Godfrey D. Pearlson

A method for making group inferences from functional MRI data using independent component analysis.

Independent component analysis (ICA) is a promising analysis method that is being increasingly applied to fMRI data. A principal advantage of this approach is its applicability to cognitive paradigms for which detailed models of brain activity are not available. Independent component analysis has been successfully utilized to analyze single‐subject fMRI data sets, and an extension of this work would be to provide for group inferences. However, unlike univariate methods (e.g., regression analysis, Kolmogorov–Smirnov statistics), ICA does not naturally generalize to a method suitable for drawing inferences about groups of subjects. We introduce a novel approach for drawing group inferences using ICA of fMRI data, and present its application to a simple visual paradigm that alternately stimulates the left or right visual field. Our group ICA analysis revealed task‐related components in left and right visual cortex, a transiently task‐related component in bilateral occipital/parietal cortex, and a non‐task‐related component in bilateral visual association cortex. We address issues involved in the use of ICA as an fMRI analysis method such as: (1) How many components should be calculated? (2) How are these components to be combined across subjects? (3) How should the final results be thresholded and/or presented? We show that the methodology we present provides answers to these questions and lay out a process for making group inferences from fMRI data using independent component analysis. Hum. Brain Mapping 14:140–151, 2001.

DtiStudio: Resource program for diffusion tensor computation and fiber bundle tracking

A versatile resource program was developed for diffusion tensor image (DTI) computation and fiber tracking. The software can read data formats from a variety of MR scanners. Tensor calculation is performed by solving an over-determined linear equation system using least square fitting. Various types of map data, such as tensor elements, eigenvalues, eigenvectors, diffusion anisotropy, diffusion constants, and color-coded orientations can be calculated. The results are visualized interactively in orthogonal views and in three-dimensional mode. Three-dimensional tract reconstruction is based on the Fiber Assignment by Continuous Tracking (FACT) algorithm and a brute-force reconstruction approach. To improve the time and memory efficiency, a rapid algorithm to perform the FACT is adopted. An index matrix for the fiber data is introduced to facilitate various types of fiber bundles selection based on approaches employing multiple regions of interest (ROIs). The program is developed using C++ and OpenGL on a Windows platform.

A Baseline for the Multivariate Comparison of Resting-State Networks

As the size of functional and structural MRI datasets expands, it becomes increasingly important to establish a baseline from which diagnostic relevance may be determined, a processing strategy that efficiently prepares data for analysis, and a statistical approach that identifies important effects in a manner that is both robust and reproducible. In this paper, we introduce a multivariate analytic approach that optimizes sensitivity and reduces unnecessary testing. We demonstrate the utility of this mega-analytic approach by identifying the effects of age and gender on the resting-state networks (RSNs) of 603 healthy adolescents and adults (mean age: 23.4 years, range: 12–71 years). Data were collected on the same scanner, preprocessed using an automated analysis pipeline based in SPM, and studied using group independent component analysis. RSNs were identified and evaluated in terms of three primary outcome measures: time course spectral power, spatial map intensity, and functional network connectivity. Results revealed robust effects of age on all three outcome measures, largely indicating decreases in network coherence and connectivity with increasing age. Gender effects were of smaller magnitude but suggested stronger intra-network connectivity in females and more inter-network connectivity in males, particularly with regard to sensorimotor networks. These findings, along with the analysis approach and statistical framework described here, provide a useful baseline for future investigations of brain networks in health and disease.

A method for functional network connectivity among spatially independent resting-state components in schizophrenia

Functional connectivity of the brain has been studied by analyzing correlation differences in time courses among seed voxels or regions with other voxels of the brain in healthy individuals as well as in patients with brain disorders. The spatial extent of strongly temporally coherent brain regions co-activated during rest has also been examined using independent component analysis (ICA). However, the weaker temporal relationships among ICA component time courses, which we operationally define as a measure of functional network connectivity (FNC), have not yet been studied. In this study, we propose an approach for evaluating FNC and apply it to functional magnetic resonance imaging (fMRI) data collected from persons with schizophrenia and healthy controls. We examined the connectivity and latency among ICA component time courses to test the hypothesis that patients with schizophrenia would show increased functional connectivity and increased lag among resting state networks compared to controls. Resting state fMRI data were collected and the inter-relationships among seven selected resting state networks (identified using group ICA) were evaluated by correlating each subjects ICA time courses with one another. Patients showed higher correlation than controls among most of the dominant resting state networks. Patients also had slightly more variability in functional connectivity than controls. We present a novel approach for quantifying functional connectivity among brain networks identified with spatial ICA. Significant differences between patient and control connectivity in different networks were revealed possibly reflecting deficiencies in cortical processing in patients.

Spatial and Temporal Independent Component Analysis of Functional MRI Data Containing a Pair of Task-Related Waveforms

Independent component analysis (ICA) is a technique that attempts to separate data into maximally independent groups. Achieving maximal independence in space or time yields two varieties of ICA meaningful for functional MRI (fMRI) applications: spatial ICA (SICA) and temporal ICA (TICA). SICA has so far dominated the application of ICA to fMRI. The objective of these experiments was to study ICA with two predictable components present and evaluate the importance of the underlying independence assumption in the application of ICA. Four novel visual activation paradigms were designed, each consisting of two spatiotemporal components that were either spatially dependent, temporally dependent, both spatially and temporally dependent, or spatially and temporally uncorrelated, respectively. Simulated data were generated and fMRI data from six subjects were acquired using these paradigms. Data from each paradigm were analyzed with regression analysis in order to determine if the signal was occurring as expected. Spatial and temporal ICA were then applied to these data, with the general result that ICA found components only where expected, e.g., S(T)ICA “failed” (i.e., yielded independent components unrelated to the “self‐evident” components) for paradigms that were spatially (temporally) dependent, and “worked” otherwise. Regression analysis proved a useful “check” for these data, however strong hypotheses will not always be available, and a strength of ICA is that it can characterize data without making specific modeling assumptions. We report a careful examination of some of the assumptions behind ICA methodologies, provide examples of when applying ICA would provide difficult‐to‐interpret results, and offer suggestions for applying ICA to fMRI data especially when more than one task‐related component is present in the data.Hum. Brain Mapping 13:43–53, 2001.

Imaging cortical association tracts in the human brain using diffusion‐tensor‐based axonal tracking

Diffusion‐tensor fiber tracking was used to identify the cores of several long‐association fibers, including the anterior (ATR) and posterior (PTR) thalamic radiations, and the uncinate (UNC), superior longitudinal (SLF), inferior longitudinal (ILF), and inferior fronto‐occipital (IFO) fasciculi. Tracking results were compared to existing anatomical knowledge, and showed good qualitative agreement. Guidelines were developed to reproducibly track these fibers in vivo. The interindividual variability of these reconstructions was assessed in a common spatial reference frame (Talairach space) using probabilistic mapping. As a first illustration of this technical capability, a reduction in brain connectivity in a patient with a childhood neurodegenerative disease (X‐linked adrenoleukodystrophy) was demonstrated. Magn Reson Med 47:215–223, 2002.

Diffusion tensor imaging and axonal tracking in the human brainstem.

Diffusion tensor MRI was used to demonstrate in vivo anatomical mapping of brainstem axonal connections. It was possible to identify the corticospinal tract (CST), medial lemniscus, and the superior, medial, and inferior cerebellar peduncles. In addition, the cerebral peduncle could be subparcellated into component tracts, namely, the frontopontine tract, the CST, and the temporo-/parieto-/occipitopontine tract. Anatomical landmarks and tracking thresholds were established for each fiber and, using these standards, reproducibility of automated tracking as assessed by intra- and interrater reliability was found to be high (kappa > 0.82). Reconstructed fibers corresponded well to existing anatomical knowledge, validating the tracking. Information on the location of individual tracts was coregistered with quantitative MRI maps to automatically measure MRI parameters on a tract-by-tract basis. The results reveal that each tract has a unique spatial signature in terms of water relaxation and diffusion anisotropy.

Modulation of Temporally Coherent Brain Networks Estimated Using ICA at Rest and During Cognitive Tasks

Brain regions which exhibit temporally coherent fluctuations, have been increasingly studied using functional magnetic resonance imaging (fMRI). Such networks are often identified in the context of an fMRI scan collected during rest (and thus are called “resting state networks”); however, they are also present during (and modulated by) the performance of a cognitive task. In this article, we will refer to such networks as temporally coherent networks (TCNs). Although there is still some debate over the physiological source of these fluctuations, TCNs are being studied in a variety of ways. Recent studies have examined ways TCNs can be used to identify patterns associated with various brain disorders (e.g. schizophrenia, autism or Alzheimers disease). Independent component analysis (ICA) is one method being used to identify TCNs. ICA is a data driven approach which is especially useful for decomposing activation during complex cognitive tasks where multiple operations occur simultaneously. In this article we review recent TCN studies with emphasis on those that use ICA. We also present new results showing that TCNs are robust, and can be consistently identified at rest and during performance of a cognitive task in healthy individuals and in patients with schizophrenia. In addition, multiple TCNs show temporal and spatial modulation during the cognitive task versus rest. In summary, TCNs show considerable promise as potential imaging biological markers of brain diseases, though each network needs to be studied in more detail. Hum Brain Mapp, 2008.

MRI volumes of amygdala and hippocampus in non-mentally retarded autistic adolescents and adults.

Objective: To determine whether volumes of hippocampus and amygdala are abnormal in people with autism. Background: Neuropathologic studies of the limbic system in autism have found decreased neuronal size, increased neuronal packing density, and decreased complexity of dendritic arbors in hippocampus, amygdala, and other limbic structures. These findings are suggestive of a developmental curtailment in the maturation of the neurons and neuropil. Methods: Measurement of hippocampus, amygdala, and total brain volumes from 1.5-mm coronal, spoiled gradient-recalled echo MRI scans in 14 non–mentally retarded autistic male adolescents and young adults and 14 individually matched, healthy community volunteers. Results: Amygdala volume was significantly smaller in the autistic subjects, both with (p = 0.006) and without (p = 0.01) correcting for total brain volume. Total brain volume and absolute hippocampal volume did not differ significantly between groups, but hippocampal volume, when corrected for total brain volume, was significantly reduced (p = 0.04) in the autistic subjects. Conclusions: There is a reduction in the volume of amygdala and hippocampus in people with autism, particularly in relation to total brain volume. The histopathology of autism suggests that these volume reductions are related to a reduction in dendritic tree and neuropil development, and likely reflect the underdevelopment of the neural connections of limbic structures with other parts of the brain, particularly cerebral cortex.

Medial and superior temporal gyral volumes and cerebral asymmetry in schizophrenia versus bipolar disorder

Prior magnetic resonance imaging (MRI) studies report both medial and lateral cortical temporal changes and disturbed temporal lobe asymmetries in schizophrenic patients compared with healthy controls. The specificity of temporal lobe (TL) changes in schizophrenia is unknown. We determined the occurrence and specificity of these TL changes. Forty-six schizophrenic patients were compared to 60 normal controls and 27 bipolar subjects on MRI measures of bilateral volumes of anterior and posterior superior temporal gyrus (STG), amygdala, entorhinal cortex, and multiple medial temporal structures, as well as global brain measures. Several regional comparisons distinguished schizophrenia from bipolar disorder. Entorhinal cortex, not previously assessed using MRI in schizophrenia, was bilaterally smaller than normal in schizophrenia but not in bipolar disorder. Schizophrenic but not bipolar patients had an alteration of normal posterior STG asymmetry. Additionally, left anterior STG and right amygdala were smaller than predicted in schizophrenia but not bipolar disorder. Left amygdala was smaller and right anterior STG larger in bipolar disorder but not schizophrenia.