Interactive Effects of Physical Activity and APOE-ε4 On White Matter Tract Diffusivity in Healthy Elders

Older adult apolipoprotein-E epsilon 4 (APOE-ε4) allele carriers vary considerably in the expression of clinical symptoms of Alzheimer\u27s disease (AD), suggesting that lifestyle or other factors may offer protection from AD-related neurodegeneration. We recently reported that physically active APOE-ε4 allele carriers exhibit a stable cognitive trajectory and protection from hippocampal atrophy over 18 months compared to sedentary ε4 allele carriers. The aim of this study was to examine the interactions between genetic risk for AD and physical activity (PA) on white matter (WM) tract integrity, using diffusion tensor imaging (DTI) MRI, in this cohort of healthy older adults (ages of 65 to 89). Four groups were compared based on the presence or absence of an APOE-ε4 allele (High Risk; Low Risk) and self-reported frequency and intensity of leisure time physical activity (PA) (High PA; Low PA). As predicted, greater levels of PA were associated with greater fractional anisotropy (FA) and lower radial diffusivity in healthy older adults who did not possess the APOE-ε4 allele. However, the effects of PA were reversed in older adults who were at increased genetic risk for AD, resulting in significant interactions between PA and genetic risk in several WM tracts. In the High Risk-Low PA participants, who had exhibited episodic memory decline over the previous 18-months, radial diffusivity was lower and fractional anisotropy was higher, compared to the High Risk-High PA participants. In WM tracts that subserve learning and memory processes, radial diffusivity (DR) was negatively correlated with episodic memory performance in physically inactive APOE-ε4 carriers, whereas DR was positively correlated with episodic memory performance in physically active APOE-ε4 carriers and the two Low Risk groups. The common model of demyelination-induced increase in radial diffusivity cannot directly explain these results. Rather, we hypothesize that PA may protect APOE-ε4 allele carriers from selective neurodegeneration of individual fiber populations at locations of crossing fibers within projection and association WM fiber tracts

The kynurenine pathway as a therapeutic target in cognitive and neurodegenerative disorders

Understanding the neurochemical basis for cognitive function is one of the major goals of neuroscience, with a potential impact on the diagnosis, prevention and treatment of a range of psychiatric and neurological disorders. In this review, the focus will be on a biochemical pathway that remains under-recognised in its implications for brain function, even though it can be responsible for moderating the activity of two neurotransmitters fundamentally involved in cognition – glutamate and acetylcholine. Since this pathway – the kynurenine pathway of tryptophan metabolism - is induced by immunological activation and stress it also stands in an unique position to mediate the effects of environmental factors on cognition and behaviour. Targetting the pathway for new drug development could, therefore, be of value not only for the treatment of existing psychiatric conditions, but also for preventing the development of cognitive disorders in response to environmental pressures

Digital twin brain: a bridge between biological intelligence and artificial intelligence

In recent years, advances in neuroscience and artificial intelligence have paved the way for unprecedented opportunities for understanding the complexity of the brain and its emulation by computational systems. Cutting-edge advancements in neuroscience research have revealed the intricate relationship between brain structure and function, while the success of artificial neural networks highlights the importance of network architecture. Now is the time to bring them together to better unravel how intelligence emerges from the brain's multiscale repositories. In this review, we propose the Digital Twin Brain (DTB) as a transformative platform that bridges the gap between biological and artificial intelligence. It consists of three core elements: the brain structure that is fundamental to the twinning process, bottom-layer models to generate brain functions, and its wide spectrum of applications. Crucially, brain atlases provide a vital constraint, preserving the brain's network organization within the DTB. Furthermore, we highlight open questions that invite joint efforts from interdisciplinary fields and emphasize the far-reaching implications of the DTB. The DTB can offer unprecedented insights into the emergence of intelligence and neurological disorders, which holds tremendous promise for advancing our understanding of both biological and artificial intelligence, and ultimately propelling the development of artificial general intelligence and facilitating precision mental healthcare

Synaptic loss in the primary tauopathies of Progressive Supranuclear Palsy and Corticobasal Degeneration

In this thesis I address the debilitating symptom of cognitive dysfunction in the primary tauopathies of Progressive Supranuclear Palsy (PSP) and Corticobasal Degeneration (CBD). Both PSP and CBD are associated with an accumulation of 4-repeat tau in cortical and subcortical areas. As well as movement disorders, they impair cognitive function, even where there is minimal atrophy. Neurophysiological studies have also identified electrophysiological changes associated with cognitive dysfunction, in areas without atrophy. I propose that synaptic loss prior to cell loss contributes to these effects of disease. Chapter two summarises my cohort and principal methods. I quantify synaptic density in vivo with dynamic [11C]UCB-J PET, and molecular pathology with [18F]AV1451 PET. Brain structural changes are quantified by MRI. Disease severity and cognition are assessed with the PSP rating scale, and neuropsychological tests. Patients with CBD are negative on amyloid-imaging ([11C]PiB PET) to exclude those with Alzheimer’s pathology. In chapter three, [11C]UCB-J PET reveals widespread loss of synapses in PSP and CBD including areas with minimal atrophy. The loss of synapses correlated with cognition and disease severity. In chapter four, I test whether presynaptic changes (from [11C]UCB-J PET) are correlated with postsynaptic abnormalities (i.e. changes to postsynaptic dendritic microstructural integrity quantified by MRI using the Neurite Orientation and Dispersion Index, NODDI). In accordance with in vitro and animal models, I confirm that loss of dendritic complexity is tightly coupled with presynaptic density, over and above the effects of atrophy. In chapter five, I test the relationship between the molecular pathology in primary tauopathies (tau burden) and synaptic loss, using [18F]AV-1451 and [11C]UCB-J PET. The use of the “tau” ligand [18F]AV-1451 has become controversial in PSP. With due consideration to the caveats, I report that brain regions with a higher synaptic density have higher [18F]AV-1451 binding, consistent with the hypothesis of connectivity-based progression of tauopathy. I further show that accrual of pathology in any given area is associated with loss of synapses, consistent with synaptic injury from tauopathy. I conclude my thesis in chapter 6, by discussing and highlighting the importance of synaptic density in primary tauopathies. The findings are relevant to other neurodegenerative disorders, and support early interventional studies targeting synaptic maintenance and restoration.Association of British Neurologists - Patrick Berthoud Charitable Trus

The white matter connectome as an individualized biomarker of language impairment in temporal lobe epilepsy.

ObjectiveThe distributed white matter network underlying language leads to difficulties in extracting clinically meaningful summaries of neural alterations leading to language impairment. Here we determine the predictive ability of the structural connectome (SC), compared with global measures of white matter tract microstructure and clinical data, to discriminate language impaired patients with temporal lobe epilepsy (TLE) from TLE patients without language impairment.MethodsT1- and diffusion-MRI, clinical variables (CVs), and neuropsychological measures of naming and verbal fluency were available for 82 TLE patients. Prediction of language impairment was performed using a robust tree-based classifier (XGBoost) for three models: (1) a CV-model which included demographic and epilepsy-related clinical features, (2) an atlas-based tract-model, including four frontotemporal white matter association tracts implicated in language (i.e., the bilateral arcuate fasciculus, inferior frontal occipital fasciculus, inferior longitudinal fasciculus, and uncinate fasciculus), and (3) a SC-model based on diffusion MRI. For the association tracts, mean fractional anisotropy was calculated as a measure of white matter microstructure for each tract using a diffusion tensor atlas (i.e., AtlasTrack). The SC-model used measurement of cortical-cortical connections arising from a temporal lobe subnetwork derived using probabilistic tractography. Dimensionality reduction of the SC was performed with principal components analysis (PCA). Each model was trained on 49 patients from one epilepsy center and tested on 33 patients from a different center (i.e., an independent dataset). Randomization was performed to test the stability of the results.ResultsThe SC-model yielded a greater area under the curve (AUC; .73) and accuracy (79%) compared to both the tract-model (AUC: .54, p < .001; accuracy: 70%, p < .001) and the CV-model (AUC: .59, p < .001; accuracy: 64%, p < .001). Within the SC-model, lateral temporal connections had the highest importance to model performance, including connections similar to language association tracts such as links between the superior temporal gyrus to pars opercularis. However, in addition to these connections many additional connections that were widely distributed, bilateral and interhemispheric in nature were identified as contributing to SC-model performance.ConclusionThe SC revealed a white matter network contributing to language impairment that was widely distributed, bilateral, and lateral temporal in nature. The distributed network underlying language may be why the SC-model has an advantage in identifying sub-components of the complex fiber networks most relevant for aspects of language performance

Characterization of age-related microstructural changes in locus coeruleus and substantia nigra pars compacta.

Locus coeruleus (LC) and substantia nigra pars compacta (SNpc) degrade with normal aging, but not much is known regarding how these changes manifest in MRI images, or whether these markers predict aspects of cognition. Here, we use high-resolution diffusion-weighted MRI to investigate microstructural and compositional changes in LC and SNpc in young and older adult cohorts, as well as their relationship with cognition. In LC, the older cohort exhibited a significant reduction in mean and radial diffusivity, but a significant increase in fractional anisotropy compared with the young cohort. We observed a significant correlation between the decrease in LC mean, axial, and radial diffusivities and measures examining cognition (Rey Auditory Verbal Learning Test delayed recall) in the older adult cohort. This observation suggests that LC is involved in retaining cognitive abilities. In addition, we observed that iron deposition in SNpc occurs early in life and continues during normal aging

Identifying Multimodal Intermediate Phenotypes between Genetic Risk Factors and Disease Status in Alzheimer’s Disease

Neuroimaging genetics has attracted growing attention and interest, which is thought to be a powerful strategy to examine the influence of genetic variants (i.e., single nucleotide polymorphisms (SNPs)) on structures or functions of human brain. In recent studies, univariate or multivariate regression analysis methods are typically used to capture the effective associations between genetic variants and quantitative traits (QTs) such as brain imaging phenotypes. The identified imaging QTs, although associated with certain genetic markers, may not be all disease specific. A useful, but underexplored, scenario could be to discover only those QTs associated with both genetic markers and disease status for revealing the chain from genotype to phenotype to symptom. In addition, multimodal brain imaging phenotypes are extracted from different perspectives and imaging markers consistently showing up in multimodalities may provide more insights for mechanistic understanding of diseases (i.e., Alzheimer’s disease (AD)). In this work, we propose a general framework to exploit multi-modal brain imaging phenotypes as intermediate traits that bridge genetic risk factors and multi-class disease status. We applied our proposed method to explore the relation between the well-known AD risk SNP APOE rs429358 and three baseline brain imaging modalities (i.e., structural magnetic resonance imaging (MRI), fluorodeoxyglucose positron emission tomography (FDG-PET) and F-18 florbetapir PET scans amyloid imaging (AV45)) from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. The empirical results demonstrate that our proposed method not only helps improve the performances of imaging genetic associations, but also discovers robust and consistent regions of interests (ROIs) across multi-modalities to guide the disease-induced interpretation

Gene-SGAN: a method for discovering disease subtypes with imaging and genetic signatures via multi-view weakly-supervised deep clustering

Disease heterogeneity has been a critical challenge for precision diagnosis and treatment, especially in neurologic and neuropsychiatric diseases. Many diseases can display multiple distinct brain phenotypes across individuals, potentially reflecting disease subtypes that can be captured using MRI and machine learning methods. However, biological interpretability and treatment relevance are limited if the derived subtypes are not associated with genetic drivers or susceptibility factors. Herein, we describe Gene-SGAN - a multi-view, weakly-supervised deep clustering method - which dissects disease heterogeneity by jointly considering phenotypic and genetic data, thereby conferring genetic correlations to the disease subtypes and associated endophenotypic signatures. We first validate the generalizability, interpretability, and robustness of Gene-SGAN in semi-synthetic experiments. We then demonstrate its application to real multi-site datasets from 28,858 individuals, deriving subtypes of Alzheimer's disease and brain endophenotypes associated with hypertension, from MRI and SNP data. Derived brain phenotypes displayed significant differences in neuroanatomical patterns, genetic determinants, biological and clinical biomarkers, indicating potentially distinct underlying neuropathologic processes, genetic drivers, and susceptibility factors. Overall, Gene-SGAN is broadly applicable to disease subtyping and endophenotype discovery, and is herein tested on disease-related, genetically-driven neuroimaging phenotypes