning framework, utilizing the AutoGluon platform, that outperforms traditional algorithms in classifying MCI and NCI, offering a promising tool for the early prediction of MCI in CSVD.

This research was conducted to explore the value of multimodal magnetic resonance imaging (MRI) based on the alternating direction algorithm in the diagnosis of early cervical cancer. 64 patients diagnosed with early cervical cancer clinicopathologically were included, and according to the examination methods, they were divided into A group with conventional multimodal MRI examination and B group with the multimodal MRI examination under the alternating direction algorithm. The diagnostic results of two types of multimodal MRI for early cervical cancer staging were compared with the results of clinicopathological examination to judge the application value in the early diagnosis of cervical cancer. The results showed that in the 6 randomly selected samples of early cervical cancer patients, the peak signal-to-noise ratio (PSNR) and structural similarity image measurement (SSIM) of multimodal MRI images under the alternating direction algorithm were significantly higher than those of conventional multimodal MRI images and the image reconstruction was clearer under this algorithm. By comparing MRI multimodal staging, statistical analysis showed that the staging accuracy of B group was 75%, while that of A group was only 59.38%. For the results of postoperative medical examinations, the examination consistency of B group was better than that of A group, with a statistically significant difference (P < 0.05). The area under the receiver operating characteristic (ROC) curve (AUC) of B group was larger than that of A group; thus, sensitivity was improved and misdiagnosis was reduced significantly. Multimodal MRI under the alternating direction algorithm was superior to conventional multimodal MRI examination in the diagnosis of early cervical cancer, as the lesions were displayed more clearly, which was conducive to the detection rate of small lesions and the staging accuracy. Therefore, it could be used as an ideal MRI method for the assistant diagnosis of cervical cancer staging.

This case report aims to present a rare case of endometrial carcinosarcoma, a highly malignant tumor with a poor prognosis. The primary objective is to describe this unique case's clinical presentation, multimodal magnetic resonance imaging (MRI) features, typical histopathological characteristics and surgical treatment. A detailed analysis of the patient's medical history, preoperative imaging evaluation, and treatment approach was conducted. This case report includes high-resolution images and figures, showcasing MRI scans, surgical treatment, and histopathology slides related to the case. The case report outlines imaging findings of a rare case of endometrial carcinosarcoma. Multimodal imaging such as T1-weighted imaging (T1WI), T2-weighted imaging (T2WI) and multi-b-value diffusion-weighted imaging (DWI), apparent diffusion coefficient (ADC) and dynamic contrast-enhanced (DCE) scanning could accurately identify the histopathological features of the case. Surgical resection is the best treatment, and preoperative imaging evaluation should be particularly important. This case report highlights endometrial carcinosarcoma's rarity and diagnostic challenges. Multimodal MRI has significant value in diagnosing endometrial carcinosarcoma. This technology not only improves the sensitivity, specificity, and accuracy of diagnosis, but also helps to more accurately evaluate the staging and grading of tumors. By comparing imaging features and pathological results, studies have found that multimodal MRI can clearly show the anatomical structure, pathological nature, and extent of the tumor, with a high degree of consistency with the pathological diagnosis. In particular, when differentiating endometrial carcinosarcoma from low-risk endometrial cancer, multimodal MRI combined with serum carbohydrate antigen 125 (CA125) and E-box binding zinc finger protein 1 (ZEB1) detection can further improve the sensitivity and specificity of differential diagnosis. In addition, research has found that the ADC value of the tumor tissue in different pathological grades is related to the multimodal MRI, which helps to better understand the biological behavior and prognosis of the tumor. In summary, multimodal MRI is an effective diagnostic tool that can provide important evidence for the precise diagnosis and treatment of endometrial carcinosarcoma.

To report a patient with Wernicke's encephalopathy (WE) using multimodal magnetic resonance imaging (MRI) including conventional MRI, diffusion-weighted MRI (DWI), arterial spin labeling (ASL), and proton MR spectroscopy (MRS). A 50-year-old woman of WE with a history of cholecystectomy and acute pancreatitis was given MRI scans including DWI, MRS, and ASL pre- and post-thiamine treatment. Two weeks after admission, the patient's condition rapidly improved. The typical MRI findings and lesions in the frontal cortex at baseline disappeared or resolved partially. The reduced apparent diffusion coefficient value in part of the thalamus lesion, the elevated cerebral blood flow in the frontal cortex, the lactate doublet peak in the right thalamus lesion, and in cerebral spinal fluid, all resolved after treatment. The combination of conventional MRI with DWI, proton MRS, and ASL, offers a powerful diagnostic tool and a better understanding of the pathophysiological and hemodynamic mechanisms.