Analyses of microstructural variation in the human striatum using non-negative matrix factorization

Midlife dynamics of white matter architecture in lexical production

We aimed to examine the white matter changes associated with lexical production difficulties, beginning in midlife with increased naming latencies. To delay lexical production decline, middle-aged adults may rely on domain-general and language-specific compensatory mechanisms proposed by the LARA model (Lexical Access and Retrieval in Aging). However, the white matter changes supporting these mechanisms remains largely unknown. Using data from the CAMCAN cohort, we employed an unsupervised and data-driven methodology to examine the relationships between diffusion-weighted imaging and lexical production. Our findings indicate that midlife is marked by alterations in brain structure within distributed dorsal, ventral, and anterior cortico-subcortical networks, marking the onset of lexical production decline around ages 53-54. Middle-aged adults may initially adopt a "semantic strategy" to compensate for lexical production challenges, but this strategy seems compromised later (ages 55-60) as semantic control declines. These insights underscore the interplay between domain-general and language-specific processes in the trajectory of lexical production performance in healthy aging and hint at potential biomarkers for language-related neurodegenerative pathologies.

Neuroanatomical and cognitive biomarkers of alpha-synuclein propagation in a mouse model of synucleinopathy prior to onset of motor symptoms

Significant evidence suggests that misfolded alpha-synuclein (aSyn), a major component of Lewy bodies, propagates in a prion-like manner contributing to disease progression in Parkinson's disease (PD) and other synucleinopathies. In fact, timed inoculation of M83 hemizygous mice with recombinant human aSyn preformed fibrils (PFF) has shown symptomatic deficits after substantial spreading of pathogenic alpha-synuclein, as detected by markers for the phosphorylation of S129 of aSyn. However, whether accumulated toxicity impact human-relevant cognitive and structural neuroanatomical measures is not fully understood. Here we performed a single unilateral striatal PFF injection in M83 hemizygous mice, and using two assays with translational potential, ex vivo magnetic resonance imaging (MRI) and touchscreen testing, we examined the combined neuroanatomical and behavioral impact of aSyn propagation. In PFF-injected mice, we observed widespread atrophy in bilateral regions that project to or receive input from the injection site using MRI. We also identified early deficits in reversal learning prior to the emergence of motor symptoms. Our findings highlight a network of regions with related cellular correlates of pathology that follow the progression of aSyn spreading, and that affect brain areas relevant for reversal learning. Our experiments suggest that M83 hemizygous mice injected with human PFF provides a model to understand how misfolded aSyn affects human-relevant pre-clinical measures and suggest that these pre-clinical biomarkers could be used to detect early toxicity of aSyn and provide better translational measures between mice and human disease.

Development of white matter fiber covariance networks supports executive function in youth

During adolescence, the brain undergoes extensive changes in white matter structure that support cognition. Data-driven approaches applied to cortical surface properties have led the field to understand brain development as a spatially and temporally coordinated mechanism that follows hierarchically organized gradients of change. Although white matter development also appears asynchronous, previous studies have relied largely on anatomical tract-based atlases, precluding a direct assessment of how white matter structure is spatially and temporally coordinated. Harnessing advances in diffusion modeling and machine learning, we identified 14 data-driven patterns of covarying white matter structure in a large sample of youth. Fiber covariance networks aligned with known major tracts, while also capturing distinct patterns of spatial covariance across distributed white matter locations. Most networks showed age-related increases in fiber network properties, which were also related to developmental changes in executive function. This study delineates data-driven patterns of white matter development that support cognition.

Assessment of white matter hyperintensity severity using multimodal magnetic resonance imaging

White matter hyperintensities are radiological abnormalities reflecting cerebrovascular dysfunction detectable using MRI. White matter hyperintensities are often present in individuals at the later stages of the lifespan and in prodromal stages in the Alzheimer's disease spectrum. Tissue alterations underlying white matter hyperintensities may include demyelination, inflammation and oedema, but these are highly variable by neuroanatomical location and between individuals. There is a crucial need to characterize these white matter hyperintensity tissue alterations in vivo to improve prognosis and, potentially, treatment outcomes. How different MRI measure(s) of tissue microstructure capture clinically-relevant white matter hyperintensity tissue damage is currently unknown. Here, we compared six MRI signal measures sampled within white matter hyperintensities and their associations with multiple clinically-relevant outcomes, consisting of global and cortical brain morphometry, cognitive function, diagnostic and demographic differences and cardiovascular risk factors. We used cross-sectional data from 118 participants: healthy controls (n = 30), individuals at high risk for Alzheimer's disease due to familial history (n = 47), mild cognitive impairment (n = 32) and clinical Alzheimer's disease dementia (n = 9). We sampled the median signal within white matter hyperintensities on weighted MRI images [T1-weighted (T1w), T2-weighted (T2w), T1w/T2w ratio, fluid-attenuated inversion recovery (FLAIR)] as well as the relaxation times from quantitative T1 (qT1) and T2* (qT2*) images. qT2* and fluid-attenuated inversion recovery signals within white matter hyperintensities displayed different age- and disease-related trends compared to normal-appearing white matter signals, suggesting sensitivity to white matter hyperintensity-specific tissue deterioration. Further, white matter hyperintensity qT2*, particularly in periventricular and occipital white matter regions, was consistently associated with all types of clinically-relevant outcomes in both univariate and multivariate analyses and across two parcellation schemes. qT1 and fluid-attenuated inversion recovery measures showed consistent clinical relationships in multivariate but not univariate analyses, while T1w, T2w and T1w/T2w ratio measures were not consistently associated with clinical variables. We observed that the qT2* signal was sensitive to clinically-relevant microstructural tissue alterations specific to white matter hyperintensities. Our results suggest that combining volumetric and signal measures of white matter hyperintensity should be considered to fully characterize the severity of white matter hyperintensities in vivo. These findings may have implications in determining the reversibility of white matter hyperintensities and the potential efficacy of cardio- and cerebrovascular treatments.

Scalable Orthonormal Projective NMF via Diversified Stochastic Optimization

The increasing availability of large-scale neuroimaging initiatives opens exciting opportunities for discovery science of human brain structure and function. Data-driven techniques, such as Orthonormal Projective Non-negative Matrix Factorization (opNMF), are well positioned to explore multivariate relationships in big data towards uncovering brain organization. opNMF enjoys advantageous interpretability and reproducibility compared to commonly used matrix factorization methods like Principal Component Analysis (PCA) and Independent Component Analysis (ICA), which led to its wide adoption in clinical computational neuroscience. However, applying opNMF in large-scale cohort studies is hindered by its limited scalability caused by its accompanying computational complexity. In this work, we address the computational challenges of opNMF using a stochastic optimization approach that learns over mini-batches of the data. Additionally, we diversify the stochastic batches via repulsive point processes, which reduce redundancy in the mini-batches and in turn lead to lower variance in the updates. We validated our framework on gray matter tissue density maps estimated from 1000 subjects part of the Open Access Series of Imaging (OASIS) dataset. We demonstrated that operations over mini-batches of data yield significant reduction in computational cost. Importantly, we showed that our novel optimization does not compromise the accuracy or interpretability of factors when compared to standard opNMF. The proposed model enables new investigations of brain structure using big neuroimaging data that could improve our understanding of brain structure in health and disease.

Development of White Matter Fiber Covariance Networks Supports Executive Function in Youth

The white matter architecture of the human brain undergoes substantial development throughout childhood and adolescence, allowing for more efficient signaling between brain regions that support executive function. Increasingly, the field understands grey matter development as a spatially and temporally coordinated mechanism that follows hierarchically organized gradients of change. While white matter development also appears asynchronous, previous studies have largely relied on anatomical atlases to characterize white matter tracts, precluding a direct assessment of how white matter structure is spatially and temporally coordinated. Here, we leveraged advances in diffusion modeling and unsupervised machine learning to delineate white matter fiber covariance networks comprised of structurally similar areas of white matter in a cross-sectional sample of 939 youth aged 8-22 years. We then evaluated associations between fiber covariance network structural properties with both age and executive function using generalized additive models. The identified fiber covariance networks aligned with the known architecture of white matter while simultaneously capturing novel spatial patterns of coordinated maturation. Fiber covariance networks showed heterochronous increases in fiber density and cross section that generally followed hierarchically organized temporal patterns of cortical development, with the greatest increases in unimodal sensorimotor networks and the most prolonged increases in superior and anterior transmodal networks. Notably, we found that executive function was associated with structural features of limbic and association networks. Taken together, this study delineates data-driven patterns of white matter network development that support cognition and align with major axes of brain maturation.

Scalable NMF via linearly optimized data compression

Orthonormal projective non-negative matrix factorization (opNMF) has been widely used in neuroimaging and clinical neuroscience applications to derive representations of the brain in health and disease. The non-negativity and orthonormality constraints of opNMF result in intuitive and well-localized factors. However, the advantages of opNMF come at a steep computational cost that prohibits its use in large-scale data. In this work, we propose novel and scalable optimization schemes for orthonormal projective non-negative matrix factorization that enable the use of the method in large-scale neuroimaging settings. We replace the high-dimensional data matrix with its corresponding singular value decomposition (SVD) and QR decompositions and combine the decompositions with opNMF multiplicative update algorithm. Empirical validation of the proposed methods demonstrated significant speed-up in computation time while keeping memory consumption low without compromising the accuracy of the solution.

Inter- and intra-individual variation in brain structural-cognition relationships in aging

The sources of inter- and intra-individual variability in age-related cognitive decline remain poorly understood. We examined the association between 20-year trajectories of cognitive decline and multimodal brain structure and morphology in older age. We used the Whitehall II Study, an extensively characterised cohort with 3T brain magnetic resonance images acquired at older age (mean age = 69.52 ± 4.9) and 5 repeated cognitive performance assessments between mid-life (mean age = 53.2 ±4.9 years) and late-life (mean age = 67.7 ± 4.9). Using non-negative matrix factorization, we identified 10 brain components integrating cortical thickness, surface area, fractional anisotropy, and mean and radial diffusivities. We observed two latent variables describing distinct brain-cognition associations. The first describes variations in 5 structural components associated with low mid-life performance across multiple cognitive domains, decline in reasoning, but maintenance of fluency abilities. The second describes variations in 6 structural components associated with low mid-life performance in fluency and memory, but retention of multiple abilities. Expression of latent variables predicts future cognition 3.2 years later (mean age = 70.87 ± 4.9). This data-driven approach highlights brain-cognition relationships wherein individuals degrees of cognitive decline and maintenance across diverse cognitive functions are both positively and negatively associated with markers of cortical structure.

Application of non-negative matrix factorization in oncology: one approach for establishing precision medicine

The increase in the expectations of artificial intelligence (AI) technology has led to machine learning technology being actively used in the medical field. Non-negative matrix factorization (NMF) is a machine learning technique used for image analysis, speech recognition, and language processing; recently, it is being applied to medical research. Precision medicine, wherein important information is extracted from large-scale medical data to provide optimal medical care for every individual, is considered important in medical policies globally, and the application of machine learning techniques to this end is being handled in several ways. NMF is also introduced differently because of the characteristics of its algorithms. In this review, the importance of NMF in the field of medicine, with a focus on the field of oncology, is described by explaining the mathematical science of NMF and the characteristics of the algorithm, providing examples of how NMF can be used to establish precision medicine, and presenting the challenges of NMF. Finally, the direction regarding the effective use of NMF in the field of oncology is also discussed.