Canonical Correlation Analysis and Partial Least Squares for identifying brain-behaviour associations: a tutorial and a comparative study

Canonical Correlation Analysis (CCA) and Partial Least Squares (PLS) are powerful multivariate methods for capturing associations across two modalities of data (e.g., brain and behaviour). However, when the sample size is similar or smaller than the number of variables in the data, CCA and PLS models may overfit, i.e., find spurious associations that generalise poorly to new data. Dimensionality reduction and regularized extensions of CCA and PLS have been proposed to address this problem, yet most studies using these approaches have some limitations. This work gives a theoretical and practical introduction into the most common CCA/PLS models and their regularized variants. We examine the limitations of standard CCA and PLS when the sample size is similar or smaller than the number of variables. We discuss how dimensionality reduction and regularization techniques address this problem and explain their main advantages and disadvantages. We highlight crucial aspects of the CCA/PLS analysis framework, including optimising the hyperparameters of the model and testing the identified associations for statistical significance. We apply the described CCA/PLS models to simulated data and real data from the Human Connectome Project and the Alzheimer's Disease Neuroimaging Initiative (both of n>500). We use both low and high dimensionality versions of each data (i.e., ratios between sample size and variables in the range of ∼1-10 and ∼0.1-0.01) to demonstrate the impact of data dimensionality on the models. Finally, we summarize the key lessons of the tutorial

Estimating frontal and parietal involvement in cognitive estimation: a study of focal neurodegenerative diseases

We often estimate an unknown value based on available relevant information, a process known as cognitive estimation. In this study, we assess the cognitive and neuroanatomic basis for quantitative estimation by examining deficits in patients with focal neurodegenerative disease in frontal and parietal cortex. Executive function and number knowledge are key components in cognitive estimation. Prefrontal cortex has been implicated in multilevel reasoning and planning processes, and parietal cortex has been associated with number knowledge required for such estimations. We administered the Biber Cognitive Estimation Test (BCET) to assess cognitive estimation in 22 patients with prefrontal disease due to behavioral variant frontotemporal dementia (bvFTD), to 17 patients with parietal disease due to corticobasal syndrome (CBS) or posterior cortical atrophy (PCA) and 11 patients with mild cognitive impairment (MCI). Both bvFTD and CBS/PCA patients had significantly more difficulty with cognitive estimation than controls. MCI were not impaired on BCET relative to controls. Regression analyses related BCET performance to gray matter atrophy in right lateral prefrontal and orbital frontal cortices in bvFTD, and to atrophy in right inferior parietal cortex, right insula and fusiform cortices in CBS/PCA. These results are consistent with the hypothesis that a frontal-parietal network plays a crucial role in cognitive estimation

Multivariate MR Biomarkers Better Predict Cognitive Dysfunction in Mouse Models of Alzheimers Disease

To understand multifactorial conditions such as Alzheimers disease (AD) we need brain signatures that predict the impact of multiple pathologies and their interactions. To help uncover the relationships between brain circuits and cognitive markers we have used mouse models that represent, at least in part, the complex interactions altered in AD. In particular, we aimed to understand the relationship between vulnerable brain circuits and memory deficits measured in the Morris water maze, and we tested several predictive modeling approaches. We used in vivo manganese enhanced MRI voxel based analyses to reveal regional differences in volume (morphometry), signal intensity (activity), and magnetic susceptibility (iron deposition, demyelination). These regions included the hippocampus, olfactory areas, entorhinal cortex and cerebellum. The image based properties of these regions were used to predict spatial memory. We next used eigenanatomy, which reduces dimensionality to produce sets of regions that explain the variance in the data. For each imaging marker, eigenanatomy revealed networks underpinning a range of cognitive functions including memory, motor function, and associative learning. Finally, the integration of multivariate markers in a supervised sparse canonical correlation approach outperformed single predictor models and had significant correlates to spatial memory. Among a priori selected regions, the fornix also provided good predictors, raising the possibility of investigating how disease propagation within brain networks leads to cognitive deterioration. Our results support that modeling approaches integrating multivariate imaging markers provide sensitive predictors of AD-like behaviors. Such strategies for mapping brain circuits responsible for behaviors may help in the future predict disease progression, or response to interventions.Comment: 23 pages, 3 Tables, 6 Figures; submitted for publicatio

Adaptação e evidências de validade do teste neuropsicológico PBAC para a população brasileira

Tese (doutorado)—Universidade de Brasília, Faculdade de Ciências da Saúde, Programa de Pós-Graduação em Ciências da Saúde, 2014.Instrumentos de rastreio cognitivos são utilizados em todo o mundo por vários profissionais de saúde para detectar indivíduos com provável comprometimento cognitivo. O teste PBAC(Philadelphia Brief Assessment of Cognition) foi proposto a partir de uma série de instrumentos neuropsicológicos bem estabelecidos na literatura para avaliar comportamento social, memória,linguagem, percepção visoespacial e funções executivas. A literatura aponta que os subtestes do PBAC são correlacionados com perda de massa encefálica em pacientes com doenças neurodegenerativas. Este estudo tem como objetivo verificar evidências de validade da adaptação brasileira do teste de rastreio cognitivo PBAC usando amostras clínicas e populacionais, comparando-o a outros testes de rastreio cognitivo e funcionais. Participaram deste estudo 325 voluntários entre 18 e 94 anos de idade (mediana de 65), com escolaridade entre 0 e 19 anos (mediana de 13). Quatro amostras distintas foram utilizadas: 103 jovens universitários, 183 idosos hígidos, 24 pacientes portadores de demências (sendo mais comum a doença de Alzheimer) e 16 Kalungas moradores da região de Cavalcante-GO, descendentes dos quilombolas. Outros instrumentos de rastreio cognitivo (MEEM, Teste do Relógio, Trilhas A e B,Stroop) e funcionais (GDS e Pfeffer) foram utilizados numa sub amostra para a validação convergente e discriminante. Análises especiais de modelagem de equação estrutural Bayesiana(BSEM) e teoria de resposta ao item multidimensionais (TRIm) e multigrupo foram utilizadas. O teste PBAC possui boa confiabilidade na amostra geral (alfa = 0,86), mas não na amostra de jovens (0,37). Quatro dimensões distintas foram encontradas: memória, linguagem, habilidade visoconstrutiva e funções executivas. O modelo BSEM mostrou para o grupo de idosos hígidos que a idade influencia negativamente a memória, mas não as demais habilidades. A escolaridade apresentou influência positiva em todos os constructos latentes. Com exceção dos itens Escrita e Trilhas, todos os demais apresentaram maior informação nas habilidades latentes dos idosos com demência, quando comparados aos hígidos nas funções de informações da TRI multigrupo.Tabelas normativas com a habilidade (teta), escore z e escala T foram calculadas para cada dimensão do teste, tendo como norma os dados dos idosos hígidos. O teste de rastreio cognitivo PBAC se mostrou tão sensível e específico quanto o MEEM para a detecção de demência do tipo Alzheimer. O teste é mais informativo em pessoas com habilidades latentes mais baixas. A retirada de alguns itens do teste pode aumentar sua acurácia para o diagnóstico de Alzheimer,no entanto mais estudos com outros grupos clínicos se tornam essenciais, uma vez que sua construção objetivou a avaliação multiclínica. ________________________________________________________________________________ ABSTRACTCognitive screening tests are used worldwide by many health professionals to detect individuals with probable cognitive impairment. The PBAC (Philadelphia Brief Assessment of Cognition) was proposed based on a series of well-established neuropsychological instruments to assess socialbehavior/comportment, memory, language, visuospatial perception, and executive functions. The literature indicates that the subtests of the PBAC are correlated with loss of brain tissue in patients with several neurodegenerative diseases. This study aims to find evidence of validation of the PBAC using clinical and population samples, and comparing it to other cognitive and functional screening tests. Participated in this study 325 individuals aged between 18 and 94 years-old(median 65), and schooling between 0 and 19 years (median 13). Four different samples were used: 103 university students, 183 healthy elderly, 24 patients with dementia (most common being Alzheimer's disease), and 16 afro-descendants (called Kalungas, living at Cavalcante-GO). All participants signed a consent form. Other cognitive screening instruments (MMSE, Clock Design Test, Trail Tests A and B, and Victoria Stroop Test) and functional tests (Pfeffer and GDS) were used in a subsample for the convergent and discriminant validity. Special analysis of Bayesianstructural equation modeling (BSEM), multi-group and multidimensional response theory item(mIRT) were used. The PBAC test showed good reliability in the overall sample (alpha = 0.86),but not in the youth sample (0.37). Four distinct dimensions were found: memory, language,executive functions, and visuos patial perception. The BSEM model showed that in healthy elderly people, age negatively influences memory, but not other skills. Education had a positive influence on all latent constructs. Except Writing and Oral Trails, all other items presented greaterinformation on the latent abilities for the clinical group when compared to the information functions of the healthy elderly group. Normative tables with the ability (theta), z scale, and T scores were calculated for each dimension of the test, using the healthy elderly normative data. The PBAC cognitive screening test proved to be as sensitive and specific as the MMSE for detecting cognitive disorders associated to Alzheimer’s dementia. The test is more informative on those individuals with lower latent abilities. The withdrawal of some items can increase PBAC’saccuracy for the diagnosis of Alzheimer's, however further studies with other clinical groups are essential since its construction aimed to evaluate other kind of neurodegenerative disease

Linking Functional Brain Networks To Psychopathology And Beyond

Neurobiological abnormalities associated with neuropsychiatric disorders do not map well to existing diagnostic categories. High co-morbidity suggests dimensional circuit-level abnormalities that cross diagnoses. As neuropsychiatric disorders are increasingly reconceptualized as disorders of brain development, deviations from normative brain network reconfiguration during development are hypothesized to underlie many illness that arise in young adulthood. In this dissertation, we first applied recent advances in machine learning to a large imaging dataset of youth (n=999) to delineate brain-guided dimensions of psychopathology across clinical diagnostic boundaries. Specifically, using sparse Canonical Correlation Analysis, an unsupervised learning method that seeks to capture sources of variation common to two high-dimensional datasets, we discovered four linked dimensions of psychopathology and connectivity in functional brain networks, namely, mood, psychosis, fear, and externalizing behavior. While each dimension exhibited an unique pattern of functional brain connectivity, loss of network segregation between the default mode and executive networks emerged as a shared connectopathy common across four dimensions of psychopathology. Building upon this work, in the second part of the dissertation, we designed, implemented, and deployed a new penalized statistical learning approach, Multi-Scale Network Regression (MSNR), to study brain network connectivity and a wide variety of phenotypes, beyond psychopathology. MSNR explicitly respects both edge- and community-level information by assuming a low rank and sparse structure, both encouraging less complex and more interpretably modeling. Capitalizing on a large neuroimaging cohort (n=1,051), we demonstrated that MSNR recapitulated interpretably and statistically significant associations between functional connectivity patterns with brain development, sex differences, and motion-related artifacts. Compared to common single-scale approaches, MSNR achieved a balance between prediction performance and model complexity, with improved interpretability. Together, integrating recent advances in multiple disciplines across machine learning, network science, developmental neuroscience, and psychiatry, this body of work fits into the broader context of computational psychiatry, where there is intense interest in the quest of delineating brain network patterns associated with psychopathology, among a diverse range of phenotypes

Aging and Alzheimer's Disease: Multimodal Investigation of Image-derived Biomarkers

Aging is an inevitable process in life and the primary risk factor for most neurodegenerative diseases including Alzheimer’s disease (AD), the most common form of dementia. As life expectancy continues to increase, AD prevalence is expected to rise. AD-related pathological processes unfold decades before the emergence of clinical signs of cognitive decline and involve brain changes such as atrophy, accumulation of amyloid-beta plaques and tau neurofibrillary tangles (NFT), synaptic and neuronal loss, demyelination, and iron accumulation that would eventually lead to cognitive impairment. Here, to assess brain myelin and iron content in vivo, quantitative MRI (qMRI) maps like magnetization transfer saturation (MTsat), Effective transverse relaxation rate (R2*), and proton density (PD) were used. And synaptic density was measured using the total volume distribution map (Vt) of [F18] UCB-H PET images. In this thesis, we examined the simultaneous occurrence of these brain changes in aging and AD, identifying significant differences in the hippocampus and amygdala. Demyelination emerged as a key distinguishing factor between AD and healthy groups. The effects of age on various brain characteristics were re-evaluated in a multivariate model, with proton density being the most age-related factor in healthy aging. Finally, we attempted to examine the association of cognitive performance and the rate of cognitive decline with qMRI maps and GM and WM volume. The univariate regression analyses at baseline revealed correlations between different cognitive scores and brain tissue properties within the cerebellum, hippocampus, middle temporal, and medial orbitofrontal cortex. Moreover, the multivariate analysis shows that cognitive performance was related to combined tissue properties in the middle frontal gyrus, insula, and cerebellum. There were only a few results for the rate of cognitive decline, with univariate correlations within the left fusiform between longitudinal relaxation rate (R1) maps in GM and attention and memory decline. To conclude, our findings shed light on the complex relationships between changes in aging and AD brains. Furthermore, we emphasize the importance of multivariate analysis for detecting subtle microstructural changes associated with aging that may motivate interventions to mitigate cognitive decline in older adults.Aging and Alzheimer’s Disease: Multimodal Investigation of Image-derived Biomarkers3. Good health and well-bein

Bridging the clinicopathological gap in the frontotemporal dementia spectrum

Frontotemporal dementia (FTD) is a currently incurable neurodegenerative disease comprising a spectrum of clinical syndromes and underlying neuropathologies. Each neuropathology is characterized by the accumulation of a specific protein (tau, TDP-43) in the brain, which cannot be easily detected during life nor predicted by clinical syndromes, hampering proper disease diagnosis. This thesis contributes to bridging this diagnostic gap by studying brain tissue from patients with FTD who underwent autopsy, and blood biomarkers in families carrying genetic variants associated with FTD. First, through pathological studies, we find that the two main underlying neuropathologies, TDP-43 and tau, show a different regional distribution in the brains of patients with FTD. Next, in a specific subtype of tau pathology, that associated with MAPT genetic variants, we observe a strongly heterogeneous pathological profile, in part explained by the biochemical structure of tau aggregates. Moreover, we find evidence of iron accumulation, associated with neuroinflammation, in two different subtypes of FTD (with MAPT and C9orf72 genetic variants), and we uncover neurovascular dysfunction as a core disease mechanism in TDP-43 pathology associated with GRN genetic variants. Finally, we study blood biomarkers suggestive of brain neurodegeneration. We find that the protein neurofilament light chain (in short, NfL) is helpful to diagnose the early (prodromal) stage of genetic FTD, and we identify altered methylation of cell-free DNA as a potential presymptomatic marker of neuronal death