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.

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.

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.

Rationale: Currently, there is some ambiguity over the role of postictal generalized electro-encephalographic suppression (PGES) as a biomarker in sudden unexpected death in epilepsy (SUDEP). Visual analysis of PGES, known to be subjective, may account for this. In this study, we set out to perform an analysis of PGES presence and duration using a validated signal processing tool, specifically to examine the association between PGES and seizure features previously reported to be associated with visually analyzed PGES. Methods: This is a prospective, multicenter epilepsy monitoring study of autonomic and breathing biomarkers of SUDEP in adult patients with intractable epilepsy. We studied videoelectroencephalogram (vEEG) recordings of generalized convulsive seizures (GCS) in a cohort of patients in whom respiratory and vEEG recording were carried out during the evaluation in the epilepsy monitoring unit. A validated automated EEG suppression detection tool was used to determine presence and duration of PGES. Results: We studied 148 GCS in 87 patients. PGES occurred in 106/148 (71.6%) seizures in 70/87 (80.5%) of patients. PGES mean duration was 38.7 ± 23.7 (37; 1-169) seconds. Presence of tonic phase during GCS, including decerebration, decortication and hemi-decerebration, were 8.29 (CI 2.6-26.39, p = 0.0003), 7.17 (CI 1.29-39.76, p = 0.02), and 4.77 (CI 1.25-18.20, p = 0.02) times more likely to have PGES, respectively. In addition, presence of decerebration (p = 0.004) and decortication (p = 0.02), older age (p = 0.009), and hypoxemia duration (p = 0.03) were associated with longer PGES durations. Conclusions: In this study, we confirmed observations made with visual analysis, that presence of tonic phase during GCS, longer hypoxemia, and older age are reliably associated with PGES. We found that of the different types of tonic phase posturing, decerebration has the strongest association with PGES, followed by decortication, followed by hemi-decerebration. This suggests that these factors are likely indicative of seizure severity and may or may not be associated with SUDEP. An automated signal processing tool enables objective metrics, and may resolve apparent ambiguities in the role of PGES in SUDEP and seizure severity studies.

Epilepsy is a common neurological disorder affecting over 70 million people worldwide, with a high rate of pharmaco-resistance, diverse comorbidities including progressive cognitive and behavioural disorders, and increased mortality from direct (e.g. sudden unexpected death in epilepsy, accidents, drowning) or indirect effects of seizures and therapies. Extensive research with animal models and human studies provides limited insights into the mechanisms underlying seizures and epileptogenesis, and these have not translated into significant reductions in pharmaco-resistance, morbidities or mortality. To help define changes in molecular signalling networks associated with seizures in epilepsy with a broad range of aetiologies, we examined the proteome of brain samples from epilepsy and control cases. Label-free quantitative mass spectrometry was performed on the hippocampal cornu ammonis 1-3 region (CA1-3), frontal cortex and dentate gyrus microdissected from epilepsy and control cases (n = 14/group). Epilepsy cases had significant differences in the expression of 777 proteins in the hippocampal CA1 - 3 region, 296 proteins in the frontal cortex and 49 proteins in the dentate gyrus in comparison to control cases. Network analysis showed that proteins involved in protein synthesis, mitochondrial function, G-protein signalling and synaptic plasticity were particularly altered in epilepsy. While protein differences were most pronounced in the hippocampus, similar changes were observed in other brain regions indicating broad proteomic abnormalities in epilepsy. Among the most significantly altered proteins, G-protein subunit beta 1 (GNB1) was one of the most significantly decreased proteins in epilepsy in all regions studied, highlighting the importance of G-protein subunit signalling and G-protein-coupled receptors in epilepsy. Our results provide insights into common molecular mechanisms underlying epilepsy across various aetiologies, which may allow for novel targeted therapeutic strategies.