Asymmetric thinning of the cerebral cortex across the adult lifespan is accelerated in Alzheimer's disease

Inspired by pathogenic mechanisms: A novel gradual multi-modal fusion framework for mild cognitive impairment diagnosis

Mild cognitive impairment (MCI) is a precursor to Alzheimer's disease (AD), and its progression involves complex pathogenic mechanisms. Specifically, disturbed by gene variants, the regulation of gene expression ultimately changes brain structure, resulting in the progression of brain diseases. However, the existing works rarely take these mechanisms into account when designing their diagnosis methods. Therefore, we propose a novel gradual multi-modal fusion framework to fuse representative data from each stage of disease progression in hybrid feature space, including single nucleotide polymorphism (SNP), gene expression (GE), and magnetic resonance imaging (MRI). Specifically, to integrate genetic sequence and expression data, we design a SNP-GE fusion module, which performs multi-modal fusion to obtain genetic embedding by considering the relation between SNP and GE. Compared with SNP-GE fusion, representation of genetic embedding and MRI have more obvious heterogeneity, especially correlation with disease. Therefore, we propose to align the manifold of genetic and imaging representations, which can explore the high-order relationship between imaging and genetic data in the presence of modal heterogeneity. Our proposed framework was validated using the Alzheimer's Disease Neuroimaging Initiative dataset, and achieved diagnosis accuracy of 76.88%, 72.84%, 87.72%, and 95.00% for distinguishing MCI from control normal, lately MCI from early MCI, MCI from AD, and AD from control normal, respectively. Additionally, our proposed framework helps to identify some multi-modal biomarkers related to MCI progression. In summary, our proposed framework is effective not only for MCI diagnosis but also for guiding the further development of genetic and imaging-based brain studies. Our code is published at https://github.com/tianxu8822/workflow_MCI/tree/main/.

Hemispheric asymmetry in language-related brain regions

Structural asymmetries of the human brain have been widely studied in previous research. However, there is a lack of consistency across studies in terms of whether brain regions are larger in the left hemisphere than the right (leftward asymmetry), larger in the right hemisphere than the left (rightward asymmetry), or similar in both hemispheres (no asymmetry). Moreover, some of the existing studies exploring brain asymmetry were based on only small sample sizes and/or restricted to younger participants. Thus, here we analysed brain asymmetry in a well-powered sample (n = 532) later in life (mean age: 67 years). Given that language is known to be strongly lateralized in the brain, the current study focused on regions related to language. When assessing cortical volumes and surface areas, we observed significant leftward asymmetries for the superior temporal gyrus, superior temporal sulcus, supramarginal gyrus, pars opercularis, transverse gyrus, and temporal gyrus, whereas the pars triangularis showed a significant rightward asymmetry. In contrast, when assessing cortical thickness, we detected a significant leftward asymmetry for the pars triangularis and a significant rightward asymmetry for the superior temporal sulcus. The present observations on asymmetry in language-related brain regions in a large sample of older but neurologically healthy participants may serve as a normative framework against which data from clinical samples can be compared.

Association of Hypoxemia Due to Obstructive Sleep Apnea With White Matter Hyperintensities and Temporal Lobe Changes in Older Adults

BACKGROUND AND OBJECTIVES

Cerebral small vessel disease (CSVD) is a leading cause of cognitive decline and functional loss in older adults. Obstructive sleep apnea (OSA) is common in older adults, can increase cerebrovascular disease risk, and is linked to medial temporal lobe (MTL) degeneration and cognitive impairment. However, the interaction between OSA features and CSVD burden and their combined effect on MTL structure and function are not well understood. This study tested the hypothesis that CSVD burden is a candidate mechanism linking OSA to MTL degeneration and impaired memory in older adults.

METHODS

Cognitively unimpaired older adults from the Biomarker Exploration in Aging, Cognition, and Neurodegeneration cohort were recruited for an observational, in-lab overnight polysomnography (PSG) study with emotional mnemonic discrimination ability assessed before and after sleep. Participants had no neurologic or psychiatric disorders and were not on sleep-affecting medications. PSG-derived OSA variables included apnea-hypopnea index (AHI), total arousal index, and minimum SpO2. MRI was used to assess global and lobar white matter hyperintensity (WMH) volumes and MTL structure (hippocampal volume; entorhinal cortex [ERC] thickness) at an earlier time point. Regressions were implemented while adjusting for age, sex, and concurrent use of antihyperlipidemic and/or antihypertensive medication. Minimum SpO2 was transformed into a Hypoxemia Severity Index for normality, in which lower SpO2 values indicated more severe hypoxemia.

RESULTS

Thirty-seven older adults were included in the study (age 72.5 ± 5.6 years, 23 women, AHI = 13.8 ± 18.0 [range 0-80]). Hypoxemia measures significantly predicted global WMH volume (bminSpO2 = 0.141 [0.001-0.282], bduration <90% = 0.008 [0.000-0.016]). This relationship was driven by hypoxemia severity during REM sleep (bREMminSpO2 = 0.143 [0.003-0.284]), which also predicted frontal (bREMminSpO2 = 0.101 [0.004-0.198]) and parietal (bREMminSpO2 = 0.121 [0.024-0.219]) WMH burden. Greater frontal WMH burden indirectly mediated the relationship between REM sleep hypoxemia and ERC thickness (indirect effect = -0.043, 95% CI -0.1174 to -0.00015). Reduced ERC thickness was, in turn, associated with worse overnight mnemonic discrimination ability (bleftERCthickness = 0.112 [0.014-0.211]).

DISCUSSION

These findings identify CSVD as a candidate mechanism linking OSA-related hypoxemia to MTL degeneration and impaired sleep-dependent memory in older adults, specifically implicating hypoxic events during REM sleep.

Brain metabolic imaging-based model identifies cognitive stability in prodromal Alzheimer's disease

The recent approval of anti-amyloid pharmaceuticals for the treatment of Alzheimer's disease (AD) has created a pressing need for the ability to accurately identify optimal candidates for anti-amyloid therapy, specifically those with evidence for incipient cognitive decline, since patients with mild cognitive impairment (MCI) may remain stable for several years even with positive AD biomarkers. Using fluorodeoxyglucose PET and biomarker data from 594 ADNI patients, a neural network ensemble was trained to forecast cognition from MCI diagnostic baseline. Training data comprised PET studies of patients with biological AD. The ensemble discriminated between progressive and stable prodromal subjects (MCI with positive amyloid and tau) at baseline with 88.6% area-under-curve, 88.6% (39/44) accuracy, 73.7% (14/19) sensitivity and 100% (25/25) specificity in the test set. It also correctly classified all other test subjects (healthy or AD continuum subjects across the cognitive spectrum) with 86.4% accuracy (206/239), 77.4% sensitivity (33/42) and 88.23% (165/197) specificity. By identifying patients with prodromal AD who will not progress to dementia, our model could significantly reduce overall societal burden and cost if implemented as a screening tool. The model's high positive predictive value in the prodromal test set makes it a practical means for selecting candidates for anti-amyloid therapy and trials.

Developing brain asymmetry shapes cognitive and psychiatric outcomes in adolescence

Cerebral asymmetry, fundamental to various cognitive functions, is often disrupted in neuropsychiatric disorders. While brain growth has been extensively studied, the maturation of brain asymmetry in children and the factors influencing it in adolescence remain poorly understood. We analyze longitudinal data from 11,270 children aged 10-14 years in the Adolescent Brain Cognitive Development (ABCD) study. Our analysis maps the developmental trajectory of structural brain asymmetry. We identify significant age-related, modality-specific development patterns. These patterns link to crystallized intelligence and mental health problems, but with weak correlations. Genetically, structural asymmetry relates to synaptic processes and neuron projections, likely through asymmetric synaptic pruning. At the microstructural level, corpus callosum integrity emerged as a key factor modulating the developing asymmetry. Environmentally, favorable perinatal conditions were associated with prolonged corpus callosum development, which affected future asymmetry patterns and cognitive outcomes. These findings underscore the dynamic yet predictable interactions between brain asymmetry, its structural determinants, and cognitive and psychiatric outcomes during a pivotal developmental stage. Our results provide empirical support for the adaptive plasticity theory in cerebral asymmetry and offer insights into both cognitive maturation and potential risk for early-onset mental health problems.

Quantitative 3D histochemistry reveals region-specific amyloid-β reduction by the antidiabetic drug netoglitazone

A hallmark of Alzheimer's disease (AD) is the extracellular aggregation of toxic amyloid-beta (Aβ) peptides in form of plaques. Here, we identify netoglitazone, an antidiabetic compound previously tested in humans, as an Aβ aggregation antagonist. Netoglitazone improved cognition and reduced microglia activity in a mouse model of AD. Using quantitative whole-brain three-dimensional histology (Q3D), we precisely identified brain regions where netoglitazone reduced the number and size of Aβ plaques. We demonstrate the utility of Q3D in preclinical drug evaluation for AD by providing a high-resolution brain-wide view of drug efficacy. Applying Q3D has the potential to improve pre-clinical drug evaluation by providing information that can help identify mechanisms leading to brain region-specific drug efficacy.

Abnormal Gyrus Rectus Asymmetry in Alzheimer's Disease: An MRI-Based Parcellation Method

BACKGROUND

The gyrus rectus is a key brain region with neural connections to the entorhinal cortex and hippocampus, both of which are among the earliest areas affected in Alzheimer's disease (AD). Investigating volumetric differences and asymmetry in this region may provide insights into disease progression. This study aimed to assess gyrus rectus volume and asymmetry in AD patients using an MRI-based parcellation method.

METHODS

This cross-sectional volumetric study included 25 cognitively healthy adults and 25 AD patients recruited from the Neurology Clinic of Balıkesir University Hospital. Brain MRI scans were obtained using a 1.5 Tesla MRI scanner. Volumetric measurements were computed using MRIStudio, an atlas-based image analysis program. Group differences in brain volume and asymmetry index were examined, and their correlations with Mini-Mental State Examination (MMSE) scores were evaluated.

RESULTS

AD patients exhibited significantly greater rightward volumetric asymmetry of the gyrus rectus volume than healthy controls (p < 0.05). Additionally, a positive correlation was observed between gyrus rectus volume and MMSE scores (p < 0.05).

CONCLUSIONS

These results suggest that rightward volumetric asymmetry of the gyrus rectus may represent a promising biomarker for tracking the progression of Alzheimer's disease. Detecting asymmetry in brain structures could improve understanding of AD pathology and aid clinical evaluation.

Compensation versus deterioration across functional networks in amnestic mild cognitive impairment subtypes

Functional connectivity studies to detect neurophysiological correlates of amnestic mild cognitive impairment (aMCI), a prodromal stage of Alzheimer's disease, have generated contradictory results in terms of compensation and deterioration, as most of the studies did not distinguish between the different aMCI subtypes: single-domain aMCI (sd-aMCI) and multiple-domain aMCI (md-aMCI). The present study aimed to characterize the neurophysiological correlates of aMCI subtypes by using resting-state functional magnetic resonance imaging. The study included sd-aMCI (n = 29), md-aMCI (n = 26), and control (n = 30) participants. The data were subjected to independent component analysis (ICA) to explore the default mode network (DMN) and the fronto-parietal control network (FPCN). Additionally, seed-based and moderation analyses were conducted to investigate the connectivity of the medial temporal lobe and functional networks. aMCI subtypes presented differences in functional connectivity relative to the control group: sd-aMCI participants displayed increased FPCN connectivity and reduced connectivity between the posterior parahippocampal gyrus (PHG) and medial structures; md-aMCI participants exhibited lower FPCN connectivity, higher anterior PHG connectivity with frontal structures and lower posterior PHG connectivity with central-parietal and temporo-occipital areas. Additionally, md-aMCI participants showed higher posterior PHG connectivity with structures of the DMN than both control and sd-aMCI participants, potentially indicating more severe cognitive deficits. The results showed gradual and qualitative neurofunctional differences between the aMCI subgroups, suggesting the existence of compensatory (sd-aMCI) and deterioration (md-aMCI) mechanisms in functional networks, mainly originated in the DMN. The findings support consideration of the subgroups as different stages of MCI within the Alzheimer disease continuum.

Slight hyperintensity of the left piriform cortex and amygdala on T2-weighted FLAIR images in older adults and patients with probable Alzheimer's disease

BackgroundThe left piriform cortex and amygdala (PC&A) is an early target for deterioration due to aging and Alzheimer's disease (AD) in several neuropathological and magnetic resonance (MR) volumetric studies. We observed slight hyperintensity of the left PC&A in older adults and probable AD (pAD) patients on T2-weighted fluid-attenuated inversion recovery (T2W-FLAIR) images.PurposeTo quantitatively assess the validity of the left PC&A hyperintensity in older adults and pAD patients.Material and MethodsT2W-FLAIR images from three groups were retrospectively evaluated: (i) younger control (YC; n = 77): individuals aged 37.9 ± 8.4 years; (ii) older control (OC; n = 98): individuals aged 76.9 ± 5.3 years without cognitive impairment; and (iii) pAD (n = 35): individuals aged 80.5 ± 6.9 years with pAD. Signal intensity (SI) ratios of the left to right PC&A (L-PC&A/R-PC&A) were calculated for all groups. In the OC and pAD groups, SI ratios of the left PC&A to pons (L-PC&A/P) and the right PC&A to pons (R-PC&A/P) were calculated. The regions of interest were defined as large as possible on transaxial images in which the PC&As were most broadly depicted.ResultsThe mean L-PC&A/R-PC&A in the YC, OC, and pAD groups showed an increasing trend in that sequence (P < 0.001). The mean L-PC&A/P was higher in the pAD group than in the OC group (P < 0.001). However, the mean R-PC&A/P was not significantly different between the OC and pAD groups (P = 0.245).ConclusionThe SI of the left PC&A on T2W-FLAIR images significantly increased with age and in individuals with pAD, likely reflecting the deterioration of the left PC&A.

The Cortical Asymmetry Index for subtyping dementia patients

OBJECTIVES

Frontotemporal dementia (FTD) usually shows more asymmetric atrophy patterns than Alzheimer's disease (AD). We aim to quantify this asymmetry to differentiate FTD, AD, and FTD subtypes.

METHODS

We studied T1-MRI scans, including FTD (different phenotypes), AD, and healthy controls (CTR). We defined the Cortical Asymmetry Index (CAI) using measures based on a metric derived from information theory with the cortical thickness measures. Some participants had additional follow-up MRIs, cerebrospinal fluid (CSF), or plasma measures. We analysed differences at cross-sectional and longitudinal levels. We then clustered FTD and AD participants based on the CAI values and studied the patients' fluid biomarker characteristics within each cluster.

RESULTS

A total of 101 FTD patients (64  ±  8 years, 53 men), 230 AD patients (65  ±  10 years, 84 men), and 173 CTR (59  ±  15 years, 67 men) were studied. CAI differentiated FTD, AD, and CTR. It also distinguished the semantic variant primary progressive aphasia (svPPA) from the other FTD phenotypes. In FTD, the CAI increased over time. The cluster analysis identified two subgroups within FTD, characterised by different neurofilament-light (NfL) levels, and two subgroups within AD, with different plasma glial fibrillary acidic protein (GFAP) levels. In AD, CAI correlated with GFAP and Mini-Mental State Examination (MMSE); in FTD, the CAI was associated with NfL levels.

CONCLUSIONS

The proposed method quantifies asymmetries previously described visually. The CAI could define clinically and biologically meaningful disease subgroups in the differential diagnosis of AD and FTD and its subtypes. CAI could also be of interest in tracking disease progression in FTD.

KEY POINTS

Question There is a need to find quantitative metrics from MRI that can identify disease subgroups, and that could be useful for diagnosis and tracking. Findings We propose a Cortical Asymmetry Index that differentiates Alzheimer's disease (AD) from Frontotemporal dementia (FTD), distinguishes FTD subtypes, correlates with NFL and GFAP levels, and monitors FTD progression. Clinical relevance Our proposed index holds the potential to support clinical applications for diagnosis and disease tracking in AD and FTD, using a quantitative summary metric from MRI data. It also contributes to the understanding of these diseases.

The Balance in the Head: How Developmental Factors Explain Relationships Between Brain Asymmetries and Mental Diseases

Cerebral lateralisation is a core organising principle of the brain that is characterised by a complex pattern of hemispheric specialisations and interhemispheric interactions. In various mental disorders, functional and/or structural hemispheric asymmetries are changed compared to healthy controls, and these alterations may contribute to the primary symptoms and cognitive impairments of a specific disorder. Since multiple genetic and epigenetic factors influence both the pathogenesis of mental illness and the development of brain asymmetries, it is likely that the neural developmental pathways overlap or are even causally intertwined, although the timing, magnitude, and direction of interactions may vary depending on the specific disorder. However, the underlying developmental steps and neuronal mechanisms are still unclear. In this review article, we briefly summarise what we know about structural, functional, and developmental relationships and outline hypothetical connections, which could be investigated in appropriate animal models. Altered cerebral asymmetries may causally contribute to the development of the structural and/or functional features of a disorder, as neural mechanisms that trigger neuropathogenesis are embedded in the asymmetrical organisation of the developing brain. Therefore, the occurrence and severity of impairments in neural processing and cognition probably cannot be understood independently of the development of the lateralised organisation of intra- and interhemispheric neuronal networks. Conversely, impaired cellular processes can also hinder favourable asymmetry development and lead to cognitive deficits in particular.

SurfNet: Reconstruction of Cortical Surfaces via Coupled Diffeomorphic Deformations

To achieve fast and accurate cortical surface reconstruction from brain magnetic resonance images (MRIs), we develop a method to jointly reconstruct the inner (white-gray matter interface), outer (pial), and midthickness surfaces, regularized by their interdependence. Rather than reconstructing these surfaces separately without taking into consideration their interdependence as in most existing methods, our method learns three diffeomorphic deformations jointly to optimize the midthickness surface to lie halfway between the inner and outer cortical surfaces and simultaneously deforms it inward and outward towards the inner and outer cortical surfaces, respectively. The surfaces are encouraged to have a spherical topology by regularization terms for non-negativeness of the cortical thickness and symmetric cycle-consistency of the coupled surface deformations. The coupled reconstruction of cortical surfaces also facilitates an accurate estimation of the cortical thickness based on the diffeomorphic deformation trajectory of each vertex on the surfaces. Validation experiments have demonstrated that our method achieves state-of-the-art cortical surface reconstruction performance in terms of accuracy and surface topological correctness on large-scale MRI datasets, including ADNI, HCP, and OASIS.

Global brain asymmetry

Lateralization is a defining characteristic of the human brain, often studied through localized approaches that focus on interhemispheric differences between homologous pairs of regions. It is also important to emphasize an integrative perspective of global brain asymmetry, in which hemispheric differences are understood through global patterns across the entire brain.

Hyperintensity of the left piriform cortex and amygdala on T2-weighted FLAIR images in patients with probable Alzheimer's disease correlates with cerebral cortical atrophy

Background

The left piriform cortex and amygdala (PC&A) tend to be slightly hyperintense relative to the right PC&A on T2-weighted fluid-attenuated inversion recovery (T2W-FLAIR) images in patients with probable Alzheimer's disease (pAD). This likely represents the antecedent and thus advanced degeneration of the left PC&A.

Purpose

To investigate the relationship between left PC&A hyperintensities and cerebral cortical atrophy on magnetic resonance (MR) voxel-based morphometry in patients with pAD and discuss how this finding could relate to AD progression.

Material and Methods

Patients with pAD (n = 47; age range = 68-93 years, mean = 80.8 ± 6.7 years; 14 men and 33 women) who underwent T2W-FLAIR imaging and MR morphometric study using a voxel-based specific regional analysis system for AD (VSRAD) were retrospectively examined. To measure signal intensity ratios of the left to right PC&A (L-PC&A/R-PC&A), regions of interest (ROIs) were set on the transaxial images in which both PC&As were most broadly depicted; the ROIs were defined as large as possible. Correlations between the L-PC&A/R-PC&A and medial temporal lobe cortical atrophy (MTLCA) as well as whole cerebral cortical atrophy (WCCA) on VSRAD were determined. Correlation between the L-PC&A/R-PC&A and age was also determined.

Results

The L-PC&A/R-PC&A correlated with both MTLCA (r = 0.375, p = .010, 95% confidence interval [CI] = 0.095-0.600) and WCCA (r = 0.576, p < .001, 95% CI = 0.343-0.742). The L-PC&A/R-PC&A did not correlate with age (r = 0.013, p = .932, 95% CI = -0.282-0.305).

Conclusion

Left-sided dominance of PC&A degeneration appeared to accelerate with the progression of AD stages.

Retinal vascular alterations in cognitive impairment: A multicenter study in China

INTRODUCTION

Foundational models suggest Alzheimer's disease (AD) can be diagnosed using retinal images, but the specific structural features remain poorly understood. This study investigates retinal vascular changes in individuals with cognitive impairment in three East Asian regions.

METHODS

A multicenter study was conducted in Shanghai, Hong Kong, and Ningxia, collecting retinal images from 176 patients with mild cognitive impairment (MCI) or AD and 264 controls. The VC-Net deep learning model segmented arterial/venous networks, extracting 36 vascular features.

RESULTS

Significant reductions in vessel length, segment number, and vascular density were observed in cognitively impaired patients, while venous structure and complexity were correlated with the level of cognitive function.

DISCUSSION

Retinal vascular changes may serve as indicators of cognitive impairment, requiring validation in larger cohorts and exploration of the underlying mechanisms.

HIGHLIGHTS

A deep learning segmentation model extracted diverse retinal vascular features. Significant alterations in the structure of retinal arterial/venous networks were identified. Partitioning vessel-rich retinal zones improved detection of vascular changes. Decreases in vessel length, segment number, and vascular density were found in CI individuals.

Hemispheric organization of the brain and its prevailing impact on the neuropsychology of aging

Age differences in brain hemispheric asymmetry have figured prominently in the neuropsychology of aging. Here, a broad overview of these empirical and theoretical approaches is provided that dates back to the 1970s and continues to the present day. Methodological advances often brought new evidence to bear on older ideas and promoted the development of new ones. The deficit-focused hypothesis of accelerated right-hemisphere aging is reviewed first, followed by subsequent accounts pertaining to compensation, reserve, and their potential hemispheric underpinnings. Structural and functional neuroimaging reveal important and consistent age-related patterns, including indications of reduced brain asymmetry in older relative to younger adults. While not mutually exclusive, different neuropsychologic theories of aging offer divergent interpretations of such patterns, including age-related reductions in neural specificity (dedifferentiation) and age-related compensatory bilateral recruitment [e.g., Hemispheric Asymmetry Reduction in Older Adults (HAROLD); Compensation-Related Utilization of Neural Circuits Hypothesis (CRUNCH)]. Further, recent neurobehavioral evidence suggests that the right hemisphere plays a unique role in resisting the neurocognitive effects of aging via brain reserve. Future advances in human cognitive neuroscience, including neurostimulation methods for targeted interventions, along with analytic techniques informed by machine learning promise new insights into the neuropsychology of aging and the role of hemispheric processes in resilience and decline.

Establishing group-level brain structural connectivity incorporating anatomical knowledge under latent space modeling

Brain structural connectivity, capturing the white matter fiber tracts among brain regions inferred by diffusion MRI (dMRI), provides a unique characterization of brain anatomical organization. One fundamental question to address with structural connectivity is how to properly summarize and perform statistical inference for a group-level connectivity architecture, for instance, under different sex groups, or disease cohorts. Existing analyses commonly summarize group-level brain connectivity by a simple entry-wise sample mean or median across individual brain connectivity matrices. However, such a heuristic approach fully ignores the associations among structural connections and the topological properties of brain networks. In this project, we propose a latent space-based generative network model to estimate group-level brain connectivity. Within our modeling framework, we incorporate the anatomical information of brain regions as the attributes of nodes to enhance the plausibility of our estimation and improve biological interpretation. We name our method the attributes-informed brain connectivity (ABC) model, which compared with existing group-level connectivity estimations, (1) offers an interpretable latent space representation of the group-level connectivity, (2) incorporates the anatomical knowledge of nodes and tests its co-varying relationship with connectivity and (3) quantifies the uncertainty and evaluates the likelihood of the estimated group-level effects against chance. We devise a novel Bayesian MCMC algorithm to estimate the model. We evaluate the performance of our model through extensive simulations. By applying the ABC model to study brain structural connectivity stratified by sex among Alzheimer's Disease (AD) subjects and healthy controls incorporating the anatomical attributes (volume, thickness and area) on nodes, our method shows superior predictive power on out-of-sample structural connectivity and identifies meaningful sex-specific network neuromarkers for AD.

Large-scale genetic mapping for human brain asymmetry

Left-right asymmetry is an important aspect of human brain organization for functions including language and hand motor control, which can be altered in some psychiatric traits. The last 5 years have seen rapid advances in the identification of specific genes linked to variation in asymmetry of the human brain and/or handedness. These advances have been driven by a new generation of large-scale genome-wide association studies, carried out in samples ranging from roughly 16,000 to over 1.5 million participants. The implicated genes tend to be most active in the embryonic and fetal brain, consistent with early developmental patterning of brain asymmetry. Several of the genes encode components of microtubules or other microtubule-associated proteins. Microtubules are key elements of the internal cellular skeleton (cytoskeleton). A major challenge remains to understand how these genes affect, or even induce, the brain's left-right axis. Several of the implicated genes have also been associated with psychiatric or neurologic disorders, and polygenic dispositions to autism and schizophrenia have been associated with structural brain asymmetry. Knowledge of developmental mechanisms that lead to hemispheric specialization may ultimately help to define etiologic subtypes of brain disorders.

Latent dimensions of brain asymmetry

Functional lateralization represents a fundamental aspect of brain organization, where certain cognitive functions are specialized in one hemisphere over the other. Deviations from typical patterns of lateralization often manifest in various brain disorders, such as autism spectrum disorder, schizophrenia, and dyslexia. However, despite its importance, uncovering the intrinsic properties of brain lateralization and its underlying structural basis remains challenging. On the one hand, functional lateralization has long been oversimplified, often reduced to a unidimensional perspective. For instance, individuals are sometimes labeled as left-brained or right-brained based on specific behavioral measures like handedness and language lateralization. Such a perspective disregards the nuanced subtypes of lateralization, each potentially attributed to distinct factors and associated with unique functional correlates. On the other hand, traditional studies of brain structural asymmetry have typically focused on localized analyses of homologous regions in the two hemispheres. This perspective fails to capture the inherent interplay between brain regions, resulting in an overly complex depiction of structural asymmetry. Such conceptual and methodological discrepancies between studies of functional lateralization and structural asymmetry pose significant obstacles to establishing meaningful links between them. To address this gap, a shift toward uncovering the dimensional structure of brain asymmetry has been proposed. This chapter introduces the concept of latent dimensions of brain asymmetry and provides an up-to-date overview of studies regarding dimensions of functional lateralization and structural asymmetry in the human brain. By transcending the traditional analysis and employing multivariate pattern techniques, these studies offer valuable insights into our understanding of the intricate organizational principles governing the human brain's lateralized functions.

Cognitive deficits and cortical volume loss in COVID-19-related hyposmia

BACKGROUND AND PURPOSE

Studies have found that up to 73% of COVID-19 patients experience hyposmia. It is unclear if the loss of smell in COVID-19 is due to damage to the peripheral or central mechanisms. This study aimed to explore the impacts of COVID-19-induced hyposmia on brain structure and cognitive functions.

METHODS

The study included 36 hyposmic (h-COV) and 21 normosmic (n-COV) participants who had recovered from mild COVID-19 infection, as well as 25 healthy controls (HCs). All participants underwent neurological examination, neuropsychiatric assessment and Sniffin' Sticks tests. High-resolution anatomical images were collected; olfactory bulb (OB) volume and cortical thickness were measured.

RESULTS

Addenbrooke's Cognitive Examination-Revised total and language sub-scores were slightly but significantly lower in the h-COV group compared to the HC group (p = 0.04 and p = 0.037). The h-COV group exhibited poorer performance in the Sniffin' Sticks test terms of discrimination score, identification score and the composite score compared to the n-COV and HC groups (p < 0.001, p = 0.001 and p = 0.002 respectively). A decrease in left and right OB volumes was observed in the h-COV group compared to the n-COV and HC groups (p = 0.003 and p = 0.006 respectively). The cortical thickness analysis revealed atrophy in the left lateral orbitofrontal cortex in the h-COV group compared to HCs. A significant low positive correlation of varying degrees was detected between discrimination and identification scores and both OB and left orbital sulci.

CONCLUSION

Temporary or permanent hyposmia after COVID-19 infection leads to atrophy in the OB and olfactory-related cortical structures and subtle cognitive problems in the long term.

Brain change trajectories in healthy adults correlate with Alzheimer's related genetic variation and memory decline across life

Throughout adulthood and ageing our brains undergo structural loss in an average pattern resembling faster atrophy in Alzheimer's disease (AD). Using a longitudinal adult lifespan sample (aged 30-89; 2-7 timepoints) and four polygenic scores for AD, we show that change in AD-sensitive brain features correlates with genetic AD-risk and memory decline in healthy adults. We first show genetic risk links with more brain loss than expected for age in early Braak regions, and find this extends beyond APOE genotype. Next, we run machine learning on AD-control data from the Alzheimer's Disease Neuroimaging Initiative using brain change trajectories conditioned on age, to identify AD-sensitive features and model their change in healthy adults. Genetic AD-risk linked with multivariate change across many AD-sensitive features, and we show most individuals over age ~50 are on an accelerated trajectory of brain loss in AD-sensitive regions. Finally, high genetic risk adults with elevated brain change showed more memory decline through adulthood, compared to high genetic risk adults with less brain change. Our findings suggest quantitative AD risk factors are detectable in healthy individuals, via a shared pattern of ageing- and AD-related neurodegeneration that occurs along a continuum and tracks memory decline through adulthood.

SegCSR: Weakly-Supervised Cortical Surfaces Reconstruction from Brain Ribbon Segmentations

Deep learning-based cortical surface reconstruction (CSR) methods heavily rely on pseudo ground truth (pGT) generated by conventional CSR pipelines as supervision, leading to dataset-specific challenges and lengthy training data preparation. We propose a new approach for reconstructing multiple cortical surfaces using weak supervision from brain MRI ribbon segmentations. Our approach initializes a midthickness surface and then deforms it inward and outward to form the inner (white matter) and outer (pial) cortical surfaces, respectively, by jointly learning diffeomorphic flows to align the surfaces with the boundaries of the cortical ribbon segmentation maps. Specifically, a boundary surface loss drives the initialization surface to the target inner and outer boundaries, and an inter-surface normal consistency loss regularizes the pial surface in challenging deep cortical sulci. Additional regularization terms are utilized to enforce surface smoothness and topology. Evaluated on two large-scale brain MRI datasets, our weakly-supervised method achieves comparable or superior CSR accuracy and regularity to existing supervised deep learning alternatives.

Exploring the relationship among Alzheimer's disease, aging and cognitive scores through neuroimaging-based approach

Alzheimer's disease (AD) is a fatal neurodegenerative disorder, with the Mini-Mental State Examination (MMSE) and Clinical Dementia Rating (CDR) serving significant roles in monitoring its progression. We hypothesize that while cognitive assessment scores can detect AD-related brain changes, the targeted brain regions may differ. Additionally, given AD's strong association with aging, we propose that specific brain regions are influenced by both AD pathology and aging, exhibiting strong correlations with both. To test these hypotheses, we developed a 3D convolutional network with a mixed-attention mechanism to recognize AD subjects from structural magnetic resonance imaging (sMRI) data and utilize 3D convolutional methods to pinpoint brain regions significantly correlated with the AD, MMSE, CDR and age. All models were trained and internally validated on 417 samples from the Alzheimer's Disease Neuroimaging Initiative (ADNI), and the classification model was externally validated on 382 samples from the Australian Imaging and Lifestyle flagship (AIBL). This approach provided robust support for using MMSE and CDR in assessing AD progression and visually illustrated the relationship between aging and AD. The analysis revealed correlations among the four identification tasks (AD, MMSE, CDR and age) and highlighted asymmetric brain lesions in both AD and aging. Notably, we found that AD can accelerate aging to some extent, and a significant correlation exists between the rate of aging and cognitive assessment scores. This offers new insights into the relationship between AD and aging.

Graph Convolutional Network With Self-Supervised Learning for Brain Disease Classification

Brain functional network (BFN) analysis has become a popular method for identifying neurological diseases at their early stages and revealing sensitive biomarkers related to these diseases. Due to the fact that BFN is a graph with complex structure, graph convolutional networks (GCNs) can be naturally used in the identification of BFN, and can generally achieve an encouraging performance if given large amounts of training data. In practice, however, it is very difficult to obtain sufficient brain functional data, especially from subjects with brain disorders. As a result, GCNs usually fail to learn a reliable feature representation from limited BFNs, leading to overfitting issues. In this paper, we propose an improved GCN method to classify brain diseases by introducing a self-supervised learning (SSL) module for assisting the graph feature representation. We conduct experiments to classify subjects with mild cognitive impairment (MCI) and autism spectrum disorder (ASD) respectively from normal controls (NCs). Experimental results on two benchmark databases demonstrate that our proposed scheme tends to obtain higher classification accuracy than the baseline methods.

Dual attention based fusion network for MCI Conversion Prediction

Alzheimer's disease (AD) severely impacts the lives of many patients and their families. Predicting the progression of the disease from the early stage of mild cognitive impairment (MCI) is of substantial value for treatment, medical research and clinical trials. In this paper, we propose a novel dual attention network to classify progressive MCI (pMCI) and stable MCI (sMCI) using both magnetic resonance imaging (MRI) and neurocognitive metadata. A 3D CNN ShuffleNet V2 model is used as the network backbone to extract MRI image features. Then, neurocognitive metadata is used to guide the spatial attention mechanism to steer the model to focus attention on the most discriminative regions of the brain. In contrast to traditional fusion methods, we propose a ViT based self attention fusion mechanism to fuse the neurocognitive metadata with the 3D CNN feature maps. The experimental results show that our proposed model achieves an accuracy, AUC, and sensitivity of 81.34%, 0.874, and 0.85 respectively using 5-fold cross validation evaluation. A comprehensive experimental study shows our proposed approach significantly outperforms all previous methods for MCI progression classification. In addition, an ablation study shows both fusion methods contribute to the high final performance.

Exploring cortical morphology biomarkers of amnesic mild cognitive impairment using novel fractal dimension-based structural MRI analysis

Amnestic mild cognitive impairment (aMCI) is considered as an intermediate stage of Alzheimer's disease, but no MRI biomarkers currently distinguish aMCI from healthy individuals effectively. Fractal dimension, a quantitative parameter, provides superior morphological information compared to conventional cortical thickness methods. Few studies have used cortical fractal dimension values to differentiate aMCI from healthy controls. In this study, we aim to build an automated discriminator for accurately distinguishing aMCI using fractal dimension measures of the cerebral cortex. Thirty aMCI patients and 30 health controls underwent structural MRI of the brain. First, the atrophy of participants' cortical sub-regions of Desikan-Killiany cortical atlas was assessed using fractal dimension and cortical thickness. The fractal dimension is more sensitive than cortical thickness in reducing dimensional effects and may accurately reflect morphological changes of the cortex in aMCI. The aMCI group had significantly lower fractal dimension values in the bilateral temporal lobes, right limbic lobe and right parietal lobe, whereas they showed significantly lower cortical thickness values only in the bilateral temporal lobes. Fractal dimension analysis was able to depict most of the significantly different focal regions detected by cortical thickness, but additionally with more regions. Second, applying the measured fractal dimensions (and cortical thickness) of both cerebral hemispheres, an unsupervised discriminator was built for the aMCI and healthy controls. The proposed fractal dimension-based method achieves 80.54% accuracy in discriminating aMCI from healthy controls. The fractal dimension appears to be a promising biomarker for cortical morphology changes that can discriminate patients with aMCI from healthy controls.

Synergistic effects of GFAP and Aβ42: Implications for white matter integrity and verbal memory across the cognitive spectrum

Background

Plasma glial fibrillary acidic protein (GFAP), an astrocytic biomarker, has previously been linked with Alzheimer's disease (AD) status, amyloid levels, and memory performance in older adults. The neuroanatomical pathways by which astrogliosis/astrocyte reactivity might impact cognitive outcomes remains unclear. We evaluated whether plasma GFAP and amyloid levels had a synergistic effect on fornix structure, which is critically involved in AD-associated cholinergic pathways. We also examined whether fornix structure mediates associations between GFAP and verbal memory.

Methods

In a cohort of both asymptomatic and symptomatic older adults (total n = 99), we assessed plasma GFAP, amyloid-β42 (Aβ42), other AD-related proteins, and vascular markers, and we conducted comprehensive memory testing. Tractography-based methods were used to assess fornix structure with whole brain diffusion metrics to control for diffuse alterations in brain white matter.

Results

In individuals in the low plasma amyloid-β42 (Aβ42) group, higher plasma GFAP was associated with lower fractional anisotropy (FA; p = 0.007), higher mean diffusivity (MD; p < 0.001), higher radial diffusivity (RD; p < 0.001), and higher axial diffusivity (DA; p = 0.001) in the left fornix. These associations were independent of APOE gene status, plasma levels of total tau and neurofilament light, plasma vascular biomarkers, and whole brain diffusion metrics. In a sub-analysis of participants in the low plasma Aβ42 group (n = 33), fornix structure mediated the association between higher plasma GFAP levels and lower verbal memory performance.

Discussion

Higher plasma GFAP was associated with altered fornix microstructure in the setting of greater amyloid deposition. We also expanded on our prior GFAP-verbal memory findings by demonstrating that in the low plasma Aβ42 group, left fornix integrity may be a primary white matter conduit for the negative associations between GFAP and verbal memory performance. Overall, these findings suggest that astrogliosis/astrocyte reactivity may play an early, pivotal role in AD pathogenesis, and further demonstrate that high GFAP and low Aβ42 in plasma may reflect a particularly detrimental synergistic role in forniceal-memory pathways.

Handedness in Alzheimer's disease: A systematic review

Handedness has traditionally been employed as a proxy of brain lateralization in research. Alzheimer's disease (AD) manifests as a neurodegenerative disorder characterized by impairments across various neuropsychological functions, including visuospatial and language, many of which exhibit lateralization in the human brain. While previous studies have investigated the relationship between AD and handedness, findings have been inconsistent. This article aims to provide an up-to-date overview of studies investigating hand preference in AD and the subtypes, specifically early- and late-onset AD. Through a synthesis of these studies, we conclude that handedness currently lacks utility as a diagnostic biomarker for AD and its subtypes, and this is further supported by the meta-analytic results based on data from over 10,000 AD patients. We emphasize the necessity for future research endeavors, particularly those leveraging advanced neuroimaging techniques to explore the role of brain asymmetry in AD.

Progression of hippocampal subfield atrophy and asymmetry in Alzheimer's disease

Alzheimer's disease (AD) affects the hippocampus during its progression, but the specific observable changes of hippocampal subfields during disease progression remain poorly understood. In this study, we employed an event-based model (EBM) to determine the sequence of occurrence of hippocampal subfield atrophy in mild cognitive impairment (MCI) and AD cohorts. Subjects (207) were included: 86 MCI, 53 AD, and 68 healthy controls from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Participants underwent structural magnetic resonance imaging (MRI) scans to analyse the hippocampal subfields. We assigned each patient to a specific EBM stage, based on the number of their abnormal subfields. A combination of 2-year follow-up MRI scans were applied to demonstrate the longitudinal consistency and utility of the model's staging system. The model estimated that the earliest atrophy occurs in the hippocampal fissure, then spreading to other subregions in both MCI and AD. We identified a marked divergence between the sequences of left and right hippocampal subfields atrophy, so inter-hemispheric asymmetry pattern was further analysed. The sequence of asymmetry index (AI) increases beginning in the molecular and granule cell layers of the dentate gyrus (GC-ML-DG), cornus ammonis (CA) 4, and the molecular layer (ML). Longitudinal analysis confirms the efficacy of the model. In addition, the model stages were significantly correlated with patients' memory scores (p < .05). Collectively, we used a data-driven method to provide new insight into AD hippocampal progression. The present model could aid in understanding of the disease stages, as well as tracking memory decline.

A Holistic Analysis of Alzheimer's Disease-Associated lncRNA Communities Reveals Enhanced lncRNA-miRNA-RBP Regulatory Triad Formation Within Functionally Segregated Clusters

Recent studies on the regulatory networks implicated in Alzheimer's disease (AD) evince long non-coding RNAs (lncRNAs) as crucial regulatory players, albeit a poor understanding of the mechanism. Analyzing differential gene expression in the RNA-seq data from the post-mortem AD brain hippocampus, we categorized a list of AD-dysregulated lncRNA transcripts into functionally similar communities based on their k-mer profiles. Using machine-learning-based algorithms, their subcellular localizations were mapped. We further explored the functional relevance of each community through AD-dysregulated miRNA, RNA-binding protein (RBP) interactors, and pathway enrichment analyses. Further investigation of the miRNA-lncRNA and RBP-lncRNA networks from each community revealed the top RBPs, miRNAs, and lncRNAs for each cluster. The experimental validation community yielded ELAVL4 and miR-16-5p as the predominant RBP and miRNA, respectively. Five lncRNAs emerged as the top-ranking candidates from the RBP/miRNA-lncRNA networks. Further analyses of these networks revealed the presence of multiple regulatory triads where the RBP-lncRNA interactions could be augmented by the enhanced miRNA-lncRNA interactions. Our results advance the understanding of the mechanism of lncRNA-mediated AD regulation through their interacting partners and demonstrate how these functionally segregated but overlapping regulatory networks can modulate the disease holistically.

An integrative multi-context Mendelian randomization method for identifying risk genes across human tissues

Mendelian randomization (MR) provides valuable assessments of the causal effect of exposure on outcome, yet the application of conventional MR methods for mapping risk genes encounters new challenges. One of the issues is the limited availability of expression quantitative trait loci (eQTLs) as instrumental variables (IVs), hampering the estimation of sparse causal effects. Additionally, the often context- or tissue-specific eQTL effects challenge the MR assumption of consistent IV effects across eQTL and GWAS data. To address these challenges, we propose a multi-context multivariable integrative MR framework, mintMR, for mapping expression and molecular traits as joint exposures. It models the effects of molecular exposures across multiple tissues in each gene region, while simultaneously estimating across multiple gene regions. It uses eQTLs with consistent effects across more than one tissue type as IVs, improving IV consistency. A major innovation of mintMR involves employing multi-view learning methods to collectively model latent indicators of disease relevance across multiple tissues, molecular traits, and gene regions. The multi-view learning captures the major patterns of disease relevance and uses these patterns to update the estimated tissue relevance probabilities. The proposed mintMR iterates between performing a multi-tissue MR for each gene region and joint learning the disease-relevant tissue probabilities across gene regions, improving the estimation of sparse effects across genes. We apply mintMR to evaluate the causal effects of gene expression and DNA methylation for 35 complex traits using multi-tissue QTLs as IVs. The proposed mintMR controls genome-wide inflation and offers insights into disease mechanisms.

Modulation of alpha oscillations by attention is predicted by hemispheric asymmetry of subcortical regions

Evidence suggests that subcortical structures play a role in high-level cognitive functions such as the allocation of spatial attention. While there is abundant evidence in humans for posterior alpha band oscillations being modulated by spatial attention, little is known about how subcortical regions contribute to these oscillatory modulations, particularly under varying conditions of cognitive challenge. In this study, we combined MEG and structural MRI data to investigate the role of subcortical structures in controlling the allocation of attentional resources by employing a cued spatial attention paradigm with varying levels of perceptual load. We asked whether hemispheric lateralization of volumetric measures of the thalamus and basal ganglia predicted the hemispheric modulation of alpha-band power. Lateral asymmetry of the globus pallidus, caudate nucleus, and thalamus predicted attention-related modulations of posterior alpha oscillations. When the perceptual load was applied to the target and the distractor was salient caudate nucleus asymmetry predicted alpha-band modulations. Globus pallidus was predictive of alpha-band modulations when either the target had a high load, or the distractor was salient, but not both. Finally, the asymmetry of the thalamus predicted alpha band modulation when neither component of the task was perceptually demanding. In addition to delivering new insight into the subcortical circuity controlling alpha oscillations with spatial attention, our finding might also have clinical applications. We provide a framework that could be followed for detecting how structural changes in subcortical regions that are associated with neurological disorders can be reflected in the modulation of oscillatory brain activity.

Temporal tau asymmetry spectrum influences divergent behavior and language patterns in Alzheimer's disease

Understanding the psychiatric symptoms of Alzheimer s disease (AD) is crucial for advancing precision medicine and therapeutic strategies. The relationship between AD behavioral symptoms and asymmetry in spatial tau PET patterns is not well-known. Braak tau progression implicates the temporal lobes early. However, the clinical and pathological implications of temporal tau laterality remain unexplored. This cross-sectional study investigated the correlation between temporal tau PET asymmetry and behavior assessed using the neuropsychiatric inventory and composite scores for memory, executive function, and language, using data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. In the entire cohort, continuous right and left temporal tau contributions to behavior and cognition were evaluated, controlling for age, sex, education, and tau burden on the contralateral side. Additionally, a temporal tau laterality index was calculated to define "asymmetry-extreme" groups (individuals with laterality indices greater than two standard deviations from the mean). 695 individuals (age = 73.9 ± 7.6 years, 372 (53.5 %) females) were included, comprising 281 (40%) cognitively unimpaired (CU) amyloid negative, 185 (27%) CU amyloid positive, and 229 (33%) impaired (CI) amyloid positive participants. In the full cohort analysis, right temporal tau was associated with worse behavior (B = 8.14, p-value = 0.007), and left temporal tau was associated with worse language (B = 1.4, p-value < 0.001). Categorization into asymmetry-extreme groups revealed 20 right- and 27 left-asymmetric participants. Within these extreme groups, there was additional heterogeneity along the anterior-posterior dimension. Asymmetrical tau burden is associated with distinct behavioral and cognitive profiles. Wide multi-cultural implementation of social cognition measures is needed to understand right-sided asymmetry in AD.

Gray matter volume in women with the BRCA mutation with and without ovarian removal: evidence for increased risk of late-life Alzheimer's disease or dementia

OBJECTIVE

Ovarian removal prior to spontaneous/natural menopause (SM) is associated with increased risk of late life dementias including Alzheimer's disease. This increased risk may be related to the sudden and early loss of endogenous estradiol. Women with breast cancer gene mutations (BRCAm) are counseled to undergo oophorectomy prior to SM to significantly reduce their risk of developing breast, ovarian, and cervical cancers. There is limited evidence of the neurological effects of ovarian removal prior to the age of SM showing women without the BRCAm had cortical thinning in medial temporal lobe structures. A second study in women with BRCAm and bilateral salpingo-oophorectomy (BSO) noted changes in cognition.

METHODS

The present, cross-sectional study examined whole-brain differences in gray matter (GM) volume using high-resolution, quantitative magnetic resonance imaging in women with BRCAm and intact ovaries (BRCA-preBSO [study cohort with BRCA mutation prior to oophorectomy]; n = 9) and after surgery with (BSO + estradiol-based therapy [ERT]; n = 10) and without (BSO; n = 10) postsurgical estradiol hormone therapy compared with age-matched women (age-matched controls; n = 10) with their ovaries.

RESULTS

The BRCA-preBSO and BSO groups showed significantly lower GM volume in the left medial temporal and frontal lobe structures. BSO + ERT exhibited few areas of lower GM volume compared with age-matched controls. Novel to this study, we also observed that all three BRCAm groups exhibited significantly higher GM volume compared with age-matched controls, suggesting continued plasticity.

CONCLUSIONS

The present study provides evidence, through lower GM volume, to support both the possibility that the BRCAm, alone, and early life BSO may play a role in increasing the risk for late-life dementia. At least for BRCAm with BSO, postsurgical ERT seems to ameliorate GM losses.

Clinical implications of brain asymmetries

No two human brains are alike, and with the rise of precision medicine in neurology, we are seeing an increased emphasis on understanding the individual variability in brain structure and function that renders every brain unique. Functional and structural brain asymmetries are a fundamental principle of brain organization, and recent research suggests substantial individual variability in these asymmetries that needs to be considered in clinical practice. In this Review, we provide an overview of brain asymmetries, variations in such asymmetries and their relevance in the clinical context. We review recent findings on brain asymmetries in neuropsychiatric and neurodevelopmental disorders, as well as in specific learning disabilities, with an emphasis on large-scale database studies and meta-analyses. We also highlight the relevance of asymmetries for disease symptom onset in neurodegenerative diseases and their implications for lateralized treatments, including brain stimulation. We conclude that alterations in brain asymmetry are not sufficiently specific to act as diagnostic biomarkers but can serve as meaningful symptom or treatment response biomarkers in certain contexts. On the basis of these insights, we provide several recommendations for neurological clinical practice.

Structural correlations between brain magnetic resonance image-derived phenotypes and retinal neuroanatomy

BACKGROUND AND PURPOSE

The eye is a well-established model of brain structure and function, yet region-specific structural correlations between the retina and the brain remain underexplored. Therefore, we aim to explore and describe the relationships between the retinal layer thicknesses and brain magnetic resonance image (MRI)-derived phenotypes in UK Biobank.

METHODS

Participants with both quality-controlled optical coherence tomography (OCT) and brain MRI were included in this study. Retinal sublayer thicknesses and total macular thickness were derived from OCT scans. Brain image-derived phenotypes (IDPs) of 153 cortical and subcortical regions were processed from MRI scans. We utilized multivariable linear regression models to examine the association between retinal thickness and brain regional volumes. All analyses were corrected for multiple testing and adjusted for confounders.

RESULTS

Data from 6446 participants were included in this study. We identified significant associations between volumetric brain MRI measures of subregions in the occipital lobe (intracalcarine cortex), parietal lobe (postcentral gyrus), cerebellum (lobules VI, VIIb, VIIIa, VIIIb, and IX), and deep brain structures (thalamus, hippocampus, caudate, putamen, pallidum, and accumbens) and the thickness of the innermost retinal sublayers and total macular thickness (all p < 3.3 × 10-5). We did not observe statistically significant associations between brain IDPs and the thickness of the outer retinal sublayers.

CONCLUSIONS

Thinner inner and total retinal thicknesses are associated with smaller volumes of specific brain regions. Notably, these relationships extend beyond anatomically established retina-brain connections.

Elderly mice with history of acetaminophen intoxication display worsened cognitive impairment and persistent elevation of astrocyte and microglia burden

Acetaminophen (APAP) is a leading cause of acute liver failure. The effect of APAP metabolite's effects in the periphery are well characterized; however, associated consequences in the brain remain poorly understood. Animal studies on this subject are few and reveal that frequent APAP intake can trigger cerebral abnormalities that vary depending on the subject's age. Alarmingly, experimental efforts have yet to examine associated consequences in elderly hosts, who correspond to the highest risk of medication overload, impaired drug clearance, and cognitive deficits. Here, we interrogated the cerebral and peripheral pathology of elderly mice submitted to monthly episodes of APAP intoxication since a young adult age. We found that weeks after the final episode of recurrent APAP exposure, mice exhibited worsened non-spatial memory deficit whereas spatial memory performance was unaltered. Interestingly, one month after the period of APAP intoxication, these mice showed increased glial burden without associated drivers, namely, blood-brain barrier disruption, cholesterol accumulation, and elevation of inflammatory molecules in the brain and/or periphery. Our experimental study reveals how recurrent APAP exposure affects the cognitive performance and cellular events in elderly brains. These data suggest that APAP-containing pharmacological interventions may foreshadow the elevated risk of neuropsychiatric disorders that afflict elderly populations.

Investigation of hemispheric asymmetry in Alzheimer's disease patients during resting state revealed BY fNIRS

Alzheimer's disease (AD) is characterized by the gradual deterioration of brain structures and changes in hemispheric asymmetry. Meanwhile, healthy aging is associated with a decrease in functional hemispheric asymmetry. In this study, functional connectivity analysis was used to compare the functional hemispheric asymmetry in eyes-open resting-state fNIRS data of 16 healthy elderly controls (mean age: 60.4 years, MMSE (Mini-Mental State Examination): 27.3 ± 2.52) and 14 Alzheimer's patients (mean age: 73.8 years, MMSE: 22 ± 4.32). Increased interhemispheric functional connectivity was found in the premotor cortex, supplementary motor cortex, primary motor cortex, inferior parietal cortex, primary somatosensory cortex, and supramarginal gyrus in the control group compared to the AD group. The study revealed that the control group had stronger interhemispheric connectivity, leading to a more significant decrease in hemispheric asymmetry than the AD group. The results show that there is a difference in interhemispheric functional connections at rest between the Alzheimer's group and the control group, suggesting that functional hemispheric asymmetry continues in Alzheimer's patients.

Diffusion imaging markers of accelerated aging of the lower cingulum in subjective cognitive decline

Introduction

Alzheimer's Disease (AD) typically starts in the medial temporal lobe, then develops into a neurodegenerative cascade which spreads to other brain regions. People with subjective cognitive decline (SCD) are more likely to develop dementia, especially in the presence of amyloid pathology. Thus, we were interested in the white matter microstructure of the medial temporal lobe in SCD, specifically the lower cingulum bundle that leads into the hippocampus. Diffusion tensor imaging (DTI) has been shown to differentiate SCD participants who will progress to mild cognitive impairment from those who will not. However, the biology underlying these DTI metrics is unclear, and results in the medial temporal lobe have been inconsistent.

Methods

To better characterize the microstructure of this region, we applied DTI to cognitively normal participants in the Cam-CAN database over the age of 55 with cognitive testing and diffusion MRI available (N = 325, 127 SCD). Diffusion MRI was processed to generate regional and voxel-wise diffusion tensor values in bilateral lower cingulum white matter, while T1-weighted MRI was processed to generate regional volume and cortical thickness in the medial temporal lobe white matter, entorhinal cortex, temporal pole, and hippocampus.

Results

SCD participants had thinner cortex in bilateral entorhinal cortex and right temporal pole. No between-group differences were noted for any of the microstructural metrics of the lower cingulum. However, correlations with delayed story recall were significant for all diffusion microstructure metrics in the right lower cingulum in SCD, but not in controls, with a significant interaction effect. Additionally, the SCD group showed an accelerated aging effect in bilateral lower cingulum with MD, AxD, and RD.

Discussion

The diffusion profiles observed in both interaction effects are suggestive of a mixed neuroinflammatory and neurodegenerative pathology. Left entorhinal cortical thinning correlated with decreased FA and increased RD, suggestive of demyelination. However, right entorhinal cortical thinning also correlated with increased AxD, suggestive of a mixed pathology. This may reflect combined pathologies implicated in early AD. DTI was more sensitive than cortical thickness to the associations between SCD, memory, and age. The combined effects of mixed pathology may increase the sensitivity of DTI metrics to variations with age and cognition.

DeepFDR: A Deep Learning-based False Discovery Rate Control Method for Neuroimaging Data

Voxel-based multiple testing is widely used in neuroimaging data analysis. Traditional false discovery rate (FDR) control methods often ignore the spatial dependence among the voxel-based tests and thus suffer from substantial loss of testing power. While recent spatial FDR control methods have emerged, their validity and optimality remain questionable when handling the complex spatial dependencies of the brain. Concurrently, deep learning methods have revolutionized image segmentation, a task closely related to voxel-based multiple testing. In this paper, we propose DeepFDR, a novel spatial FDR control method that leverages unsupervised deep learning-based image segmentation to address the voxel-based multiple testing problem. Numerical studies, including comprehensive simulations and Alzheimer's disease FDG-PET image analysis, demonstrate DeepFDR's superiority over existing methods. DeepFDR not only excels in FDR control and effectively diminishes the false nondiscovery rate, but also boasts exceptional computational efficiency highly suited for tackling large-scale neuroimaging data.

Using a deep generation network reveals neuroanatomical specificity in hemispheres

Asymmetry is an important property of brain organization, but its nature is still poorly understood. Capturing the neuroanatomical components specific to each hemisphere facilitates the understanding of the establishment of brain asymmetry. Since deep generative networks (DGNs) have powerful inference and recovery capabilities, we use one hemisphere to predict the opposite hemisphere by training the DGNs, which automatically fit the built-in dependencies between the left and right hemispheres. After training, the reconstructed images approximate the homologous components in the hemisphere. We use the difference between the actual and reconstructed hemispheres to measure hemisphere-specific components due to asymmetric expression of environmental and genetic factors. The results show that our model is biologically plausible and that our proposed metric of hemispheric specialization is reliable, representing a wide range of individual variation. Together, this work provides promising tools for exploring brain asymmetry and new insights into self-supervised DGNs for representing the brain.

Brain structural indicators of β-amyloid neuropathology

Recent efforts demonstrated the efficacy of identifying early-stage neuropathology of Alzheimer's disease (AD) through lumbar puncture cerebrospinal fluid assessment and positron emission tomography (PET) radiotracer imaging. These methods are effective yet are invasive, expensive, and not widely accessible. We extend and improve the multiscale structural mapping (MSSM) procedure to develop structural indicators of β-amyloid neuropathology in preclinical AD, by capturing both macrostructural and microstructural properties throughout the cerebral cortex using a structural MRI. We find that the MSSM signal is regionally altered in clear positive and negative cases of preclinical amyloid pathology (N = 220) when cortical thickness alone or hippocampal volume is not. It exhibits widespread effects of amyloid positivity across the posterior temporal, parietal, and medial prefrontal cortex, surprisingly consistent with the typical pattern of amyloid deposition. The MSSM signal is significantly correlated with amyloid PET in almost half of the cortex, much of which overlaps with regions where beta-amyloid accumulates, suggesting it could provide a regional brain 'map' that is not available from systemic markers such as plasma markers.

Lateralized brunt of sleep deprivation on white matter injury in a rat model of Alzheimer's disease

Sleep disturbance is a recognized risk factor for Alzheimer's disease (AD), but the underlying micro-pathological evidence remains limited. To bridge this gap, we established an amyloid-β oligomers (AβO)-induced rat model of AD and subjected it to intermittent sleep deprivation (SD). Diffusion tensor imaging (DTI) and transmission electron microscopy were employed to assess white matter (WM) integrity and ultrastructural changes in myelin sheaths. Our findings demonstrated that SD exacerbated AβO-induced cognitive decline. Furthermore, we found SD aggravated AβO-induced asymmetrical impairments in WM, presenting with reductions in tract integrity observed in commissural fibers and association fasciculi, particularly the right anterior commissure, right corpus callosum, and left cingulum. Ultrastructural changes in myelin sheaths within the hippocampus and corpus callosum further confirmed a lateralized effect. Moreover, SD worsened AβO-induced lateralized disruption of the brain structural network, with impairments in critical nodes of the left hemisphere strongly correlated with cognitive dysfunction. This work represents the first identification of a lateralized impact of SD on the mesoscopic network and cognitive deficits in an AD rat model. These findings could deepen our understanding of the complex interplay between sleep disturbance and AD pathology, providing valuable insights into the early progression of the disease, as well as the development of neuroimaging biomarkers for screening early AD patients with self-reported sleep disturbances. Enhanced understanding of these mechanisms may pave the way for targeted interventions to alleviate cognitive decline and improve the quality of life for individuals at risk of or affected by AD.

Brain gray matter morphometry relates to onset age of bilingualism and theory of mind in young and older adults

Lifelong bilingualism may result in neural reserve against decline not only in the general cognitive domain, but also in social cognitive functioning. In this study, we show the brain structural correlates that are associated with second language age of acquisition (L2AoA) and theory of mind (the ability to reason about mental states) in normal aging. Participants were bilingual adults (46 young, 50 older) who completed a theory-of-mind task battery, a language background questionnaire, and an anatomical MRI scan to obtain cortical morphometric features (i.e., gray matter volume, thickness, and surface area). Findings indicated a theory-of-mind decline in older adults compared to young adults, controlling for education and general cognition. Importantly, earlier L2AoA and better theory-of-mind performance were associated with larger volume, higher thickness, and larger surface area in the bilateral temporal, medial temporal, superior parietal, and prefrontal brain regions. These regions are likely to be involved in mental representations, language, and cognitive control. The morphometric association with L2AoA in young and older adults were comparable, but its association with theory of mind was stronger in older adults than young adults. The results demonstrate that early bilingual acquisition may provide protective benefits to intact theory-of-mind abilities against normal age-related declines.

Sex- and region-specific cortical and hippocampal whole genome transcriptome profiles from control and APP/PS1 Alzheimer's disease mice

A variety of Alzheimer's disease (AD) mouse models has been established and characterized within the last decades. To get an integrative view of the sophisticated etiopathogenesis of AD, whole genome transcriptome studies turned out to be indispensable. Here we carried out microarray data collection based on RNA extracted from the retrosplenial cortex and hippocampus of age-matched, eight months old male and female APP/PS1 AD mice and control animals to perform sex- and brain region specific analysis of transcriptome profiles. The results of our studies reveal novel, detailed insight into differentially expressed signature genes and related fold changes in the individual APP/PS1 subgroups. Gene ontology and Venn analysis unmasked that intersectional, upregulated genes were predominantly involved in, e.g., activation of microglial, astrocytic and neutrophilic cells, innate immune response/immune effector response, neuroinflammation, phagosome/proteasome activation, and synaptic transmission. The number of (intersectional) downregulated genes was substantially less in the different subgroups and related GO categories included, e.g., the synaptic vesicle docking/fusion machinery, synaptic transmission, rRNA processing, ubiquitination, proteasome degradation, histone modification and cellular senescence. Importantly, this is the first study to systematically unravel sex- and brain region-specific transcriptome fingerprints/signature genes in APP/PS1 mice. The latter will be of central relevance in future preclinical and clinical AD related studies, biomarker characterization and personalized medicinal approaches.

Brain asymmetries from mid- to late life and hemispheric brain age

The human brain demonstrates structural and functional asymmetries which have implications for ageing and mental and neurological disease development. We used a set of magnetic resonance imaging (MRI) metrics derived from structural and diffusion MRI data in N=48,040 UK Biobank participants to evaluate age-related differences in brain asymmetry. Most regional grey and white matter metrics presented asymmetry, which were higher later in life. Informed by these results, we conducted hemispheric brain age (HBA) predictions from left/right multimodal MRI metrics. HBA was concordant to conventional brain age predictions, using metrics from both hemispheres, but offers a supplemental general marker of brain asymmetry when setting left/right HBA into relationship with each other. In contrast to WM brain asymmetries, left/right discrepancies in HBA are lower at higher ages. Our findings outline various sex-specific differences, particularly important for brain age estimates, and the value of further investigating the role of brain asymmetries in brain ageing and disease development.

Depth- and curvature-based quantitative susceptibility mapping analyses of cortical iron in Alzheimer's disease

In addition to amyloid beta plaques and neurofibrillary tangles, Alzheimer's disease (AD) has been associated with elevated iron in deep gray matter nuclei using quantitative susceptibility mapping (QSM). However, only a few studies have examined cortical iron, using more macroscopic approaches that cannot assess layer-specific differences. Here, we conducted column-based QSM analyses to assess whether AD-related increases in cortical iron vary in relation to layer-specific differences in the type and density of neurons. We obtained global and regional measures of positive (iron) and negative (myelin, protein aggregation) susceptibility from 22 adults with AD and 22 demographically matched healthy controls. Depth-wise analyses indicated that global susceptibility increased from the pial surface to the gray/white matter boundary, with a larger slope for positive susceptibility in the left hemisphere for adults with AD than controls. Curvature-based analyses indicated larger global susceptibility for adults with AD versus controls; the right hemisphere versus left; and gyri versus sulci. Region-of-interest analyses identified similar depth- and curvature-specific group differences, especially for temporo-parietal regions. Finding that iron accumulates in a topographically heterogenous manner across the cortical mantle may help explain the profound cognitive deterioration that differentiates AD from the slowing of general motor processes in healthy aging.

A multiscale characterization of cortical shape asymmetries in early psychosis

Psychosis has often been linked to abnormal cortical asymmetry, but prior results have been inconsistent. Here, we applied a novel spectral shape analysis to characterize cortical shape asymmetries in patients with early psychosis across different spatial scales. We used the Human Connectome Project for Early Psychosis dataset (aged 16-35), comprising 56 healthy controls (37 males, 19 females) and 112 patients with early psychosis (68 males, 44 females). We quantified shape variations of each hemisphere over different spatial frequencies and applied a general linear model to compare differences between healthy controls and patients with early psychosis. We further used canonical correlation analysis to examine associations between shape asymmetries and clinical symptoms. Cortical shape asymmetries, spanning wavelengths from about 22 to 75 mm, were significantly different between healthy controls and patients with early psychosis (Cohen's d = 0.28-0.51), with patients showing greater asymmetry in cortical shape than controls. A single canonical mode linked the asymmetry measures to symptoms (canonical correlation analysis r = 0.45), such that higher cortical asymmetry was correlated with more severe excitement symptoms and less severe emotional distress. Significant group differences in the asymmetries of traditional morphological measures of cortical thickness, surface area, and gyrification, at either global or regional levels, were not identified. Cortical shape asymmetries are more sensitive than other morphological asymmetries in capturing abnormalities in patients with early psychosis. These abnormalities are expressed at coarse spatial scales and are correlated with specific symptom domains.

A deep learning model for brain age prediction using minimally preprocessed T1w images as input

Introduction

In the last few years, several models trying to calculate the biological brain age have been proposed based on structural magnetic resonance imaging scans (T1-weighted MRIs, T1w) using multivariate methods and machine learning. We developed and validated a convolutional neural network (CNN)-based biological brain age prediction model that uses one T1w MRI preprocessing step when applying the model to external datasets to simplify implementation and increase accessibility in research settings. Our model only requires rigid image registration to the MNI space, which is an advantage compared to previous methods that require more preprocessing steps, such as feature extraction.

Methods

We used a multicohort dataset of cognitively healthy individuals (age range = 32.0-95.7 years) comprising 17,296 MRIs for training and evaluation. We compared our model using hold-out (CNN1) and cross-validation (CNN2-4) approaches. To verify generalisability, we used two external datasets with different populations and MRI scan characteristics to evaluate the model. To demonstrate its usability, we included the external dataset's images in the cross-validation training (CNN3). To ensure that our model used only the brain signal on the image, we also predicted brain age using skull-stripped images (CNN4).

Results

The trained models achieved a mean absolute error of 2.99, 2.67, 2.67, and 3.08 years for CNN1-4, respectively. The model's performance in the external dataset was in the typical range of mean absolute error (MAE) found in the literature for testing sets. Adding the external dataset to the training set (CNN3), overall, MAE is unaffected, but individual cohort MAE improves (5.63-2.25 years). Salience maps of predictions reveal that periventricular, temporal, and insular regions are the most important for age prediction.

Discussion

We provide indicators for using biological (predicted) brain age as a metric for age correction in neuroimaging studies as an alternative to the traditional chronological age. In conclusion, using different approaches, our CNN-based model showed good performance using one T1w brain MRI preprocessing step. The proposed CNN model is made publicly available for the research community to be easily implemented and used to study ageing and age-related disorders.

Age-related changes in the primary auditory cortex of newborn, adults and aging bottlenose dolphins (Tursiops truncatus) are located in the upper cortical layers

Introduction

The auditory system of dolphins and whales allows them to dive in dark waters, hunt for prey well below the limit of solar light absorption, and to communicate with their conspecific. These complex behaviors require specific and sufficient functional circuitry in the neocortex, and vicarious learning capacities. Dolphins are also precocious animals that can hold their breath and swim within minutes after birth. However, diving and hunting behaviors are likely not innate and need to be learned. Our hypothesis is that the organization of the auditory cortex of dolphins grows and mature not only in the early phases of life, but also in adults and aging individuals. These changes may be subtle and involve sub-populations of cells specificall linked to some circuits.

Methods

In the primary auditory cortex of 11 bottlenose dolphins belonging to three age groups (calves, adults, and old animals), neuronal cell shapes were analyzed separately and by cortical layer using custom computer vision and multivariate statistical analysis, to determine potential minute morphological differences across these age groups.

Results

The results show definite changes in interneurons, characterized by round and ellipsoid shapes predominantly located in upper cortical layers. Notably, neonates interneurons exhibited a pattern of being closer together and smaller, developing into a more dispersed and diverse set of shapes in adulthood.

Discussion

This trend persisted in older animals, suggesting a continuous development of connections throughout the life of these marine animals. Our findings further support the proposition that thalamic input reach upper layers in cetaceans, at least within a cortical area critical for their survival. Moreover, our results indicate the likelihood of changes in cell populations occurring in adult animals, prompting the need for characterization.