Structural Neuroimaging Data Research Articles

MRI-based Whole-Brain Elastography and Volumetric Measurements to Predict Brain Age

Abstract Brain age, as a correlate of an individual’s chronological age obtained from structural and functional neuroimaging data, enables assessing developmental or neurodegenerative pathology relative to the overall population. Accurately inferring brain age from brain magnetic resonance imaging (MRI) data requires imaging methods sensitive to tissue health and sophisticated statistical models to identify the underlying age-related brain changes. Magnetic resonance elastography (MRE) is a specialized MRI technique which has emerged as a reliable, non-invasive method to measure the brain’s mechanical properties, such as the viscoelastic shear stiffness and damping ratio. These mechanical properties have been shown to change across the life span, reflect neurodegenerative diseases, and are associated with individual differences in cognitive function. Here, we aim to develop a machine learning framework to accurately predict a healthy individual’s chronological age from maps of brain mechanical properties. This framework can later be applied to understand neurostructural deviations from normal in individuals with neurodevelopmental or neurodegenerative conditions. Using 3D convolutional networks as deep learning models and more traditional statistical models, we relate chronological age as a function of multiple modalities of whole-brain measurements: stiffness, damping ratio, and volume. Evaluations on held-out subjects show that combining stiffness and volume in a multimodal approach achieves the most accurate predictions. Interpretation of the different models highlight important regions that are distinct between the modalities. The results demonstrate the complementary value of MRE measurements in brain age models, which, in future studies, could improve model sensitivity to brain integrity differences in individuals with neuropathology.

Neuro connect: Integrating data-driven and BiGRU classification for enhanced autism prediction from fMRI data

ABSTRACT Autism Spectrum Disorder (ASD) poses a significant challenge in early diagnosis and intervention due to its multifaceted clinical presentation and lack of objective biomarkers. This research presents a novel approach, termed Neuro Connect, which integrates data-driven techniques with Bidirectional Gated Recurrent Unit (BiGRU) classification to enhance the prediction of ASD using functional Magnetic Resonance Imaging (fMRI) data. This study uses both structural and functional neuroimaging data to investigate the complex brain underpinnings of autism spectrum disorder (ASD). They use an Auto-Encoder (AE) to efficiently reduce dimensionality while retaining critical information by learning and compressing important characteristics from high-dimensional data. We treat the feature-extracted data using a BiGRU model for the classification task of predicting ASD. They provide a new optimization strategy, the Horse Herd Algorithm (HHA), and show that it outperforms other established optimizers, such SGD and Adam, in order to improve classification accuracy. The model’s performance is greatly enhanced by the HHA’s novel optimization technique, which more precisely refines weight modifications made during training. The proposed ASD and EEG dataset accuracy value is 99.5%, and 99.3 compared to the existing method the proposed has a high accuracy value.

Brain-age prediction: Systematic evaluation of site effects, and sample age range and size.

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.

Cerebellum in Alzheimer's disease and other neurodegenerative diseases: an emerging research frontier.

The cerebellum is crucial for both motor and nonmotor functions. Alzheimer's disease (AD), alongside other dementias such as vascular dementia (VaD), Lewy body dementia (DLB), and frontotemporal dementia (FTD), as well as other neurodegenerative diseases (NDs) like Parkinson's disease (PD), amyotrophic lateral sclerosis (ALS), Huntington's disease (HD), and spinocerebellar ataxias (SCA), are characterized by specific and non-specific neurodegenerationsin central nervous system. Previously, the cerebellum's significance in these conditions was underestimated. However, advancing research has elevated its profile as a critical node in disease pathology. We comprehensively review the existing evidence to elucidate the relationship between cerebellum and the aforementioned diseases. Our findings reveal a growing body of research unequivocally establishing a link between the cerebellum and AD, other forms of dementia, and other NDs, supported by clinical evidence, pathological and biochemical profiles, structural and functional neuroimaging data, and electrophysiological findings. By contrasting cerebellar observations with those from the cerebral cortex and hippocampus, we highlight the cerebellum's distinct role in the disease processes. Furthermore, we also explore the emerging therapeutic potential of targeting cerebellum for the treatment of these diseases. This review underscores the importance of the cerebellum in these diseases, offering new insights into the disease mechanisms and novel therapeutic strategies.

The effect of head motion on brain age prediction using deep convolutional neural networks

Deep learning can be used effectively to predict participants' age from brain magnetic resonance imaging (MRI) data, and a growing body of evidence suggests that the difference between predicted and chronological age—referred to as brain-predicted age difference (brain-PAD)—is related to various neurological and neuropsychiatric disease states. A crucial aspect of the applicability of brain-PAD as a biomarker of individual brain health is whether and how brain-predicted age is affected by MR image artifacts commonly encountered in clinical settings. To investigate this issue, we trained and validated two different 3D convolutional neural network architectures (CNNs) from scratch and tested the models on a separate dataset consisting of motion-free and motion-corrupted T1-weighted MRI scans from the same participants, the quality of which were rated by neuroradiologists from a clinical diagnostic point of view. Our results revealed a systematic increase in brain-PAD with worsening image quality for both models. This effect was also observed for images that were deemed usable from a clinical perspective, with brains appearing older in medium than in good quality images. These findings were also supported by significant associations found between the brain-PAD and standard image quality metrics indicating larger brain-PAD for lower-quality images. Our results demonstrate a spurious effect of advanced brain aging as a result of head motion and underline the importance of controlling for image quality when using brain-predicted age based on structural neuroimaging data as a proxy measure for brain health.

Computational limits to the legibility of the imaged human brain

Our knowledge of the organisation of the human brain at the population-level is yet to translate into power to predict functional differences at the individual-level, limiting clinical applications and casting doubt on the generalisability of inferred mechanisms. It remains unknown whether the difficulty arises from the absence of individuating biological patterns within the brain, or from limited power to access them with the models and compute at our disposal.Here we comprehensively investigate the resolvability of such patterns with data and compute at unprecedented scale. Across 23 810 unique participants from UK Biobank, we systematically evaluate the predictability of 25 individual biological characteristics, from all available combinations of structural and functional neuroimaging data. Over 4526 GPU*hours of computation, we train, optimize, and evaluate out-of-sample 700 individual predictive models, including fully-connected feed-forward neural networks of demographic, psychological, serological, chronic disease, and functional connectivity characteristics, and both uni- and multi-modal 3D convolutional neural network models of macro- and micro-structural brain imaging.We find a marked discrepancy between the high predictability of sex (balanced accuracy 99.7%), age (mean absolute error 2.048 years, R2 0.859), and weight (mean absolute error 2.609Kg, R2 0.625), for which we set new state-of-the-art performance, and the surprisingly low predictability of other characteristics. Neither structural nor functional imaging predicted an individual's psychology better than the coincidence of common chronic disease (p < 0.05). Serology predicted chronic disease (p < 0.05) and was best predicted by it (p < 0.001), followed by structural neuroimaging (p < 0.05).Our findings suggest either more informative imaging or more powerful models will be needed to decipher individual level characteristics from the human brain. We make our models and code openly available.

480 Neuropsychiatric Symptom Clusters in behavioral variant frontotemporal dementia: The Role of Early Anxiety/Depression on Functional Progression

OBJECTIVES/GOALS: To identify empiric neuropsychiatric symptom (NPS) clusters in behavioral variant frontotemporal dementia and to determine the role of early anxiety/depression on functional progression. METHODS/STUDY POPULATION: Analyses were conducted using data from the ARTFL-LEFFTDS Longitudinal Frontotemporal Lobar Degeneration (ALLFTD) study, an established consortium with an ongoing cohort study of FTD patients across 18 clinical sites which includes comprehensive cognitive, neuropsychiatric, and structural neuroimaging data. A polychoric cluster analysis was performed on subjects from the ALLFTD cohort [applewebdata%3A//044E463E-34DA-4677-9EDC-B8309D14C337#_msocom_1] with early-stage disease (N=145, male 61%, median age 62 years) in order to identify empiric NPS clusters. Cox proportional hazard regression was then used to examine the association between early affective symptoms in bvFTD and subsequent functional disabilities adjusted for age, sex, level of education, and FTLD CDR global score. RESULTS/ANTICIPATED RESULTS: We identified a four-factor model as the best fit for the data: (1) an affective cluster with prominent depression, anxiety, agitation, and irritability, (2) a disinhibited symptom cluster with prominent elation and disinhibition, (3) an obsessive symptom cluster with prominent obsessive/ritualistic behavior and hyperorality, and (4) a psychotic symptom cluster with prominent delusions and hallucinations. The hazard of developing impairments in transactions, language, self-care, meal preparation, and incontinence was significantly elevated in those with early affective symptoms (depression/anxiety). DISCUSSION/SIGNIFICANCE: In this study we show that, NPS cluster into four discrete groups: (1) affective symptoms, (2) disinhibited symptoms, (3) obsessive symptoms, and (4) psychotic symptoms. Anxiety and depression are prominent within the affective symptom cluster and are associated with accelerated functional decline in a number of domains.

The superior frontal sulcus in the human brain: Morphology and probability maps.

The superior frontal sulcus (SFS) is the major sulcus on the dorsolateral frontal cortex that defines the lateral limit of the superior frontal gyrus. Caudally, it originates near the superior precentral sulcus (SPRS) and, rostrally, it terminates near the frontal pole. The advent of structural neuroimaging has demonstrated significant variability in this sulcus that is not captured by the classic sulcal maps. The present investigation examined the morphological variability of the SFS in 50 individual magnetic resonance imaging (MRI) scans of the human brain that were registered to the Montreal Neurological Institute (MNI) standard stereotaxic space. Two primary morphological patterns were identified: (i) the SFS was classified as a continuous sulcus or (ii) the SFS was a complex of sulcal segments. The SFS showed a high probability of merging with neighbouring sulci on the superior and middle frontal gyri and these patterns were documented. In addition, the morphological variability and spatial extent of the SFS were quantified using volumetric and surface spatial probability maps. The results from the current investigation provide an anatomical framework for understanding the morphology of the SFS, which is critical for the interpretation of structural and functional neuroimaging data in the dorsolateral frontal region, as well as for improving the accuracy of neurosurgical interventions.

Where alcohol use disorder meets interoception: A meta-analytic view on structural and functional neuroimaging data.

Alcohol use disorder (AUD) has been associated with changes in the processing of internal body signals, known as interoception. Changes in brain structure, particularly in the insula, are thought to underlie impaired interoception. As studies specifically investigating this association are largely lacking, this analysis takes an approach that compares meta-analytic results on interoception with recently published meta-analytic results on gray matter reduction in AUD. A systematic literature search identified 25 eligible interoception studies. Activation likelihood estimation (ALE) was used to test for spatial convergence of study results. Overlap between interoception and AUD clusters was tested using conjunction analysis. Meta-analytic connectivity modeling (MACM) and resting-state functional connectivity were used to identify the functional network of interoception and to test where this network overlapped with AUD meta-analytic clusters. The results were characterized using behavioral domain analysis. The interoception ALE identified a cluster in the left middle insula. There was no overlap with clusters of reduced gray matter in AUD. MACM analysis of the interoception cluster revealed a large network located in the insulae, thalami, basal nuclei, cingulate and medial frontal cortices, and pre- and postcentral gyri. Resting state analysis confirmed this result, showing the strongest connections to nodes of the salience- and somatomotor network. Five of the eight clusters that showed a structural reduction in AUD were located within these networks. The behavioral profiles of these clusters were suggestive of higher-level processes such as salience control, somatomotor functions, and skin sensations. The results suggest an altered salience mapping of interoceptive signals in AUD, consistent with current models. Connections to the somatomotor network may be related to action control and integration of skin sensations. Mindfulness-based interventions, pleasurable touch, and (deep) transcranial magnetic stimulation may be targeted interventions that reduce interoceptive deficits in AUD and thus contribute to drug use reduction and relapse prevention.

The Cerebellum and Cognitive Function: Anatomical Evidence from a Transdiagnostic Sample

Multiple lines of evidence across human functional, lesion, and animal data point to a cerebellar role, in particular of crus I, crus II, and lobule VIIB, in cognitive function. However, a mapping of distinct facets of cognitive function to cerebellar structure is missing. We analyzed structural neuroimaging data from the Healthy Brain Network (HBN). Cerebellar parcellation was performed with a validated automated segmentation pipeline (CERES) and stringent visual quality check (n = 662 subjects retained from initial n = 1452). Canonical correlation analyses (CCA) examined regional gray matter volumetric (GMV) differences in association to cognitive function (quantified with NIH Toolbox Cognition domain, NIH-TB), accounting for psychopathology severity, age, sex, scan location, and intracranial volume. Multivariate CCA uncovered a significant correlation between two components entailing a latent cognitive canonical (NIH-TB subscales) and a brain canonical variate (cerebellar GMV and intracranial volume, ICV), surviving bootstrapping and permutation procedures. The components correspond to partly shared cerebellar-cognitive function relationship with a first map encompassing cognitive flexibility (r = 0.89), speed of processing (r = 0.65), and working memory (r = 0.52) associated with regional GMV in crus II (r = 0.57) and lobule X (r = 0.59) and a second map including the crus I (r = 0.49) and lobule VI (r = 0.49) associated with working memory (r = 0.51). We show evidence for a structural subspecialization of the cerebellum topography for cognitive function in a transdiagnostic sample.

Deep learning based normative models identify structures that become abnormal with Alzheimer’s disease

AbstractBackgroundImproving dementia diagnosis is a major global concern, given that up to 20% of subjects are misdiagnosed. Normative modelling, which quantifies how far individuals deviate from healthy controls (HC), may help to better situate subjects along the spectrum. To determine whether this is the case, we implemented a deep learning based normative model and tested it on structural neuroimaging data from subjects with HC, mild cognitive impairment (MCI), and Alzheimer’s disease (AD).MethodWe developed an adversarial autoencoder (AAE) (Figure 1) leveraging FreeSurfer features, namely cortical surface area of 68 cortical subregions (34 by hemisphere; Desikan‐Killiany atlas) and volumes of 33 neuroanatomical structures (Aseg atlas). Our model not only returns individual “deviation” scores—divergence from HC—but also identifies regions of interest that contribute to said score, facilitating interpretability. We retrieved T1‐weighted MRI scans (n = 204) from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (HC/MCI/AD 105/29/60; Table 1). We trained our AAE on 71 HC samples (n = 71) and tested it on the rest (n = 133). We used bootstrapping to compute confidence intervals (CI) for each point estimate and examined regions where scores differed for the considered groups [1].ResultDeviation scores were lowest for HC (mean 0.74 [95%‐CI 0.63–0.88]), highest for AD (mean 1.21 [95%‐CI 1.03–1.50]), and intermediate for MCI (mean 1.04 [95%‐CI 0.88–1.27]), implying subjects indeed fell along the AD spectrum (Figure 2A). Said scores also enabled classifying subjects into HC, MCI, and AD with a mean area under the curve (AUC) of 75% (Figure 2B‐C; HC vs. MCI: 0.75 [95%‐CI 0.62–0.85]; HC vs. AD: 0.76 [95%‐CI 0.64–0.82]). According to the AAE, the hippocampus, parahippocampal cortex, ventricles, and inferior parietal lobe contributed to such differences (Figure 2D‐E).ConclusionNormative models can identify structures that become abnormal as the brain undergoes ageing and AD, in line with the literature. Further testing on a larger and more heterogeneous sample will reveal the potential of this tool and its potential deployment in clinics.

Poster Symposium: How Well do Western Methods Used to Assess Atypical Aging in Western Countries Generalize to Sub-Saharan African Countries?

Summary Abstract:Risk factors associated with development of neurodegenerative disease has been well-studied in Western and European populations. However, there has been considerably less research in the assessment of such risk factors in developing countries, notably sub-Saharan Africa. There is a paucity of data at the micro level (e.g. neuroimaging and biomarker data) and macro level (e.g. cognitive assessment and psychosocial/environmental risk factors) for development of neurodegenerative conditions in these populations.This symposium examines Western methods of assessment of risk factors and cognitive profiles of older adults at risk for neurodegenerative disorder to determine if they are relevant to sub-Saharan African populations, specifically older Congolese adults. This symposium utilizes an older adult sample that has been comprehensively assessed both at the cellular level (via blood biomarkers and neuroimaging typically used for assessment of dementia in Western populations), to the individual functional level (via cognitive assessment), to finally, psychosocial and environmental risk factors for dementia seen at a community level. First, Dr. Ikanga will present on the association between performance on the African Neuropsychological Battery (ANB) with biomarkers specific to Alzhemer's disease and more general vascular risk factors for cognitive decline. Second, Dr. Hickle will present on structural neuroimaging data of mesial temporal lobe atrophy in comparison with performance on the ANB. Dr. Reyes will then discuss the utility of a cognitive screener developed for use in Sub-Saharan Africa on older adults from the Democratic Republic of Congo, with specific emphasis on educational corrections. Finally, Dr. De Wit will discuss health and psychosocial predictors of depressive symptoms as well as the relation between depressive symptoms and neuropsychological functioning in Congolese older adults, to determine if neurocognitive profiles are similar in Sub-Saharan Africa relative to Western populations. This "micro to macro" approach is unique in providing a comprehensive overview of risks associated with dementia in Congolese adults. This is the first study of its kind to utilize a multi-method approach for older adults at risk for dementia in Sub-Saharan Africa, and results suggest that some approaches are more valid in this population than others. Future areas of research will be discussed, as well as feasibility and validity of Western approaches in assessment of dementia to non-Western populations.

ALE meta-analyses of voxel-based morphometry studies: Parameter validation via large-scale simulations

Activation likelihood estimation (ALE) meta-analysis has been applied to structural neuroimaging data since long, but up to now, any systematic assessment of the algorithm's behavior, power and sensitivity has been based on simulations using functional neuroimaging databases as their foundation. Here, we aimed to determine whether the guidelines offered by previous evaluations can be generalized to ALE meta-analyses of voxel-based morphometry (VBM) studies. We ran 365000 distinct ALE analyses filled with simulated experiments, randomly sampling parameters from BrainMap's VBM experiment database. We then examined the algorithm's sensitivity, its susceptibility to spurious convergence, and its susceptibility to excessive contributions by individual experiments. In general, the performance of the ALE algorithm was highly comparable between imaging modalities, with the algorithm's sensitivity and specificity reaching similar levels with structural data as previously observed with functional data. Because of the lower number of foci reported and the higher number of participants usually included in structural experiments, individual studies had, on average, a higher impact towards significant clusters. To prevent significant clusters from being driven by single experiments, we recommend that researchers include at least 23 experiments in a VBM ALE dataset, instead of the previously recommended minimum of n = 17. While these recommendations do not constitute hard borders, running ALE analyses on smaller datasets would require special diligence in assessing and reporting the contributions of experiments to individual clusters.

The subjective amplitude of the diurnal rhythm matters – Chronobiological insights for neuroimaging studies

Multiple aspects of human psychophysiology, including mood and cognition, are subjected to diurnal rhythms. While the previous magnetic resonance imaging (MRI) studies have focused solely on the morningness-eveningness (ME) preference dichotomy, i.e. the circadian phase, the second key dimension of the diurnal rhythms, i.e. the strength of these preferences (amplitude; AM), has been completely overlooked. Uncovering the neural correlates of AM is especially important considering its link with negative emotionality. Structural T1-weighted neuroimaging data from 79 early (EC) and 74 late (LC) chronotypes were analysed to compare grey matter (GM) volume and cortical thickness. The study aimed to elucidate whether the subjective AM and its interaction with ME was a significant predictor of individual brain structure. Both GM volume and cortical thickness of the left primary visual cortex was negatively correlated with AM scores across the entire sample. Furthermore, EC and LC differed in their association between AM scores and the GM volume in the right middle temporal gyrus, with the positive and negative correlations reported respectively in the two groups. The current study underlines the importance of the visual system in circadian rhythmicity and provides possible neural correlates for AM-related differences in negative affect processing. Furthermore, the presence of the opposite correlations between brain anatomy and AM in the two groups suggests that the behavioural and neuronal chronotype differences might become more pronounced in individuals with extreme diurnal differences in mood and cognition, highlighting the necessity to additionally account for AM in neuroimaging studies.

Emotion regulation and reactivity are associated with cortical thickness in early to mid-childhood.

This study explored the neural correlates of emotion regulation and emotional reactivity in early to mid-childhood. A sample of 96 children (70% White, mid-to-high socioeconomic status) aged 3-8years provided structural neuroimaging data and caregivers reported on emotion regulation and emotional reactivity. The amygdala, insula, inferior frontal gyrus, anterior cingulate cortex, and medial orbitofrontal cortex were explored as a priori regions of interest (ROIs). ROI analyses revealed that emotion regulation was positively associated with cortical thickness in the insula, whereas emotional reactivity was negatively associated with cortical thickness in the inferior frontal gyrus. Exploratory whole-brain analyses suggested positive associations between emotion regulation and both left superior temporal thickness and right inferior temporal thickness, as well as negative associations between emotional reactivity and left superior temporal thickness. There were no significant associations between emotional regulation or reactivity and amygdala volume or cortical surface area. These findings support the notion that surface area and cortical thickness are distinct measures of brain maturation. In sum, these findings suggest that children may rely on a wider set of neural regions for emotion regulation and reactivity than adults, which is consistent with theories of interactive specialization across the life span.

Functional activity of the caudate mediates the relation between early childhood microstructural variations and elevated metabolic syndrome scores

BackgroundMetabolic syndrome score in children assesses the risk of developing cardiovascular disease in future. We aim to probe the role of the caudate in relation to the metabolic syndrome score. Furthermore, using both functional and structural neuroimaging, we aim to examine the interplay between functional and structural measures. MethodsA longitudinal birth cohort study with functional and structural neuroimaging data obtained at 4.5, 6.0 and 7.5 years and metabolic syndrome scores at 8.0 years was used. Pearson correlation and linear regression was used to test for correlation fractional anisotropy (FA) and fractional amplitude of low frequency fluctuations (fALFF) of the caudate with metabolic syndrome scores. Mediation analysis was used to test if later brain measures mediated the relation between earlier brain measures and metabolic syndrome scores. Inhibitory control was also tested as a mediator of the relation between caudate brain measures and metabolic syndrome scores. ResultsFA at 4.5 years and fALFF at 7.5 years of the left caudate was significantly correlated with metabolic syndrome scores. Post-hoc mediation analysis showed that fALFF at 7.5 years fully mediated the relation between FA at 4.5 years and metabolic syndrome scores. Inhibitory control was significantly correlated with fALFF at 7.5 years, but did not mediate the relation between fALFF at 7.5 years and metabolic syndrome scores. ConclusionsWe found that variations in caudate microstructure at 4.5 years predict later variation in functional activity at 7.5 years. This later variation in functional activity fully mediates the relation between microstructural changes in early childhood and metabolic syndrome scores at 8.0 years.

Utilizing diverse methods to characterize puberty: Three investigations across development

The talks in this symposium will present diverse methods of characterizing pubertal development and utilize these diverse characterizations to understand the broader developmental changes of adolescence. The first paper will present data from a study of brain development during the earliest stages of puberty and highlight the role of adrenal androgens in promoting neurodevelopment. Structural neuroimaging, pubertal staging, and hormone data (dehydroepiandrosterone-sulfate [DHEAS] and dehydroepiandrosterone [DHEA]) were collected from 92 prepubertal children (Tanner stage = 1, chronological age = 8-9 years old). The results demonstrated that DHEAS was associated with grey matter volume in prepubertal children, while DHEA was associated with cortical thickness. These findings implicate DHEA(S) in the neurodevelopmental plasticity typically associated with gonadarche. The second talk in this symposium will present longitudinal data on cross-talk between the hypothalamic-pituitary-adrenal (HPA) and hypothalamic-pituitary-gonadal (HPG) axes (also known as hormone coupling) across the pubertal transition. A community sample of 437 children provided diurnal saliva samples at ages nine and 12, and saliva was assayed for cortisol, DHEA, and testosterone. Results indicated that cortisol-DHEA coupling varied by sex and developmental stage, as males initially showed positive cortisol-DHEA coupling that became progressively more negative as they aged, while females demonstrated more negative DHEA-cortisol coupling that became progressively more positive as they aged. However, cortisol-testosterone coupling did not vary by sex or pubertal status, demonstrating positive associations at age nine which strengthened by age 12. These findings contribute to a fuller understanding of hormone coupling during the early stages of puberty and across development. Finally, the third presentation will present longitudinal data from a community sample of male children first assessed prior to puberty (mean age = 7.44 years) and then assessed six and seven years later. Utilizing Tanner staging, self- and parent-report measures of pubertal development (Pubertal Development Scale; PDS), and hormone assaying for DHEA and testosterone, the authors explored the relationship between pubertal development and psychiatric symptoms over time. Overall, they found that earlier pubertal timing predicted greater symptom severity. However, while higher DHEA levels predicted lower symptom severity, higher testosterone values were associated with elevated symptom severity. The overarching goal of this symposium is to demonstrate that puberty is not a singular event that should be measured unidimensionally, but a multi-faceted process that has down-stream implications for brain development, coordination across biological systems, and psychological health.

Association of Dual-Task Gait Cost and White Matter Hyperintensity Burden Poststroke: Results From the ONDRI

Background Acute change in gait speed while performing a mental task [dual-task gait cost (DTC)], and hyperintensity magnetic resonance imaging signals in white matter are both important disability predictors in older individuals with history of stroke (poststroke). It is still unclear, however, whether DTC is associated with overall hyperintensity volume from specific major brain regions in poststroke. Methods This is a cohort study with a total of 123 older (69 ± 7 years of age) participants with history of stroke were included from the Ontario Neurodegenerative Disease Research Initiative. Participants were clinically assessed and had gait performance assessed under single- and dual-task conditions. Structural neuroimaging data were analyzed to measure both, white matter hyperintensity (WMH) and normal appearing volumes. Percentage of WMH volume in frontal, parietal, occipital, and temporal lobes as well as subcortical hyperintensities in basal ganglia + thalamus were the main outcomes. Multivariate models investigated associations between DTC and hyperintensity volumes, adjusted for age, sex, years of education, global cognition, vascular risk factors, APOE4 genotype, residual sensorimotor symptoms from previous stroke and brain volume. Results There was a significant positive global linear association between DTC and hyperintensity burden (adjusted Wilks’ λ = .87, P = .01). Amongst all WMH volumes, hyperintensity burden from basal ganglia + thalamus provided the most significant contribution to the global association (adjusted β = .008, η2 = .03; P = .04), independently of brain atrophy. Conclusions In poststroke, increased DTC may be an indicator of larger white matter damages, specifically in subcortical regions, which can potentially affect the overall cognitive processing and decrease gait automaticity by increasing the cortical control over patients’ locomotion.

Gender Diversity and Brain Morphology Among Adolescents

Gender-diverse youths have higher rates of mental health problems compared with the general population, as shown in both clinical and nonclinical populations. Brain correlates of gender diversity, however, have been reported only among youths with gender dysphoria or in transgender individuals. To examine brain morphologic correlates of gender diversity among adolescents from a general pediatric population who were assigned male or female at birth, separately. This cross-sectional study was embedded in Generation R, a multiethnic population-based study conducted in Rotterdam, the Netherlands. Adolescents who were born between April 1, 2002, and January 31, 2006, and had information on self-reported or parent-reported gender diversity and structural neuroimaging at ages 13 to 15 years were included. Data analysis was performed from April 1 to July 31, 2022. Gender-diverse experiences among adolescents were measured with selected items from the Achenbach System of Empirically Based Assessment forms and the Gender Identity/Gender Dysphoria Questionnaire for Adolescents and Adults, as reported by adolescents and/or their parents. High-resolution structural neuroimaging data were collected using a 3-T magnetic resonance imaging scanner (at a single site). We used linear regression models to examine differences in global brain volumetric measures between adolescents who reported gender diversity and those who did not. This study included 2165 participants, with a mean (SD) age of 13.8 (0.6) years at scanning. A total of 1159 participants (53.5%) were assigned female at birth and 1006 (46.5%) were assigned male at birth. With regard to maternal country of origin, 1217 mothers (57.6%) were from the Netherlands and 896 (42.4%) were from outside the Netherlands. Adolescents who reported gender diversity did not differ in global brain volumetric measures from adolescents who did not report gender diversity. In whole-brain, vertexwise analyses among adolescents assigned male at birth, thicker cortices in the left inferior temporal gyrus were observed among youths who reported gender diversity compared with those who did not. No associations were observed between gender diversity and surface area in vertexwise analyses. The findings of this cross-sectional study suggest that global brain volumetric measures did not differ between adolescents who reported gender diversity and those who did not. However, these findings further suggest that gender diversity in the general population correlates with specific brain morphologic features in the inferior temporal gyrus among youths who are assigned male at birth. Replication of these findings is necessary to elucidate the potential neurobiological basis of gender diversity in the general population. Future longitudinal studies should also investigate the directionality of these associations.

Biological brain age prediction using machine learning on structural neuroimaging data: Multi-cohort validation against biomarkers of Alzheimer's disease and neurodegeneration stratified by sex.

Brain-age can be inferred from structural neuroimaging and compared to chronological age (brain-age delta) as a marker of biological brain aging. Accelerated aging has been found in neurodegenerative disorders like Alzheimer's disease (AD), but its validation against markers of neurodegeneration and AD is lacking. Here, imaging-derived measures from the UK Biobank dataset (N=22,661) were used to predict brain-age in 2,314 cognitively unimpaired (CU) individuals at higher risk of AD and mild cognitive impaired (MCI) patients from four independent cohorts with available biomarker data: ALFA+, ADNI, EPAD, and OASIS. Brain-age delta was associated with abnormal amyloid-β, more advanced stages (AT) of AD pathology and APOE-ε4 status. Brain-age delta was positively associated with plasma neurofilament light, a marker of neurodegeneration, and sex differences in the brain effects of this marker were found. These results validate brain-age delta as a non-invasive marker of biological brain aging in non-demented individuals with abnormal levels of biomarkers of AD and axonal injury.