Faculty Bibliography

The WU-Minn Human Connectome Project (HCP) is a publicly-available dataset containing state-of-art structural, functional, and diffusion-MRI for over a thousand healthy subjects. While the planned scope of the HCP included an anatomical connectome, resting-state functional-MRI forms the bulk of the HCP's current connectomic output. We address this by presenting a full-cortex connectome derived from probabilistic diffusion tractography and organized into the HCP-MMP1.0 atlas. Probabilistic methods and large sample sizes are preferable for whole-connectome mapping as they increase the fidelity of traced low-probability connections. We find that overall, connection strengths are lognormally distributed and decay exponentially with tract length, that connectivity reasonably matches macaque histological tracing in homologous areas, that contralateral homologs and left-lateralized language areas are hyperconnected, and that hierarchical similarity influences connectivity. We compare the diffusion-MRI connectome to existing resting-state fMRI and cortico-cortico evoked potential connectivity matrices and find that it is more similar to the latter. This work helps fulfill the promise of the HCP and will make possible comparisons between the underlying structural connectome and functional connectomes of various modalities, brain states, and clinical conditions.Significance Statement The tracts between cortical parcels can be estimated from diffusion MRI, but most studies concentrate on only the largest connections. Here we present an atlas, the largest and most detailed of its kind, showing connections among all cortical parcels. Connectivity is relatively enhanced between frontotemporal language areas and homologous contralateral locations. We find that connectivity decays with fiber tract distance more slowly than predicted by brain volume and that structural and stimulation-derived connectivity are more similar to each other than to resting-state functional MRI correlations. The connectome presented is publicly available and organized into a commonly used scheme for defining brain areas in order to enable ready comparison to other brain imaging datasets of various modalities.

BACKGROUND:APOEɛ4 allele carriers present with increased risk for late-onset Alzheimer's disease (AD), show cognitive symptoms at earlier age, and are more likely to transition from mild cognitive impairment (MCI) to dementia but despite this, it remains unclear whether or not the ɛ4 allele controls the rate of disease progression. OBJECTIVE:To determine effects of the ɛ4 allele on rates of cognitive decline and brain atrophy during MCI and dementia stages of AD. METHODS:A segmented linear mixed model was chosen for longitudinal modeling of cognitive and brain volumetric data of 73 ɛ3/ɛ3, 99 ɛ3/ɛ4, and 39 ɛ4/ɛ4 Alzheimer's Disease Neuroimaging Initiative participants who transitioned during the study from MCI to AD dementia. RESULTS:ɛ4 carriers showed faster decline on MMSE, ADAS-11, CDR-SB, and MoCA scales, with the last two measures showing significant ɛ4 allele-dose effects after dementia transition but not during MCI. The ɛ4 effect was more prevalent in younger participants and in females. ɛ4 carriers also demonstrated faster rates of atrophy of the whole brain, the hippocampus, the entorhinal cortex, the middle temporal gyrus, and expansion of the ventricles after transitioning to dementia but not during MCI. CONCLUSION/CONCLUSIONS:Possession of the ɛ4 allele is associated with a faster progression of dementia due to AD. Our observations support the notion that APOE genotype not only controls AD risk but also differentially regulates mechanisms of neurodegeneration underlying disease advancement. Furthermore, our findings carry significance for AD clinical trial design.

BACKGROUND:There is an increased effort to better understand neuropsychiatric symptoms of Alzheimer's disease (AD) as an important feature of symptomatic burden as well as potential modi- fiable factors of the disease process. Anxiety is one of the most common neuropsychiatric symptoms in Alzheimer's disease (AD). A growing body of work has emerged that addresses the epidemiology and biological correlations of anxiety in AD. OBJECTIVE AND METHODS/OBJECTIVE:Here, we review human studies in research and clinical cohorts that examined anxiety in AD. We focused on work related to prevalence across AD stages, correlation with established biomarkers, relationship with AD neuropathology and genetic risk factors, and impact on progression. RESULTS:Anxiety is prominent in the early stages and increases across the spectrum of functional stages. Biomarker relationships are strongest at the level of FDG-PET and amyloid measured via PET or cerebrospinal fluid analysis. Neuropathologically, anxiety emerges with early Braak stage tau pathology. The presence of the apolipoprotein E e4 allele is associated with increased anxiety at all stages, most notably at mild cognitive impairment. Anxiety portended a faster progression at all pre-dementia stages. CONCLUSION/CONCLUSIONS:This body of work suggests a close biological relationship between anxiety and AD that begins in early stages and influences functional decline. As such, we discuss future work that would improve our understanding of this relationship and test the validity of anxiolytic treatment as disease modifying therapy for AD.

Recent findings challenge the prior notion that the cerebellum remains unaffected by Alzheimer's disease (AD). Yet, it is unclear whether AD exacerbates age-related cerebellar grey matter decline or engages distinct structural and functional territories. We performed a meta-analysis of cerebellar grey matter loss in normal ageing and AD. We mapped voxels with structural decline onto established brain networks, functional parcellations, and along gradients that govern the functional organisation of the cerebellum. Importantly, these gradients track continuous changes in cerebellar specialisation providing a more nuanced measure of the functional profile of regions vulnerable to ageing and AD. Gradient 1 progresses from motor to cognitive territories; Gradient 2 isolates attentional processing; Gradient 3 captures lateralisation differences in cognitive functions. We identified bilateral and right-lateralised posterior cerebellar atrophy in ageing and AD, respectively. Age- and AD-related structural decline only showed partial spatial overlap in right lobule VI/Crus I. Despite the seemingly distinct patterns of AD- and age-related atrophy, the functional profiles of these regions were similar. Both participate in the same macroscale networks (default mode, frontoparietal, attention), support executive functions and language processing, and did not exhibit a difference in relative positions along Gradients 1 or 2. However, Gradient 3 values were significantly different in ageing vs. AD, suggesting that the roles of left and right atrophied cerebellar regions exhibit subtle functional differences despite their membership in similar macroscale networks. These findings provide an unprecedented characterisation of structural and functional differences and similarities in cerebellar grey matter loss between normal ageing and AD.

The use of stem cell-derived dopamine neurons or deep brain stimulation (DBS) represents two alternative approaches to treat Parkinson's Disease. DBS is a widely used FDA-approved treatment and stem cell-derived dopamine neuron replacement has now evolved to the first in-human clinical trials. In this debate, we discuss which of these approaches will evolve to be the treatment of choice for Parkinsonian patients in the future.

Several COVID-19 vaccines have recently been approved for emergency use according to governmental immunization programs. The arrival of these vaccines has created hope for people with Parkinson's disease (PD), as this can help to mitigate their risk of becoming infected with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which can lead to serious, life-threatening disease, at least among those with more advanced PD. However, both persons with PD and physicians looking after these individuals have expressed concerns about the vaccine's efficacy and safety in the specific context of PD and its symptomatic treatment. Here, we discuss our perspective on these concerns, based on our interpretation of the literature plus the unfolding experience with widespread vaccination in the population at large. Because the benefits and risks of COVID-19 vaccines do not appear to be different than in the general population, we recommend COVID-19 vaccination with approved vaccines to persons with PD, unless there is a specific contraindication. Some caution seems warranted in very frail and terminally ill elderly persons with PD living in long-term care facilities.

Artificial intelligence has recently gained popularity across different medical fields to aid in the detection of diseases based on pathology samples or medical imaging findings. Brain magnetic resonance imaging (MRI) is a key assessment tool for patients with temporal lobe epilepsy (TLE). The role of machine learning and artificial intelligence to increase detection of brain abnormalities in TLE remains inconclusive. We used support vector machine (SV) and deep learning (DL) models based on region of interest (ROI-based) structural (n = 336) and diffusion (n = 863) brain MRI data from patients with TLE with ("lesional") and without ("non-lesional") radiographic features suggestive of underlying hippocampal sclerosis from the multinational (multi-center) ENIGMA-Epilepsy consortium. Our data showed that models to identify TLE performed better or similar (68-75%) compared to models to lateralize the side of TLE (56-73%, except structural-based) based on diffusion data with the opposite pattern seen for structural data (67-75% to diagnose vs. 83% to lateralize). In other aspects, structural and diffusion-based models showed similar classification accuracies. Our classification models for patients with hippocampal sclerosis were more accurate (68-76%) than models that stratified non-lesional patients (53-62%). Overall, SV and DL models performed similarly with several instances in which SV mildly outperformed DL. We discuss the relative performance of these models with ROI-level data and the implications for future applications of machine learning and artificial intelligence in epilepsy care.