Diffusion map for clustering fMRI spatial maps extracted by independent component analysis

Functional magnetic resonance imaging (fMRI) produces data about activity inside the brain, from which spatial maps can be extracted by independent component analysis (ICA). In datasets, there are n spatial maps that contain p voxels. The number of voxels is very high compared to the number of analyzed spatial maps. Clustering of the spatial maps is usually based on correlation matrices. This usually works well, although such a similarity matrix inherently can explain only a certain amount of the total variance contained in the high-dimensional data where n is relatively small but p is large. For high-dimensional space, it is reasonable to perform dimensionality reduction before clustering. In this research, we used the recently developed diffusion map for dimensionality reduction in conjunction with spectral clustering. This research revealed that the diffusion map based clustering worked as well as the more traditional methods, and produced more compact clusters when needed.Comment: 6 pages. 8 figures. Copyright (c) 2013 IEEE. Published at 2013 IEEE International Workshop on Machine Learning for Signal Processin

Construction of embedded fMRI resting state functional connectivity networks using manifold learning

We construct embedded functional connectivity networks (FCN) from benchmark resting-state functional magnetic resonance imaging (rsfMRI) data acquired from patients with schizophrenia and healthy controls based on linear and nonlinear manifold learning algorithms, namely, Multidimensional Scaling (MDS), Isometric Feature Mapping (ISOMAP) and Diffusion Maps. Furthermore, based on key global graph-theoretical properties of the embedded FCN, we compare their classification potential using machine learning techniques. We also assess the performance of two metrics that are widely used for the construction of FCN from fMRI, namely the Euclidean distance and the lagged cross-correlation metric. We show that the FCN constructed with Diffusion Maps and the lagged cross-correlation metric outperform the other combinations

Analysis of structural and functional brain networks

The brain is a representative example of a network. It consists of numerous spatially distributed regions that continuously exchange information through structural connections. In the past decade, an increasing number of studies have explored the brain network in both structural and functional aspects; they have begun to decipher complex brain wirings, as well as elucidate how the rich functionality emerges from this architecture. Based upon previous studies, this thesis addresses three critical gaps in the field. (I) Although it is known that the community structures of brain network are spatially overlapping, conventional studies have focused on grouping brain regions into communities such that each region belongs to only one community. Therefore, a recent “link community” concept was employed to disentangle those overlapping architectures. (II) Spatial independent component analysis (sICA) and k-means clustering are two representative data-driven algorithms used to analyze functional networks. However, it is still unclear how these two methods compare to each other in terms of their theoretical basis and biological relevance. Hence, the relationship between these two methods were investigated. (III) Despite the multi-scale functional organization of the brain, previous studies have primarily examined the large-scale networks of the entire brain. Complex neural activity patterns in relatively smaller spatial scales have been poorly understood. Therefore, the fine-scale spatiotemporal patterns within visual cortex were explored. The distinguishing results obtained in this study may provide new insights regarding the brain\u27s organization, as well as a better understanding of mathematical and statistical tools for functional and structural network analysis

A group model for stable multi-subject ICA on fMRI datasets

Spatial Independent Component Analysis (ICA) is an increasingly used data-driven method to analyze functional Magnetic Resonance Imaging (fMRI) data. To date, it has been used to extract sets of mutually correlated brain regions without prior information on the time course of these regions. Some of these sets of regions, interpreted as functional networks, have recently been used to provide markers of brain diseases and open the road to paradigm-free population comparisons. Such group studies raise the question of modeling subject variability within ICA: how can the patterns representative of a group be modeled and estimated via ICA for reliable inter-group comparisons? In this paper, we propose a hierarchical model for patterns in multi-subject fMRI datasets, akin to mixed-effect group models used in linear-model-based analysis. We introduce an estimation procedure, CanICA (Canonical ICA), based on i) probabilistic dimension reduction of the individual data, ii) canonical correlation analysis to identify a data subspace common to the group iii) ICA-based pattern extraction. In addition, we introduce a procedure based on cross-validation to quantify the stability of ICA patterns at the level of the group. We compare our method with state-of-the-art multi-subject fMRI ICA methods and show that the features extracted using our procedure are more reproducible at the group level on two datasets of 12 healthy controls: a resting-state and a functional localizer study

Enhanced pre-frontal functional-structural networks to support postural control deficits after traumatic brain injury in a pediatric population

Traumatic brain injury (TBI) affects the structural connectivity, triggering the re-organization of structural-functional circuits in a manner that remains poorly understood. We focus here on brain networks re-organization in relation to postural control deficits after TBI. We enrolled young participants who had suffered moderate to severeTBI, comparing them to young typically developing control participants. In comparison to control participants, TBI patients (but not controls) recruited prefrontal regions to interact with two separated networks: 1) a subcortical network including part of the motor network, basal ganglia, cerebellum, hippocampus, amygdala, posterior cingulum and precuneus; and 2) a task-positive network, involving regions of the dorsal attention system together with the dorsolateral and ventrolateral prefrontal regions

Application of resting-state fMRI methods to acute ischemic stroke

Diffusion weighted imaging (DWI) and dynamic susceptibility contrast-enhanced (DSC) perfusion-weighted imaging (PWI) are commonly employed in clinical practice and in research to give pathophysiological information for patients with acute ischemic stroke. DWI is thought to roughly reflect the severely damaged infarct core, while DSC-PWI reflects the area of hypoperfusion. The volumetric difference between DWI and DSC-PWI is termed the PWI/DWI-mismatch, and has been suggested as an MRI surrogate of the ischemic penumbra. However, due to the application of a contrast agent, which has potentially severe side-effects (e.g., nephrogenic systemic fibrosis), the DSC-PWI precludes repetitive examinations for monitoring purposes. New approaches are being sought to overcome this shortcoming. BOLD (blood oxygen-level dependent) signal can reflect the metabolism of blood oxygen in the brain and hemodynamics can be assessed with resting-state fMRI. The aim of this thesis was to use resting-state fMRI as a new approach to give similar information as DSC-PWI. This thesis comprises two studies: In the first study (see Chapter 2), two resting-state fMRI methods, local methods which compare low frequency amplitudes between two hemispheres and a k-means clustering approach, were applied to investigate the functional damage of patients with acute ischemic stroke both in the time domain and frequency domain. We found that the lesion areas had lower amplitudes than contralateral homotopic healthy tissues. We also differentiated the lesion areas from healthy tissues using a k-means clustering approach. In the second study (see Chapter 3), time-shift analysis (TSA), which assesses time delays of the spontaneous low frequency fluctuations of the resting-state BOLD signal, was applied to give similar pathophysiological information as DSC-PWI in the acute phase of stroke. We found that areas which showed a pronounced time delay to the respective mean time course were very similar to the hypoperfusion area. In summary, we suggest that the resting-state fMRI methods, especially the time-shift analysis (TSA), may provide comparable information to DSC-PWI and thus serve as a useful diagnostic tool for stroke MRI without the need for the application of a contrast agent

Corticostriatal connectivity fingerprints:Probability maps based on resting-state functional connectivity

Over the last decade, structure-function relationships have begun to encompass networks of brain areas rather than individual structures. For example, corticostriatal circuits have been associated with sensorimotor, limbic, and cognitive information processing, and damage to these circuits has been shown to produce unique behavioral outcomes in Autism, Parkinson's Disease, Schizophrenia and healthy ageing. However, it remains an open question how abnormal or absent connectivity can be detected at the individual level. Here, we provide a method for clustering gross morphological structures into subregions with unique functional connectivity fingerprints, and generate network probability maps usable as a baseline to compare individual cases against. We used connectivity metrics derived from resting-state fMRI (N = 100), in conjunction with hierarchical clustering methods, to parcellate the striatum into functionally distinct clusters. We identified three highly reproducible striatal subregions, across both hemispheres and in an independent replication dataset (N = 100) (dice-similarity values 0.40-1.00). Each striatal seed region resulted in a highly reproducible distinct connectivity fingerprint: the putamen showed predominant connectivity with cortical and cerebellar sensorimotor and language processing areas; the ventromedial striatum cluster had a distinct limbic connectivity pattern; the caudate showed predominant connectivity with the thalamus, frontal and occipital areas, and the cerebellum. Our corticostriatal probability maps agree with existing connectivity data in humans and non-human primates, and showed a high degree of replication. We believe that these maps offer an efficient tool to further advance hypothesis driven research and provide important guidance when investigating deviant connectivity in neurological patient populations suffering from e.g., stroke or cerebral palsy. Hum Brain Mapp 38:1478-1491, 2017. © 2016 Wiley Periodicals, Inc.status: publishe