Diffusion spectrum imaging reveals detailed local diffusion properties at the expense of substantially long imaging times. It is possible to accelerate acquisition by undersampling in q-space, followed by image reconstruction that exploits prior knowledge on the diffusion probability density functions (pdfs). Previously proposed methods impose this prior in the form of sparsity under wavelet and total variation transforms, or under adaptive dictionaries that are trained on example datasets to maximize the sparsity of the representation. These compressed sensing (CS) methods require full-brain processing times on the order of hours using MATLAB running on a workstation. This work presents two dictionary-based reconstruction techniques that use analytical solutions, and are two orders of magnitude faster than the previously proposed dictionary-based CS approach. The first method generates a dictionary from the training data using principal component analysis (PCA), and performs the reconstruction in the PCA space. The second proposed method applies reconstruction using pseudoinverse with Tikhonov regularization with respect to a dictionary. This dictionary can either be obtained using the K-SVD algorithm, or it can simply be the training dataset of pdfs without any training. All of the proposed methods achieve reconstruction times on the order of seconds per imaging slice, and have reconstruction quality comparable to that of dictionary-based CS algorithm.
The lack of consistency of genetic associations in highly heritable mental illnesses, such as schizophrenia, remains a challenge in molecular psychiatry. Because clinical phenotypes for psychiatric disorders are often ill defined, considerable effort has been made to relate genetic polymorphisms to underlying physiological aspects of schizophrenia (so called intermediate phenotypes), that may be more reliable. Given the polygenic etiology of schizophrenia, the aim of this work was to form a measure of cumulative genetic risk and study its effect on neural activity during working memory (WM) using functional magnetic resonance imaging. Neural activity during the Sternberg Item Recognition Paradigm was measured in 79 schizophrenia patients and 99 healthy controls. Participants were genotyped, and a genetic risk score (GRS), which combined the additive effects of 41 single-nucleotide polymorphisms (SNPs) from 34 risk genes for schizophrenia, was calculated. These risk SNPs were chosen according to the continuously updated meta-analysis of genetic studies on schizophrenia available at www.schizophreniaresearchforum.org. We found a positive relationship between GRS and left dorsolateral prefrontal cortex inefficiency during WM processing. GRS was not correlated with age, performance, intelligence, or medication effects and did not differ between acquisition sites, gender, or diagnostic groups. Our study suggests that cumulative genetic risk, combining the impact of many genes with small effects, is associated with a known brain-based intermediate phenotype for schizophrenia. The GRS approach could provide an advantage over studying single genes in studies focusing on the genetic basis of polygenic conditions such as neuropsychiatric disorders.
Developmental dyslexia, an unexplained difficulty in learning to read, has been associated with alterations in white matter organization as measured by diffusion-weighted imaging. It is unknown, however, whether these differences in structural connectivity are related to the cause of dyslexia or if they are consequences of reading difficulty (e.g., less reading experience or compensatory brain organization). Here, in 40 kindergartners who had received little or no reading instruction, we examined the relation between behavioral predictors of dyslexia and white matter organization in left arcuate fasciculus, inferior longitudinal fasciculus, and the parietal portion of the superior longitudinal fasciculus using probabilistic tractography. Higher composite phonological awareness scores were significantly and positively correlated with the volume of the arcuate fasciculus, but not with other tracts. Two other behavioral predictors of dyslexia, rapid naming and letter knowledge, did not correlate with volumes or diffusion values in these tracts. The volume and fractional anisotropy of the left arcuate showed a particularly strong positive correlation with a phoneme blending test. Whole-brain regressions of behavioral scores with diffusion measures confirmed the unique relation between phonological awareness and the left arcuate. These findings indicate that the left arcuate fasciculus, which connects anterior and posterior language regions of the human brain and which has been previously associated with reading ability in older individuals, is already smaller and has less integrity in kindergartners who are at risk for dyslexia because of poor phonological awareness. These findings suggest a structural basis of behavioral risk for dyslexia that predates reading instruction.
The neural mechanisms underlying genetic risk for schizophrenia, a highly heritable psychiatric condition, are still under investigation. New schizophrenia risk genes discovered through genome-wide association studies (GWAS), such as neurogranin (NRGN), can be used to identify these mechanisms. In this study we examined the association of two common NRGN risk single nucleotide polymorphisms (SNPs) with functional and structural brain-based intermediate phenotypes for schizophrenia. We obtained structural, functional MRI and genotype data of 92 schizophrenia patients and 114 healthy volunteers from the multisite Mind Clinical Imaging Consortium study. Two schizophrenia-associated NRGN SNPs (rs12807809 and rs12541) were tested for association with working memory-elicited dorsolateral prefrontal cortex (DLPFC) activity and surface-wide cortical thickness. NRGN rs12541 risk allele homozygotes (TT) displayed increased working memory-related activity in several brain regions, including the left DLPFC, left insula, left somatosensory cortex and the cingulate cortex, when compared to non-risk allele carriers. NRGN rs12807809 non-risk allele (C) carriers showed reduced cortical gray matter thickness compared to risk allele homozygotes (TT) in an area comprising the right pericalcarine gyrus, the right cuneus, and the right lingual gyrus. Our study highlights the effects of schizophrenia risk variants in the NRGN gene on functional and structural brain-based intermediate phenotypes for schizophrenia. These results support recent GWAS findings and further implicate NRGN in the pathophysiology of schizophrenia by suggesting that genetic NRGN risk variants contribute to subtle changes in neural functioning and anatomy that can be quantified with neuroimaging methods.
Previous studies have found varying relationships between cognitive functioning and brain volumes in patients with schizophrenia. However, cortical thickness may more closely reflect cytoarchitectural characteristics than gray matter density or volume estimates. Here, we aimed to compare associations between regional variation in cortical thickness and executive functions, memory, as well as verbal and spatial processing in patients with schizophrenia and healthy controls (HCs). We obtained magnetic resonance imaging and neuropsychological data for 131 patients and 138 matched controls. Automated cortical pattern matching methods allowed testing for associations with cortical thickness estimated as the shortest distance between the gray/white matter border and the pial surface at thousands of points across the entire cortical surface. Two independent measures of working memory showed robust associations with cortical thickness in lateral prefrontal cortex in HCs, whereas patients exhibited associations between working memory and cortical thickness in the right middle and superior temporal lobe. This study provides additional evidence for a disrupted structure-function relationship in schizophrenia. In line with the prefrontal inefficiency hypothesis, schizophrenia patients may engage a larger compensatory network of brain regions other than frontal cortex to recall and manipulate verbal material in working memory.
In diffusion MRI, simultaneous multi-slice single-shot EPI acquisitions have the potential to increase the number of diffusion directions obtained per unit time, allowing more diffusion encoding in high angular resolution diffusion imaging (HARDI) acquisitions. Nonetheless, unaliasing simultaneously acquired, closely spaced slices with parallel imaging methods can be difficult, leading to high g-factor penalties (i.e., lower SNR). The CAIPIRINHA technique was developed to reduce the g-factor in simultaneous multi-slice acquisitions by introducing inter-slice image shifts and thus increase the distance between aliased voxels. Because the CAIPIRINHA technique achieved this by controlling the phase of the RF excitations for each line of k-space, it is not directly applicable to single-shot EPI employed in conventional diffusion imaging. We adopt a recent gradient encoding method, which we termed "blipped-CAIPI", to create the image shifts needed to apply CAIPIRINHA to EPI. Here, we use pseudo-multiple replica SNR and bootstrapping metrics to assess the performance of the blipped-CAIPI method in 3× simultaneous multi-slice diffusion studies. Further, we introduce a novel image reconstruction method to reduce detrimental ghosting artifacts in these acquisitions. We show that data acquisition times for Q-ball and diffusion spectrum imaging (DSI) can be reduced 3-fold with a minor loss in SNR and with similar diffusion results compared to conventional acquisitions.
BACKGROUND: Previous studies have suggested that motivational aspects of executive functioning, which may be disrupted in schizophrenia patients with negative symptoms, are mediated in part by the striatum. Negative symptoms have been linked to impaired recruitment of both the striatum and the dorsolateral prefrontal cortex (DLPFC). Here we tested the hypothesis that negative symptoms are associated primarily with striatal dysfunction, using functional magnetic resonance imaging (fMRI).
METHOD: Working-memory load-dependent activation and gray matter volumes of the striatum and DLPFC were measured using a region-of-interest (ROI) approach, in 147 schizophrenia patients and 160 healthy controls. In addition to testing for a linear relationships between striatal function and negative symptoms, we chose a second, categorical analytic strategy in which we compared three demographically and behaviorally matched subgroups: patients with a high burden of negative symptoms, patients with minimal negative symptoms, and healthy subjects.
RESULTS: There were no differences in striatal response magnitudes between schizophrenia patients and healthy controls, but right DLPFC activity was higher in patients than in controls. Negative symptoms were inversely associated with striatal, but not DLPFC, activity. In addition, patients with a high burden of negative symptoms exhibited significantly lower bilateral striatal, but not DLPFC, activation than schizophrenia patients with minimal negative symptoms. Working memory performance, antipsychotic exposure and changes in gray matter volumes did not account for these differences.
CONCLUSIONS: These data provide further evidence for a robust association between negative symptoms and diminished striatal activity. Future work will determine whether low striatal activity in schizophrenia patients could serve as a reliable biomarker for negative symptoms.
Diffusion spectrum imaging offers detailed information on complex distributions of intravoxel fiber orientations at the expense of extremely long imaging times (∼1 h). Recent work by Menzel et al. demonstrated successful recovery of diffusion probability density functions from sub-Nyquist sampled q-space by imposing sparsity constraints on the probability density functions under wavelet and total variation transforms. As the performance of compressed sensing reconstruction depends strongly on the level of sparsity in the selected transform space, a dictionary specifically tailored for diffusion probability density functions can yield higher fidelity results. To our knowledge, this work is the first application of adaptive dictionaries in diffusion spectrum imaging, whereby we reduce the scan time of whole brain diffusion spectrum imaging acquisition from 50 to 17 min while retaining high image quality. In vivo experiments were conducted with the 3T Connectome MRI. The root-mean-square error of the reconstructed "missing" diffusion images were calculated by comparing them to a gold standard dataset (obtained from acquiring 10 averages of diffusion images in these missing directions). The root-mean-square error from the proposed reconstruction method is up to two times lower than that of Menzel et al.'s method and is actually comparable to that of the fully-sampled 50 minute scan. Comparison of tractography solutions in 18 major white-matter pathways also indicated good agreement between the fully-sampled and 3-fold accelerated reconstructions. Further, we demonstrate that a dictionary trained using probability density functions from a single slice of a particular subject generalizes well to other slices from the same subject, as well as to slices from other subjects.
BACKGROUND: Disrupted in schizophrenia 1 (DISC1) is known to play a major role during brain development and is a candidate gene for schizophrenia. Cortical thickness is highly heritable and several MRI studies have shown widespread reductions of cortical thickness in patients with schizophrenia. Here, we investigated the effects of variation in DISC1 on cortical thickness. In a subsequent analysis we tested whether the identified DISC1 risk variant is also associated with neural activity during working memory functioning.
METHODS: We acquired structural MRI (sMRI), functional MRI (fMRI) and genotype data from 96 healthy volunteers. Separate cortical statistical maps for five single nucleotide polymorphisms (SNP) of DISC1 were generated to detect differences of cortical thickness in genotype groups across the entire cortical surface. Working-memory related load-dependent activation was measured during the Sternberg Item Recognition Paradigm and analyzed using a region-of-interest approach.
RESULTS: Phe allele carriers of the DISC1 SNP Leu607Phe had significantly reduced cortical thickness in the left supramarginal gyrus compared to Leu/Leu homozygotes. Neural activity in the left dorsolateral prefrontal cortex (DLPFC) during working memory task was increased in Phe allele carriers, whereas working memory performance did not differ between genotype groups.
CONCLUSIONS: This study provides convergent evidence for the effect of DISC1 risk variants on two independent brain-based intermediate phenotypes of schizophrenia. The same risk variant was associated with cortical thickness reductions and signs of neural inefficiency during a working memory task. Our findings provide further evidence for a neurodevelopmental model of schizophrenia.
We have developed a method for automated probabilistic reconstruction of a set of major white-matter pathways from diffusion-weighted MR images. Our method is called TRACULA (TRActs Constrained by UnderLying Anatomy) and utilizes prior information on the anatomy of the pathways from a set of training subjects. By incorporating this prior knowledge in the reconstruction procedure, our method obviates the need for manual interaction with the tract solutions at a later stage and thus facilitates the application of tractography to large studies. In this paper we illustrate the application of the method on data from a schizophrenia study and investigate whether the inclusion of both patients and healthy subjects in the training set affects our ability to reconstruct the pathways reliably. We show that, since our method does not constrain the exact spatial location or shape of the pathways but only their trajectory relative to the surrounding anatomical structures, a set a of healthy training subjects can be used to reconstruct the pathways accurately in patients as well as in controls.
Neuroimaging studies are facilitated significantly when it is possible to recruit subjects and acquire data at multiple sites. However, the use of different scanners and acquisition protocols is a potential source of variability in multi-site data. In this work we present a multi-site study of the reliability of fMRI activation indices, where 10 healthy volunteers were scanned at 4 different sites while performing a working memory paradigm. Our results indicate that, even with different scanner manufacturers and field strengths, activation variability due to site differences is small compared to variability due to subject differences in this cognitive task, provided we choose an appropriate activation measure.
Abnormalities of the medial temporal lobe have been consistently demonstrated in schizophrenia. A common functional polymorphism, Val108/158Met, in the putative schizophrenia susceptibility gene, catechol-O-methyltransferase (COMT), has been shown to influence medial temporal lobe function. However, the effects of this polymorphism on volumes of medial temporal lobe structures, particularly in patients with schizophrenia, are less clear. Here we measured the effects of COMT Val108/158Met genotype on the volume of two regions within the medial temporal lobe, the amygdala and hippocampus, in patients with schizophrenia and healthy control subjects. We obtained MRI and genotype data for 98 schizophrenic patients and 114 matched controls. An automated atlas-based segmentation algorithm was used to generate volumetric measures of the amygdala and hippocampus. Regression analyses included COMT met allele load as an additive effect, and also controlled for age, intracranial volume, gender and acquisition site. Across patients and controls, each copy of the COMT met allele was associated on average with a 2.6% increase in right amygdala volume, a 3.8% increase in left amygdala volume and a 2.2% increase in right hippocampus volume. There were no effects of COMT genotype on volumes of the whole brain and prefrontal regions. Thus, the COMT Val108/158Met polymorphism was shown to influence medial temporal lobe volumes in a linear-additive manner, mirroring its effect on dopamine catabolism. Taken together with previous work, our data support a model in which lower COMT activity, and a resulting elevation in extracellular dopamine levels, stimulates growth of medial temporal lobe structures.
The dorsolateral prefrontal cortex (DLPFC) is a brain region that has figured prominently in studies of schizophrenia and working memory, yet the exact neuroanatomical localization of this brain region remains to be defined. DLPFC primarily involves the superior frontal gyrus and middle frontal gyrus (MFG). The latter, however is not a single neuroanatomical entity but instead is comprised of rostral (anterior, middle, and posterior) and caudal regions. In this study we used structural MRI to develop a method for parcellating MFG into its component parts. We focused on this region of DLPFC because it includes BA46, a region involved in working memory. We evaluated volume differences in MFG in 20 patients with chronic schizophrenia and 20 healthy controls. Mid-rostral MFG (MR-MFG) was delineated within the rostral MFG using anterior and posterior neuroanatomical landmarks derived from cytoarchitectonic definitions of BA46. Gray matter volumes of MR-MFG were then compared between groups, and a significant reduction in gray matter volume was observed (p<0.008), but not in other areas of MFG (i.e., anterior or posterior rostral MFG, or caudal regions of MFG). Our results demonstrate that volumetric alterations in MFG gray matter are localized exclusively to MR-MFG. 3D reconstructions of the cortical surface made it possible to follow MFG into its anterior part, where other approaches have failed. This method of parcellation offers a more precise way of measuring MR-MFG that will likely be important in further documentation of DLPFC anomalies in schizophrenia.
We experimentally investigated the SPECT recovery of I-131 activity in multiple spheres located simultaneously at different locations within a cylindrical phantom that had an elliptical cross section. The sphere volumes ranged from 209 cc down to 4.2 cc. A Prism 3000 camera and two types of parallel-hexagonal-hole collimation were employed: high energy (HE) and ultra high energy (UHE). Using appropriately-different 3D models of the point source response function for the two types of collimation, approximately the same recovery of activity could be achieved with either collimation by 3D OSEM reconstruction. The recovery coefficient was greater with no background activity in the phantom by 0.10, on average, compared to that with background. In the HE collimation case, the activity recovery was considerably better for all volumes using 3D OSEM reconstruction than it had been in the past using 1D SAGE reconstruction. Recovery-coefficient-based correction in a simulated patient case involving spherical tumours moderately improved the activity estimates (average error reduced from 14% to 9% for UHE collimation, and from 15% to 11% for HE collimation). For a test case with HE collimation, increasing the projection-image sampling density while decreasing the image voxel size increased the recovery coefficient by 0.075 on average, and, if used in a full set of calibration measurements of recovery coefficient versus volume, might lead to further improvement in accuracy for the patient case.
Our goal is to optimize regularized image reconstruction for emission tomography with respect to lesion detectability in the reconstructed images. We consider model observers whose decision variable is the maximum value of a local test statistic within a search area. Previous approaches have used simulations to evaluate the performance of such observers. We propose an alternative approach, where approximations of tail probabilities for the maximum of correlated Gaussian random fields facilitate analytical evaluation of detection performance. We illustrate how these approximations, which are reasonably accurate at low probability of false alarm operating points, can be used to optimize regularization with respect to lesion detectability.
We consider the task of detecting a statistically varying signal of known location on a statistically varying background in a reconstructed tomographic image. We analyze the performance of linear observer models in this task. We show that, if one chooses a suitable reconstruction method, a broad family of linear observers can exactly achieve the optimal detection performance attainable with any combination of a linear observer and linear reconstructor. This conclusion encompasses several well-known observer models from the literature, including models with a frequency-selective channel mechanism and certain types of internal noise. Interestingly, the "optimal" reconstruction methods are unregularized and in some cases quite unconventional. These results suggest that, for the purposes of designing regularized reconstruction methods that optimize lesion detectability, known-location tasks are of limited use.
We compare two different implementations of a 3D SPECT system model for iterative reconstruction, both of which compensate for non-uniform photon attenuation and depth-dependent system response. One implementation performs fast rotation of images represented using a basis of rectangular voxels, whereas the other represents images using a basis of rotationally symmetric volume elements. In our simulations the blob-based approach was found to slightly outperform the rotation-based one in terms of the bias-variance tradeoff in the reconstructed images. Their difference can be significant, however, in terms of computational load. The rotation-based method is faster for many typical SPECT reconstruction problems, but the blob-based one can be better-suited to cases where the reconstruction algorithm needs to process one volume element at a time.