Amy Kuceyeski

Network Diffusion Model of Progression Predicts Longitudinal Patterns of Atrophy and Metabolism in Alzheimer’s Disease

Alzheimers disease pathology (AD) originates in the hippocampus and subsequently spreads to temporal, parietal, and prefrontal association cortices in a relatively stereotyped progression. Current evidence attributes this orderly progression to transneuronal transmission of misfolded proteins along the projection pathways of affected neurons. A network diffusion model was recently proposed to mathematically predict disease topography resulting from transneuronal transmission on the brains connectivity network. Here, we use this model to predict future patterns of regional atrophy and metabolism from baseline regional patterns of 418 subjects. The model accurately predicts end-of-study regional atrophy and metabolism starting from baseline data, with significantly higher correlation strength than given by the baseline statistics directly. The models rate parameter encapsulates overall atrophy progression rate; group analysis revealed this rate to depend on diagnosis as well as baseline cerebrospinal fluid (CSF) biomarker levels. This work helps validate the model as a prognostic tool for Alzheimers disease assessment.

The Network Modification (NeMo) Tool: Elucidating the Effect of White Matter Integrity Changes on Cortical and Subcortical Structural Connectivity

Accurate prediction of brain dysfunction caused by disease or injury requires the quantification of resultant neural connectivity changes compared with the normal state. There are many methods with which to assess anatomical changes in structural or diffusion magnetic resonance imaging, but most overlook the topology of white matter (WM) connections that make up the healthy brain network. Here, a new neuroimaging software pipeline called the Network Modification (NeMo) Tool is presented that associates alterations in WM integrity with expected changes in neural connectivity between gray matter regions. The NeMo Tool uses a large reference set of healthy tractograms to assess implied network changes arising from a particular pattern of WM alteration on a region- and network-wise level. In this way, WM integrity changes can be extrapolated to the cortices and deep brain nuclei, enabling assessment of functional and cognitive alterations. Unlike current techniques that assess network dysfunction, the NeMo tool does not require tractography in pathological brains for which the algorithms may be unreliable or diffusion data are unavailable. The versatility of the NeMo Tool is demonstrated by applying it to data from patients with Alzheimers disease, fronto-temporal dementia, normal pressure hydrocephalus, and mild traumatic brain injury. This tool fills a gap in the quantitative neuroimaging field by enabling an investigation of morphological and functional implications of changes in structural WM integrity.

Spatial patterns of genome‐wide expression profiles reflect anatomic and fiber connectivity architecture of healthy human brain

Unraveling the relationship between molecular signatures in the brain and their functional, architectonic, and anatomic correlates is an important neuroscientific goal. It is still not well understood whether the diversity demonstrated by histological studies in the human brain is reflected in the spatial patterning of whole brain transcriptional profiles. Using genome‐wide maps of transcriptional distribution of the human brain by the Allen Brain Institute, we test the hypothesis that gene expression profiles are specific to anatomically described brain regions. In this work, we demonstrate that this is indeed the case by showing that gene similarity clusters appear to respect conventional basal‐cortical and caudal‐rostral gradients. To fully investigate the causes of this observed spatial clustering, we test a connectionist hypothesis that states that the spatial patterning of gene expression in the brain is simply reflective of the fiber tract connectivity between brain regions. We find that although gene expression and structural connectivity are not determined by each other, they do influence each other with a high statistical significance. This implies that spatial diversity of gene expressions is a result of mainly location‐specific features but is influenced by neuronal connectivity, such that like cellular species preferentially connects with like cells. Hum Brain Mapp 35:4204–4218, 2014.

Predicting Future Brain Tissue Loss From White Matter Connectivity Disruption in Ischemic Stroke

Background and Purpose— The Network Modification (NeMo) Tool uses a library of brain connectivity maps from normal subjects to quantify the amount of structural connectivity loss caused by focal brain lesions. We hypothesized that the Network Modification Tool could predict remote brain tissue loss caused by poststroke loss of connectivity. Methods— Baseline and follow-up MRIs (10.7±7.5 months apart) from 26 patients with acute ischemic stroke (age, 74.6±14.1 years, initial National Institutes of Health Stroke Scale, 3.1±3.1) were collected. Lesion masks derived from diffusion-weighted images were superimposed on the Network Modification Tool’s connectivity maps, and regional structural connectivity losses were estimated via the Change in Connectivity (ChaCo) score (ie, the percentage of tracks connecting to a given region that pass through the lesion mask). ChaCo scores were correlated with subsequent atrophy. Results— Stroke lesions’ size and location varied, but they were more frequent in the left hemisphere. ChaCo scores, generally higher in regions near stroke lesions, reflected this lateralization and heterogeneity. ChaCo scores were highest in the postcentral and precentral gyri, insula, middle cingulate, thalami, putamen, caudate nuclei, and pallidum. Moderate, significant partial correlations were found between baseline ChaCo scores and measures of subsequent tissue loss (r=0.43, P=4.6×10–9; r=0.61, P=1.4×10–18), correcting for the time between scans. Conclusions— ChaCo scores varied, but the most affected regions included those with sensorimotor, perception, learning, and memory functions. Correlations between baseline ChaCo and subsequent tissue loss suggest that the Network Modification Tool could be used to identify regions most susceptible to remote degeneration from acute infarcts.

Reduced glucose uptake and Aβ in brain regions with hyperintensities in connected white matter

Interstitial concentration of amyloid beta (Aß) is positively related to synaptic activity in animal experiments. In humans, Aß deposition in Alzheimers disease overlaps with cortical regions highly active earlier in life. White matter lesions (WML) disrupt connections between gray matter (GM) regions which in turn changes their activation patterns. Here, we tested if WML are related to Aß accumulation (measured with PiB-PET) and glucose uptake (measured with FDG-PET) in connected GM. WML masks from 72 cognitively normal (age 61.7 ± 9.6 years, 71% women) individuals were obtained from T2-FLAIR. MRI and PET images were normalized into common space, segmented and parcellated into gray matter (GM) regions. The effects of WML on connected GM regions were assessed using the Change in Connectivity (ChaCo) score. Defined for each GM region, ChaCo is the percentage of WM tracts connecting to that region that pass through the WML mask. The regional relationship between ChaCo, glucose uptake and Aß was explored via linear regression. Subcortical regions of the bilateral caudate, putamen, calcarine, insula, thalamus and anterior cingulum had WM connections with the most lesions, followed by frontal, occipital, temporal, parietal and cerebellar regions. Regional analysis revealed that GM with more lesions in connecting WM and thus impaired connectivity had lower FDG-PET (r = 0.20, p<0.05 corrected) and lower PiB uptake (r = 0.28, p<0.05 corrected). Regional regression also revealed that both ChaCo (β = 0.045) and FDG-PET (β = 0.089) were significant predictors of PiB. In conclusion, brain regions with more lesions in connecting WM had lower glucose metabolism and lower Aß deposition.

Linking white matter integrity loss to associated cortical regions using structural connectivity information in Alzheimer's disease and fronto-temporal dementia: The Loss in Connectivity (LoCo) score

It is well known that gray matter changes occur in neurodegenerative diseases like Alzheimers (AD) and fronto-temporal dementia (FTD), and several studies have investigated their respective patterns of atrophy progression. Recent work, however, has revealed that diffusion MRI that is able to detect white matter integrity changes may be an earlier or more sensitive biomarker in both diseases. However, studies that examine white matter changes only are limited in that they do not provide the functional specificity of GM region-based analysis. In this study, we develop a new metric called the Loss in Connectivity (LoCo) score that gives the amount of structural network disruption incurred by a gray matter region for a particular pattern of white matter integrity loss. Leveraging the relative strengths of WM and GM markers, this metric links areas of WM integrity loss to their connected GM regions as a first step in understanding their functional implications. The LoCo score is calculated for three groups: 18AD, 18 FTD, and 19 age-matched normal controls. We show significant correlations of the LoCo with the respective atrophy patterns in AD (R=0.51, p=2.2 × 10(-9)) and FTD (R=0.49, p=2.5 × 10(-8)) for a standard 116 region gray matter atlas. In addition, we demonstrate that the LoCo outperforms a measure of gray matter atrophy when classifying individuals into AD, FTD, and normal groups.

Modeling the Relationship among Gray Matter Atrophy, Abnormalities in Connecting White Matter, and Cognitive Performance in Early Multiple Sclerosis

BACKGROUND AND PURPOSE: Quantitative assessment of clinical and pathologic consequences of white matter abnormalities in multiple sclerosis is critical in understanding the pathways of disease. This study aimed to test whether gray matter atrophy was related to abnormalities in connecting white matter and to identify patterns of imaging biomarker abnormalities that were related to patient processing speed. MATERIALS AND METHODS: Image data and Symbol Digit Modalities Test scores were collected from a cohort of patients with early multiple sclerosis. The Network Modification Tool was used to estimate connectivity irregularities by projecting white matter abnormalities onto connecting gray matter regions. Partial least-squares regression quantified the relationship between imaging biomarkers and processing speed as measured by the Symbol Digit Modalities Test. RESULTS: Atrophy in deep gray matter structures of the thalami and putamen had moderate and significant correlations with abnormalities in connecting white matter (r = 0.39–0.41, P < .05 corrected). The 2 models of processing speed, 1 for each of the WM imaging biomarkers, had goodness-of-fit (R2) values of 0.42 and 0.30. A measure of the impact of white matter lesions on the connectivity of occipital and parietal areas had significant nonzero regression coefficients. CONCLUSIONS: We concluded that deep gray matter regions may be susceptible to inflammation and/or demyelination in white matter, possibly having a higher sensitivity to remote degeneration, and that lesions affecting visual processing pathways were related to processing speed. The Network Modification Tool may be used to quantify the impact of early white matter abnormalities on both connecting gray matter structures and processing speed.

Statistics of Weighted Brain Networks Reveal Hierarchical Organization and Gaussian Degree Distribution

Whole brain weighted connectivity networks were extracted from high resolution diffusion MRI data of 14 healthy volunteers. A statistically robust technique was proposed for the removal of questionable connections. Unlike most previous studies our methods are completely adapted for networks with arbitrary weights. Conventional statistics of these weighted networks were computed and found to be comparable to existing reports. After a robust fitting procedure using multiple parametric distributions it was found that the weighted node degree of our networks is best described by the normal distribution, in contrast to previous reports which have proposed heavy tailed distributions. We show that post-processing of the connectivity weights, such as thresholding, can influence the weighted degree asymptotics. The clustering coefficients were found to be distributed either as gamma or power-law distribution, depending on the formula used. We proposed a new hierarchical graph clustering approach, which revealed that the brain network is divided into a regular base-2 hierarchical tree. Connections within and across this hierarchy were found to be uncommonly ordered. The combined weight of our results supports a hierarchically ordered view of the brain, whose connections have heavy tails, but whose weighted node degrees are comparable.

Exploring the brain's structural connectome: A quantitative stroke lesion-dysfunction mapping study

The aim of this work was to quantitatively model cross‐sectional relationships between structural connectome disruptions caused by cerebral infarction and measures of clinical performance. Imaging biomarkers of 41 ischemic stroke patients (72.0 ± 12.0 years, 20 female) were related to their baseline performance in 18 cognitive, physical and daily life activity assessments. Individual estimates of structural connectivity disruption in gray matter regions were computed using the Change in Connectivity (ChaCo) score. ChaCo scores were utilized because they can be calculated using routinely collected clinical magnetic resonance imagings. Partial Least Squares Regression (PLSR) was used to predict various acute impairment and activity measures from ChaCo scores and patient demographics. Statistical methods of cross‐validation, bootstrapping and multiple comparisons correction were implemented to minimize over‐fitting and Type I errors. Multiple linear regression models based on lesion volume and lateralization information were constructed for comparison. All models based on connectivity disruption had lower Akaike Information Criterion and almost all had better goodness‐of‐fit values (R2: 0.26–0.92) than models based on lesion characteristics (R2: 0.06–0.50). Confidence intervals of PLSR coefficients identified brain regions important in predicting each clinical assessment. Appropriate mapping of eloquent functions, that is, language and motor, and replication of results across pathologies provided validation of this method. Models of complex functions provided new insights into brain‐behavior relationships. In addition to the potential applications in prognostication and rehabilitation development, this quantitative approach provides insight into the structural networks underlying complex functions like activities of daily living and cognition. Quantitative analysis of big data will be invaluable in understanding complex brain‐behavior relationships. Hum Brain Mapp 36:2147–2160, 2015.

Structural connectome disruption at baseline predicts 6-months post-stroke outcome

In this study, models based on quantitative imaging biomarkers of post‐stroke structural connectome disruption were used to predict six‐month outcomes in various domains. Demographic information and clinical MRIs were collected from 40 ischemic stroke subjects (age: 68.1 ± 13.2 years, 17 female, NIHSS: 6.8 ± 5.6). Diffusion‐weighted images were used to create lesion masks, which were uploaded to the Network Modification (NeMo) Tool. The NeMo Tool, using only clinical MRIs, allows estimation of connectome disruption at three levels: whole brain, individual gray matter regions and between pairs of gray matter regions. Partial Least Squares Regression models were constructed for each level of connectome disruption and for each of the three six‐month outcomes: applied cognitive, basic mobility and daily activity. Models based on lesion volume were created for comparison. Cross‐validation, bootstrapping and multiple comparisons corrections were implemented to minimize over‐fitting and Type I errors. The regional disconnection model best predicted applied cognitive (R2 = 0.56) and basic mobility outcomes (R2 = 0.70), while the pairwise disconnection model best predicted the daily activity measure (R2 = 0.72). These results demonstrate that models based on connectome disruption metrics were more accurate than ones based on lesion volume and that increasing anatomical specificity of disconnection metrics does not always increase model accuracy, likely due to statistical adjustments for concomitant increases in data dimensionality. This work establishes that the NeMo Tools measures of baseline connectome disruption, acquired using only routinely collected MRI scans, can predict 6‐month post‐stroke outcomes in various functional domains including cognition, motor function and daily activities. Hum Brain Mapp, 2016.