Search

Understanding synergies between neurodegenerative and cerebrovascular pathologies that modify dementia presentation represents an important knowledge gap.This multi-site, longitudinal, observational cohort study recruited participants across prevalent neurodegenerative diseases and cerebrovascular disease and assessed participants comprehensively across modalities. We describe univariate and multivariate baseline features of the cohort and summarize recruitment, data collection, and curation processes.We enrolled 520 participants across five neurodegenerative and cerebrovascular diseases. Median age was 69 years, median Montreal Cognitive Assessment score was 25, median independence in activities of daily living was 100% for basic and 93% for instrumental activities. Spousal study partners predominated; participants were often male, White, and more educated. Milder disease stages predominated, yet cohorts reflect clinical presentation.Data will be shared with the global scientific community. Within-disease and disease-agnostic approaches are expected to identify markers of severity, progression, and therapy targets. Sampling characteristics also provide guidance for future study design.

Background Cerebral small vessel disease is associated with higher ratios of soluble‐epoxide hydrolase derived linoleic acid diols (12,13‐dihydroxyoctadecenoic acid [DiHOME] and 9,10‐DiHOME) to their parent epoxides (12(13)‐epoxyoctadecenoic acid [EpOME] and 9(10)‐EpOME); however, the relationship has not yet been examined in stroke. Methods and Results Participants with mild to moderate small vessel stroke or large vessel stroke were selected based on clinical and imaging criteria. Metabolites were quantified by ultra‐high‐performance liquid chromatography–mass spectrometry. Volumes of stroke, lacunes, white matter hyperintensities, magnetic resonance imaging visible perivascular spaces, and free water diffusion were quantified from structural and diffusion magnetic resonance imaging (3 Tesla). Adjusted linear regression models were used for analysis. Compared with participants with large vessel stroke (n=30), participants with small vessel stroke (n=50) had a higher 12,13‐DiHOME/12(13)‐EpOME ratio (β=0.251, P =0.023). The 12,13‐DiHOME/12(13)‐EpOME ratio was associated with more lacunes (β=0.266, P =0.028) but not with large vessel stroke volumes. Ratios of 12,13‐DiHOME/12(13)‐EpOME and 9,10‐DiHOME/9(10)‐EpOME were associated with greater volumes of white matter hyperintensities (β=0.364, P P P =0.011; β=0.314, P =0.006). In small vessel stroke, the 12,13‐DiHOME/12(13)‐EpOME ratio was associated with higher white matter free water diffusion (β=0.439, P =0.016), which was specific to the temporal lobe in exploratory regional analyses. The 9,10‐DiHOME/9(10)‐EpOME ratio was associated with temporal lobe atrophy (β=−0.277, P =0.031). Conclusions Linoleic acid markers of cytochrome P450/soluble‐epoxide hydrolase activity were associated with small versus large vessel stroke, with small vessel disease markers consistent with blood brain barrier and neurovascular‐glial disruption, and temporal lobe atrophy. The findings may indicate a novel modifiable risk factor for small vessel disease and related neurodegeneration.

Background Remote health monitoring with wearable sensor technology may positively impact patient self-management and clinical care. In individuals with complex health conditions, multi-sensor wear may yield meaningful information about health-related behaviors. Despite available technology, feasibility of device-wearing in daily life has received little attention in persons with physical or cognitive limitations. This mixed methods study assessed the feasibility of continuous, multi-sensor wear in persons with cerebrovascular (CVD) or neurodegenerative disease (NDD). Methods Thirty-nine participants with CVD, Alzheimer’s disease/amnestic mild cognitive impairment, frontotemporal dementia, Parkinson’s disease, or amyotrophic lateral sclerosis (median age 68 (45–83) years, 36% female) wore five devices (bilateral ankles and wrists, chest) continuously for a 7-day period. Adherence to device wearing was quantified by examining volume and pattern of device removal (non-wear). A thematic analysis of semi-structured de-brief interviews with participants and study partners was used to examine user acceptance. Results Adherence to multi-sensor wear, defined as a minimum of three devices worn concurrently, was high (median 98.2% of the study period). Non-wear rates were low across all sensor locations (median 17–22 min/day), with significant differences between some locations (p = 0.006). Multi-sensor non-wear was higher for daytime versus nighttime wear (p

Background: Neuropsychiatric symptoms (NPS) are a core feature of most neurodegenerative and cerebrovascular diseases. White matter hyperintensities and brain atrophy have been implicated in NPS. We aimed to investigate the relative contribution of white matter hyperintensities and cortical atrophy to NPS in participants across neurodegenerative and cerebrovascular diseases. Methods: 513 participants with one of these conditions, i.e. Alzheimer’s Disease/Mild Cognitive Impairment, Amyotrophic Lateral Sclerosis, Frontotemporal Dementia, Parkinson’s Disease, or Cerebrovascular Disease were included in the study. NPS were assessed using the Neuropsychiatric Inventory – Questionnaire and grouped into hyperactivity, psychotic, affective, and apathy subsyndromes. White matter hyperintensities were quantified using a semi-automatic segmentation technique and FreeSurfer cortical thickness was used to measure regional grey matter atrophy. Results: Although NPS were frequent across the five disease groups, participants with Frontotemporal Dementia had the highest frequency of hyperactivity, apathy, and affective subsyndromes compared to other groups, whilst psychotic subsyndrome was high in both Frontotemporal Dementia and Parkinson’s Disease. Results from univariate and multivariate results showed that various predictors were associated with neuropsychiatric subsyndromes, especially cortical thickness in the inferior frontal, cingulate, and insula regions, sex(female), global cognition, and basal ganglia-thalamus white matter hyperintensities. Conclusions: In participants with neurodegenerative and cerebrovascular diseases, our results suggest that increased cortical atrophy and white matter hyperintensities burden in several cortical-subcortical structures may contribute to the development of NPS. Further studies investigating the mechanisms that determine the progression of NPS in various neurodegenerative and cerebrovascular diseases are needed.

Objectives: Caregiving burdens are a substantial concern in the clinical care of persons with neurodegenerative disorders. In the Ontario Neurodegenerative Disease Research Initiative, we used the Zarit's Burden Interview (ZBI) to examine: (1) the types of burdens captured by the ZBI in a cross-disorder sample of neurodegenerative conditions (2) whether there are categorical or disorder-specific effects on caregiving burdens, and (3) which demographic, clinical, and cognitive measures are related to burden(s) in neurodegenerative disorders?. Methods/Design: N = 504 participants and their study partners (e.g., family, friends) across: Alzheimer's disease/mild cognitive impairment (AD/MCI; n = 120), Parkinson's disease (PD; n = 136), amyotrophic lateral sclerosis (ALS; n = 38), frontotemporal dementia (FTD; n = 53), and cerebrovascular disease (CVD; n = 157). Study partners provided information about themselves, and information about the clinical participants (e.g., activities of daily living (ADL)). We used Correspondence Analysis to identify types of caregiving concerns in the ZBI. We then identified relationships between those concerns and demographic and clinical measures, and a cognitive battery. Results: We found three components in the ZBI. The first was “overall burden” and was (1) strongly related to increased neuropsychiatric symptoms (NPI severity r = 0.586, NPI distress r = 0.587) and decreased independence in ADL (instrumental ADLs r = −0.566, basic ADLs r = −0.43), (2) moderately related to cognition (MoCA r = −0.268), and (3) showed little-to-no differences between disorders. The second and third components together showed four types of caregiving concerns: current care of the person with the neurodegenerative disease, future care of the person with the neurodegenerative disease, personal concerns of study partners, and social concerns of study partners. Conclusions: Our results suggest that the experience of caregiving in neurodegenerative and cerebrovascular diseases is individualized and is not defined by diagnostic categories. Our findings highlight the importance of targeting ADL and neuropsychiatric symptoms with caregiver-personalized solutions.

BACKGROUND White matter hyperintensities (WMH) on magnetic resonance imaging may influence clinical presentation in patients with Parkinson's disease (PD), although their significance and pathophysiological origins remain unresolved. Studies examining WMH have identified pathogenic variants in NOTCH3 as an underlying cause of inherited forms of cerebral small vessel disease. METHODS We examined NOTCH3 variants, WMH volumes, and clinical correlates in 139 PD patients in the Ontario Neurodegenerative Disease Research Initiative cohort. RESULTS We identified 13 PD patients (~9%) with rare (

Background:Vascular cognitive impairment (VCI) post-stroke is frequent but may go undetected, which highlights the need to better screen cognitive functioning following a stroke.Aim:We examined the clinical utility of the Montreal Cognitive Assessment (MoCA) in detecting cognitive impairment against a gold-standard neuropsychological battery.Methods:We assessed cognitive status with a comprehensive battery of neuropsychological tests in 161 individuals who were at least 3-months post-stroke. We used receiver operating characteristic (ROC) curves to identify two cut points for the MoCA to maximize sensitivity and specificity at a minimum 90% threshold. We examined the utility of the Symbol Digit Modalities Test, a processing speed measure, to determine whether this additional metric would improve classification relative to the MoCA total score alone.Results:Using two cut points, 27% of participants scored ≤ 23 and were classified as high probability of cognitive impairment (sensitivity 92%), and 24% of participants scored ≥ 28 and were classified as low probability of cognitive impairment (specificity 91%). The remaining 48% of participants scored from 24 to 27 and were classified as indeterminate probability of cognitive impairment. The addition of a processing speed measure improved classification for the indeterminate group by correctly identifying 65% of these individuals, for an overall classification accuracy of 79%.Conclusions:The utility of the MoCA in detecting cognitive impairment post-stroke is improved when using a three-category approach. The addition of a processing speed measure provides a practical and efficient method to increase confidence in the determined outcome while minimally extending the screening routine for VCI.

As large research initiatives designed to generate big data on clinical cohorts become more common, there is an increasing need to establish standard quality assurance (QA; preventing errors) and quality control (QC; identifying and correcting errors) procedures for critical outcome measures. The present article describes the QA and QC approach developed and implemented for the neuropsychology data collected as part of the Ontario Neurodegenerative Disease Research Initiative study. We report on the efficacy of our approach and provide data quality metrics. Our findings demonstrate that even with a comprehensive QA protocol, the proportion of data errors still can be high. Additionally, we show that several widely used neuropsychological measures are particularly susceptible to error. These findings highlight the need for large research programs to put into place active, comprehensive, and separate QA and QC procedures before, during, and after protocol deployment. Detailed recommendations and considerations for future studies are provided.

ObjectiveTo assess overlap and uniqueness of established behavioral markers of speed of processing for different aspects of visual information within a cerebrovascular disease cohort, and to examine the link between these speed of processing markers and functional behavior, specifically walking.MethodsA cohort of 161 participants with cerebrovascular disease recruited to the Ontario Neurodegenerative Disease Research Initiative (ONDRI) were examined with three types of assessments: neuropsychology, saccadic eye movement and gait. Principal component analysis (PCA) and canonical correlation analysis (CCA) were performed on select variables from these assessments to reveal commonalities and discrepancies among the measures.ResultsPCA analysis revealed different variable patterns between neuropsychology and saccade assessments, with the first component characterized primarily by neuropsychology, and the second and third components more influenced by the saccade assessments. CCA analysis did not reveal association between different types of assessments with the exception of a modest, but significant, positive association between speed of processing measures from the neuropsychological assessments and gait speed.DiscussionNeuropsychological tests and the pro-saccade task can be used for assessment of speed of processing for two major features of visual information, visual perception vs. spatial location. Despite a general lack of association between different types of assessments, combining gait speed as an important contributor to the models reinforces the idea of the link between speed of processing and complex function such as walking, and provides support for the importance of attending to the potential consequences of changes in speed of processing after neurologic injury.

Introduction: Change in empathy is an increasingly recognised symptom of neurodegenerative diseases and contributes to caregiver burden and patient distress. Empathy impairment has been associated with brain atrophy but its relationship to white matter hyperintensities (WMH) is unknown. We aimed to investigate the relationships amongst WMH, brain atrophy, and empathy deficits in neurodegenerative and cerebrovascular diseases.Methods: 513 participants with Alzheimer’s Disease/Mild Cognitive Impairment, Amyotrophic Lateral Sclerosis, Frontotemporal Dementia (FTD), Parkinson’s Disease, or Cerebrovascular Disease (CVD) were included. Empathy was assessed using the Interpersonal Reactivity Index. WMH were measured using a semi-automatic segmentation and FreeSurfer was used to measure cortical thickness.Results: A heterogeneous pattern of cortical thinning was found between groups, with FTD showing thinning in frontotemporal regions and CVD in left superior parietal, left insula, and left postcentral. Results from both univariate and multivariate analyses revealed that several variables were associated with empathy, particularly cortical thickness in the fronto-insulo-temporal and cingulate regions, sex(female), global cognition, and right parietal and occipital WMH.Conclusions: Our results suggest that cortical atrophy and WMH may be associated with empathy deficits in neurodegenerative and cerebrovascular diseases. Future work should consider investigating the longitudinal effects of WMH and atrophy on empathy deficits in neurodegenerative and cerebrovascular diseases.

In this chapter we offer a survey of self-organizing feature maps with emphasis on recent advances, and more specifically, on growing architectures. Several of the methods are developed by the authors and offer unique combination of theoretical fundamentals and neural network architectures. Included in this survey of dynamic architectures, will also be examples of application domains, usage and resources for learners and researchers alike, to pursue their interest in SOMs. The primary reason for pursuing this branch of machine learning, is that these techniques are unsupervised – requiring no a priori knowledge or trainer. As such, SOMs lend themselves readily to difficult problem domains in machine learning, such as clustering, pattern identification and recognition and feature extraction. SOMs utilize competitive neural network learning algorithms introduced by Kohonen in the early 1980’s. SOMs maintain the features (in terms of vectors) of the input space the network is observing. This chapter, as work emphasizing dynamic architectures, will be incomplete without presenting the significant achievements in SOMs including the work of Fritzke and his growing architectures. To exemplify more modern approaches we present state-of-the art developments in SOMs. These approaches include parallelization (ParaSOM – as developed by the authors), incremental learning (ESOINN), connection reorganization (TurSOM – as developed by the authors), and function space organization (mnSOM). Additionally, we introduce some methods of analyzing SOMs. These include methods for measuring the quality of SOMs with respect to input, neighbors and map size. We also present techniques of posterior recognition, clustering and input feature significance. In summary, this chapter presents a modern gamut of self-organizing neural networks, and measurement and analysis techniques.

Objectives Recent studies suggest that interindividual genetic differences in glial-dependent CSF flow through the brain parenchyma, known as glymphatic flow, may trigger compensatory changes in human sleep physiology. In animal models, brain perivascular spaces are a critical conduit for glymphatic flow. We tested the hypothesis that MRI-visible PVS volumes, a putative marker of perivascular dysfunction, are associated with compensatory differences in real-world human sleep behavior. Methods We analyzed data from 152 cerebrovascular disease patients from the Ontario Neurodegenerative Disease Research Initiative (ONDRI). PVS volumes were measured using 3T-MRI. Self-reported total sleep time, time in bed, and daytime dysfunction were extracted from the Pittsburgh Sleep Quality Index. Results Individuals with greater PVS volumes reported longer time in bed (+0.85 h per log10 proportion of intracranial volume (ICV) occupied by PVS, SE = 0.30, p = 0.006) and longer total sleep times (+0.70 h per log10 proportion of ICV occupied by PVS volume, SE = 0.33, p = 0.04), independent of vascular risk factors, sleep apnea, nocturnal sleep disturbance, depression, and global cognitive status. Further analyses suggested that the positive association between PVS volumes and total sleep time was mediated by greater time in bed. Moreover, despite having on average greater total sleep times, individuals with greater basal ganglia PVS volumes were more likely to report daytime dysfunction (OR 5.63 per log10 proportion of ICV occupied by PVS, 95% CI: 1.38–22.26, p = 0.018). Conclusions Individuals with greater PVS volumes spend more time in bed, resulting in greater total sleep time, which may represent a behavioral compensatory response to perivascular space dysfunction.

BackgroundRegional changes to cortical thickness in individuals with neurodegenerative and cerebrovascular diseases can be estimated using specialised neuroimaging software. However, the presence of cerebral small vessel disease, focal atrophy, and cortico-subcortical stroke lesions, pose significant challenges that increase the likelihood of misclassification errors and segmentation failures.PurposeThe main goal of this study was to examine a correction procedure developed for enhancing FreeSurfer’s cortical thickness estimation tool, particularly when applied to the most challenging MRI obtained from participants with chronic stroke and cerebrovascular disease, with varying degrees of neurovascular lesions and brain atrophy.MethodsIn 155 cerebrovascular disease patients enrolled in the Ontario Neurodegenerative Disease Research Initiative (ONDRI), FreeSurfer outputs were compared between a fully automated, unmodified procedure and a corrected procedure that accounted for potential sources of error due to atrophy and neurovascular lesions. Quality control (QC) measures were obtained from both procedures. Association between cortical thickness and global cognitive status as assessed by the Montreal Cognitive Assessment (MoCA) score was also investigated from both procedures.ResultsCorrected procedures increased ‘Acceptable’ QC ratings from 18% to 76% for the cortical ribbon and from 38% to 92% for tissue segmentation. Corrected procedures reduced ‘Fail’ ratings from 11% to 0% for the cortical ribbon and 62% to 8% for tissue segmentation. FreeSurfer-based segmentation of T1-weighted white matter hypointensities were significantly greater in the corrected procedure (5.8mL vs. 15.9mL, pConclusionsThese findings suggest that correction procedures that account for brain atrophy and neurovascular lesions can significantly improve FreeSurfer’s segmentation results, reduce failure rates, and potentially increase sensitivity to examine brain-behaviour relationships. Future work will examine relationships between cortical thickness, cerebral small vessel disease, and neurodegenerative disease in the ONDRI study.

ObjectiveIn individuals over the age of 65, concomitant neurodegenerative pathologies contribute to cognitive and/or motor decline and can be aggravated by cerebrovascular disease, but our understanding of how these pathologies synergize to produce the decline represents an important knowledge gap. The Ontario Neurodegenerative Disease Research Initiative (ONDRI), a multi-site, longitudinal, observational cohort study, recruited participants across multiple prevalent neurodegenerative diseases and cerebrovascular disease, collecting a wide array of data and thus allowing for deep investigation into common and unique phenotypes. This paper describes baseline features of the ONDRI cohort, understanding of which is essential when conducting analyses or interpreting results.MethodsFive disease cohorts were recruited: Alzheimer’s disease/amnestic mild cognitive impairment (AD/MCI), amyotrophic lateral sclerosis (ALS), frontotemporal dementia (FTD), Parkinson’s disease (PD), and cerebrovascular disease (CVD). Assessment platforms included clinical, neuropsychology, eye tracking, gait and balance, neuroimaging, retinal imaging, genomics, and pathology. We describe recruitment, data collection, and data curation protocols, and provide a summary of ONDRI baseline characteristics.Results520 participants were enrolled. Most participants were in the early stages of disease progression. Participants had a median age of 69 years, a median Montreal Cognitive Assessment score of 25, a median percent of independence of 100 for basic activities of daily living, and a median of 93 for instrumental activities. Variation between disease cohorts existed for age, level of cognition, and geographic location.ConclusionONDRI data will enable exploration into unique and shared pathological mechanisms contributing to cognitive and motor decline across the spectrum of neurodegenerative diseases.

Objectives: In the Ontario Neurodegenerative Disease Research Initiative (ONDRI), we aimed to ask and answer: (1) How many and what types of burdens are captured by the Zarit’s Burden Interview (ZBI)? (2) Do we see categorical or spectrum-like effects for burden(s)? and (3) Which if any demographic, clinical, and cognitive measures are related to burden(s)?Methods: N = 504 participants and their study partners (e.g., family, friends) across: Alzheimer’s disease/mild cognitive impairment (AD/MCI; n = 120), Parkinson’s disease (PD; n = 136), amyotrophic lateral sclerosis (ALS; n = 38), frontotemporal dementia (FTD; n = 53), and cerebrovascular disease (CVD; n = 157). Study partners provided information about themselves, and information about the clinical participants (e.g., activities of daily living). We used Correspondence Analysis to identify types of caregiving concerns in the ZBI, then identified relationships between those concerns and demographic and clinical measures, and a cognitive battery.Results: We found three components in the ZBI. The first was “overall burden” and was (1) strongly related to increased neuropsychiatric symptoms and decreased independence in activities of daily living, (2) moderately related to cognition, and (3) showed little-to-no differences between disorders. The second and third components showed four types of caregiving concerns: current care of patient, future care of patient, personal concerns of study partner, and social concerns of study partner. Discussion: Caregiving concerns are individual experiences and emphasize the importance of support for management of activities of daily living and neuropsychiatric symptoms, and underscore individualized needs for caregiving assessment and education.

The brain's endocannabinoid system plays a crucial role in reward processes by mediating appetitive learning and encoding the reinforcing properties of substances. Evidence also suggests that endocannabinoids are an important constituent of neuronal substrates involved in emotional responses to stress. Thus, it is critical to understand how the endocannabinoid system and stress may affect reward processes given their importance in substance use disorders. We examined the relationship between factors that regulate endocannabinoid system signaling (i.e., cannabinoid receptor genes and prolonged cannabis exposure) and stress on fMRI BOLD response to reward cues using multivariate statistical analysis. We found that proxies for endocannabinoid system signaling (i.e., endocannabinoid genes and chronic exposure to cannabis) and stress have differential effects on neural response to cannabis cues. Specifically, a single nucleotide polymorphism (SNP) variant in the cannabinoid receptor 1 (CNR1) gene, early life stress, and current perceived stress modulated reward responsivity in long-term, heavy cannabis users, while a variant in the fatty acid amide hydrolase (FAAH) gene and current perceived stress modulated cue-elicited response in non-using controls. These associations were related to distinct neural responses to cannabis-related cues compared to natural reward cues. Understanding the contributions of endocannabinoid system factors and stress that lead to downstream effects on neural mechanisms underlying sensitivity to rewards, such as cannabis, will contribute towards a better understanding of endocannabinoid-targeted therapies as well as individual risks for cannabis use disorder.

International audience; Multiple Correspondence Analysis (MCA) is the method of choicefor themultivariate analysis of categorical data. In MCA each qualitative variable is representedby a group of binary variables (with a coding scheme called “complete disjunctive coding”)and each binary variable has a weight inversely proportional to its frequency. The datamatrix concatenates all these binary variables, and once normalized and centered thisdata matrix is analyzed with a generalized singular value decomposition (GSVD) thatincorporates the variable weights as constraints (or “metric”). The GSVD is, of course,based on the plain SVD and so MCA can be sparsified by extending algorithms designedto sparsify the SVD. To do so requires two additional features: to include weights andto be able to sparsify entire groups of variables at once. Another important feature ofsuch a sparsification should be to preserve the orthogonality of the components, Here, weintegrate all these constraints by using an exact projection scheme onto the intersectionof subspaces (i.e., balls) where each ball represents a specific type of constraints. Weillustrate our procedure with the data from a questionnaire survey on the perception ofcheese in two French cities.

Botvinik-Nezer, Rotem et al., Data analysis workflows in many scientific domains have become increasingly complex and flexible. Here we assess the effect of this flexibility on the results of functional magnetic resonance imaging by asking 70 independent teams to analyse the same dataset, testing the same 9 ex-ante hypotheses. The flexibility of analytical approaches is exemplified by the fact that no two teams chose identical workflows to analyse the data. This flexibility resulted in sizeable variation in the results of hypothesis tests, even for teams whose statistical maps were highly correlated at intermediate stages of the analysis pipeline. Variation in reported results was related to several aspects of analysis methodology. Notably, a meta-analytical approach that aggregated information across teams yielded a significant consensus in activated regions. Furthermore, prediction markets of researchers in the field revealed an overestimation of the likelihood of significant findings, even by researchers with direct knowledge of the dataset. Our findings show that analytical flexibility can have substantial effects on scientific conclusions, and identify factors that may be related to variability in the analysis of functional magnetic resonance imaging. The results emphasize the importance of validating and sharing complex analysis workflows, and demonstrate the need for performing and reporting multiple analyses of the same data. Potential approaches that could be used to mitigate issues related to analytical variability are discussed., European Union’s Horizon 2020 research and innovation programme (https://ec.europa.eu/programmes/horizon2020/en) under the Marie Skłodowska-Curie grant agreement no. 665501. A. Eed was supported by a predoctoral fellowship La Caixa-Severo Ochoa from Obra Social La Caixa. S.B.E. was supported by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement no. 785907 (HBP SGA2).

The Ontario Neurodegenerative Research Initiative (ONDRI) is a 3 year multi-site prospective cohort study that has acquired comprehensive multiple assessment platform data, including 3T structural MRI, from neurodegenerative patients with Alzheimer’s disease, mild cognitive impairment, Parkinson’s disease, amyotrophic lateral sclerosis, frontotemporal dementia, and cerebrovascular disease patients. This heterogeneous cross-section of patients with complex neurodegenerative and neurovascular pathologies pose significant challenges for standard neuroimaging tools. To effectively quantify regional measures of normal and pathological brain tissue volumes, the ONDRI neuroimaging platform implemented a semi-automated MRI processing pipeline that was able to address many of the challenges resulting from this heterogeneity. This paper describes the comprehensive neuroimaging pipeline methods used to generate regional brain tissue volumes & neurovascular markers.

The present and future of large scale studies of human brain and behavior—in typical and disease populations—is “mutli-omics”, “deep-phenotyping”, or other types of multi-source and multi-domain data collection initiatives. These massive studies rely on highly interdisciplinary teams that collect extremely diverse types of data across numerous systems and scales of measurement (e.g., genetics, brain structure, behavior, and demographics). Such large, complex, and heterogeneous data requires relatively simple methods that allow for flexibility in analyses without the loss of the inherent properties of various data types. Here we introduce a method designed specifically to address these problems: partial least squares-correspondence analysis-regression (PLS-CA-R). PLS-CA-R generalizes PLS regression for use with virtually any data type (e.g., continuous, ordinal, categorical, non-negative values). Though the primary emphasis here is on a PLS-regression approach generalized for data types, we also show that PLS-CA-R leads to additional generalizations of many routine “two-table” multivariate techniques and their respective algorithms, such as various PLS approaches, canonical correlation analysis, and redundancy analysis (a.k.a. reduced rank regression).