Groupwise Multimodal Image Registration using Joint Total Variation

In medical imaging it is common practice to acquire a wide range of modalities (MRI, CT, PET, etc.), to highlight different structures or pathologies. As patient movement between scans or scanning session is unavoidable, registration is often an essential step before any subsequent image analysis. In this paper, we introduce a cost function based on joint total variation for such multimodal image registration. This cost function has the advantage of enabling principled, groupwise alignment of multiple images, whilst being insensitive to strong intensity non-uniformities. We evaluate our algorithm on rigidly aligning both simulated and real 3D brain scans. This validation shows robustness to strong intensity non-uniformities and low registration errors for CT/PET to MRI alignment. Our implementation is publicly available at https://github.com/brudfors/coregistration-njtv

Function-based Intersubject Alignment of Human Cortical Anatomy

Making conclusions about the functional neuroanatomical organization of the human brain requires methods for relating the functional anatomy of an individual's brain to population variability. We have developed a method for aligning the functional neuroanatomy of individual brains based on the patterns of neural activity that are elicited by viewing a movie. Instead of basing alignment on functionally defined areas, whose location is defined as the center of mass or the local maximum response, the alignment is based on patterns of response as they are distributed spatially both within and across cortical areas. The method is implemented in the two-dimensional manifold of an inflated, spherical cortical surface. The method, although developed using movie data, generalizes successfully to data obtained with another cognitive activation paradigm—viewing static images of objects and faces—and improves group statistics in that experiment as measured by a standard general linear model (GLM) analysis

Morphogenetic Principles of Brain Organisation in Health and Disease

Non-invasive neuroimaging methods, such as MRI, provide a window into the structure of the mammalian brain. However, despite the ubiquity of these methods, the biological interpretation of the information obtained using these tools remains elusive. In order to accurately link this macroscale data to microscale measurements, it is critical that the construct validity is high. This thesis provides novel analyses, pipelines and methods to: i) generate and validate maps of brain organisation obtained via MRI, and ii) demonstrate the utility of these methods in capturing elements of cognition and psychopathology. First, in Chapter 1, I review some of the neuroscientific context for the new methods presented, from cytoarchitecture to gene expression to connectomes. Chapters 2-4 introduce a new method, “Morphometric Similarity Mapping”, which captures the brain organisation of an individual by mapping the relationships of multiple features of the cerebral cortex. Chapter 2 focuses on the development of the analysis pipeline and the graph theoretical features of the resulting morphometric similarity networks (MSNs), with an emphasis on reproducibility. Chapter 3 highlights the generalisability of MSNs to the macaque monkey, linking MSNs to ex vivo tract tracing experiments and presenting new tools for processing non-human imaging data; as well as evidence that MSN topography is organised by cytoarchitectonic features. Chapter 4 is focused on determining the transcriptomic correlates of MSNs using publicly available gene expression maps, and on applying MSNs to examine the relationship between brain organisation and intelligence. Chapter 5 is dedicated to rigorous evaluation of the applicability of MSNs to measure specific disease-relevant phenotypes in 8 rare genetic disorder cohorts. This includes the validation of novel methods for utilising data from both single-cell sequencing technologies and differential gene expression experiments (in multiple tissue types) in analysing neuroimaging and bulk transcriptomic brain maps. Chapter 6 provides a brief summary and presents some ongoing and future projects expanding on this original work. It also importantly discusses a general framework of comparing brain maps, including MSNs and gene expression, as well as other canonical maps of brain structure and function. Altogether, this thesis presents and evaluates novel methods and applications for integrating multimodal neuroimaging data with genetic data derived from multiple tissue types and through various acquisition strategies. It also includes tools for performing these analyses in non-human primates, and pipelines for statistically comparing brain maps. These results not only provide insight into the manifestation of brain-related changes due to various components of human variation, but also provides a framework for evaluating this variation at multiple biological scales purely from non-invasive neuroimaging data

Quantitative volumetric study of brain in chronic striatolenticular stroke

Perforating branches of the middle cerebral artery, namely the striato-lenticular arteries provide the majority of blood supply for the striatum and posterior limb of the internal capsules. Occlusions of these arteries cause a small stroke but have a devastating effect on patients’ functions. Previous studies showed that the anterior two thirds of the internal capsule is occupied by the prefrontal tracts with the posterior one third by connection to/from sensorimotor, temporal and posterior parietal cortices. In this study, we aimed to examine the long-term effect of infarction in the striato-capsular region on cerebral cortex thickness and also its association with stroke volume and different functional tests. We hypothesized that because of extensive connections of striatum and internal capsule with the cerebral cortex, infarction of this area results in an extensive cortical thickness degeneration which could in turn cause low fictional measurement scores. High resolution T1 weighted MRI was obtained from 21 patients with ischemic stroke in the striatum/posterior limb of the internal capsule region. Subjects were carefully selected from a pool of 140 stroke cases recruited for the Northstar Stroke Project. 63 healthy volunteers (30 male), matched for age and gender were also chosen to form the control group from the OASIS database. Patients and normal subjects were right handed except for 3 patients who have the stroke in the left side of the brain. Patients were defined as left-sided stroke and right-sided stroke depending on the side of the stroke in brain. MRI scans were done 6 months to 2 years after the stroke. To measure cortical thickness, we used Freesurfer software. Vertexwise group comparison was carried out using General Linear Models (GLM). With the Significance level set at 0.05. Population maps of stroke lesions showed that the majority of strokes were located in the striatum and posterior internal capsule. Cortical thickness reduction was greater in the ipsilateral hemisphere. Vertex-wise group comparison between leftsided stroke patients and controls group showed significant reduction in the cortical II thickness in the dorsal and medial prefrontal, premotor, posterior parietal, precuneus, and temporal cortex which survived after correction for multiple comparison using false discovery rate at Freesurfer. Similar comparison for rightsided stroke showed a similar pattern of cortical thinning, however the extent of cortical thinning was much less than in that of the left-sided stroke patients but the ROI analysis showed the main effect of side was significant (f (1, 19) =6.909, p=0.017), which showed that the left hemisphere stroke side group had a thicker cortex (mean=2.463, sd= 0.020) on average compare to the right hemisphere stroke side (mean=2.372, sd= 0.028). Primary motor cortex was surprisingly spared in both stroke groups. In addition, volume of the corpus callosum increased significantly in the stroke group. The differences between motor cortex (M1) thickness in left-hemispheric stroke patients versus controls (t=1.24, n=14, p>0.05) and right-hemispheric stroke patients versus controls (t=-0.511, n=7, p>0.05) were not significant. There was a negative correlation between the volume of the stroke lesions and the affected M1 thickness. There was no correlation between the stroke volume and functional tests in patients and also no correlation between the motor cortex thickness and functional tests in patients. Regarding normal subjects, comparison between two sides of the brain showed that the both hemispheres are symmetrical. In addition, correlation between age and cortical thickness showed a negative significant correlation (1-tailed, p<0.0007, manual correction for multiple comparisons) in M1, superior frontal, lingual cortex at both side of the brain and also negative significant correlation in superior temporal cortex and isthmus cingulated cortex on the left side of brain and supramarginal cortex on the right side of brain but there was no significant difference in cortical thickness between males and females. The finding from this study suggests that the size of the lesion can be a predictor of further M1 cortex reduction. The correlation of M1 thickness with stroke volume showed that secondary cortical degeneration may be mainly depends on the size of neuronal loss in strital-capsular stroke. From normal subject study it can be concluded that generally cortical thickness will decrease with ageing but gender does not have an effect on the cortical thickness. III Furthermore, the lack of behavioural correlation with M1 thickness and stroke volume and also the non significant M1 cortex reduction versus control group may suggest that the long-term functional disability after capsular-striatal stroke may not be entirely dependent on primary motor cortex and secondary motor cortex and primary somatosensory cortex could have an important role as well. These results may help to understand why relatively small subcortical infarcts often cause severe disability that is relatively resistant to recovery in the long term

A blind deconvolution approach to recover effective connectivity brain networks from resting state fMRI data

A great improvement to the insight on brain function that we can get from fMRI data can come from effective connectivity analysis, in which the flow of information between even remote brain regions is inferred by the parameters of a predictive dynamical model. As opposed to biologically inspired models, some techniques as Granger causality (GC) are purely data-driven and rely on statistical prediction and temporal precedence. While powerful and widely applicable, this approach could suffer from two main limitations when applied to BOLD fMRI data: confounding effect of hemodynamic response function (HRF) and conditioning to a large number of variables in presence of short time series. For task-related fMRI, neural population dynamics can be captured by modeling signal dynamics with explicit exogenous inputs; for resting-state fMRI on the other hand, the absence of explicit inputs makes this task more difficult, unless relying on some specific prior physiological hypothesis. In order to overcome these issues and to allow a more general approach, here we present a simple and novel blind-deconvolution technique for BOLD-fMRI signal. Coming to the second limitation, a fully multivariate conditioning with short and noisy data leads to computational problems due to overfitting. Furthermore, conceptual issues arise in presence of redundancy. We thus apply partial conditioning to a limited subset of variables in the framework of information theory, as recently proposed. Mixing these two improvements we compare the differences between BOLD and deconvolved BOLD level effective networks and draw some conclusions

Modeling Semantic Encoding in a Common Neural Representational Space

Encoding models for mapping voxelwise semantic tuning are typically estimated separately for each individual, limiting their generalizability. In the current report, we develop a method for estimating semantic encoding models that generalize across individuals. Functional MRI was used to measure brain responses while participants freely viewed a naturalistic audiovisual movie. Word embeddings capturing agent-, action-, object-, and scene-related semantic content were assigned to each imaging volume based on an annotation of the film. We constructed both conventional within-subject semantic encoding models and between-subject models where the model was trained on a subset of participants and validated on a left-out participant. Between-subject models were trained using cortical surface-based anatomical normalization or surface-based whole-cortex hyperalignment. We used hyperalignment to project group data into an individual’s unique anatomical space via a common representational space, thus leveraging a larger volume of data for out-of-sample prediction while preserving the individual’s fine-grained functional–anatomical idiosyncrasies. Our findings demonstrate that anatomical normalization degrades the spatial specificity of between-subject encoding models relative to within-subject models. Hyperalignment, on the other hand, recovers the spatial specificity of semantic tuning lost during anatomical normalization, and yields model performance exceeding that of within-subject models