Multisite neuroimaging studies can facilitate the investigation of brain-related changes in many contexts, including patient groups that are relatively rare in the general population. Though multisite studies have characterized the reliability of brain activation during working memory and motor functional magnetic resonance imaging tasks, emotion processing tasks, pertinent to many clinical populations, remain less explored. A traveling participants study was conducted with eight healthy volunteers scanned twice on consecutive days at each of the eight North American Longitudinal Prodrome Study sites. Tests derived from generalizability theory showed excellent reliability in the amygdala
(Eρ2=0.82), inferior frontal gyrus
(IFG;Eρ2=0.83), anterior cingulate cortex
(Eρ2=0.85), and fusiform gyrus
(Eρ2=0.91) for maximum activation and fair to excellent reliability in the amygdala
(Eρ2=0.42), and fusiform gyrus
(Eρ2=0.83) for mean activation across sites and test days. For the amygdala, habituation
(Eρ2=0.71) was more stable than mean activation. In a second investigation, data from 111 healthy individuals across sites were aggregated in a voxelwise, quantitative meta-analysis. When compared with a mixed effects model controlling for site, both approaches identified robust activation in regions consistent with expected results based on prior single-site research. Overall, regions central to emotion processing showed strong reliability in the traveling participants study and robust activation in the aggregation study. These results support the reliability of blood oxygen level-dependent signal in emotion processing areas across different sites and scanners and may inform future efforts to increase efficiency and enhance knowledge of rare conditions in the population through multisite neuroimaging paradigms.
fMRI; reliability; multisite; emotion; meta-analysis; amygdale
Progress in our understanding of brain disorders increasingly relies on the costly collection of large standardized brain magnetic resonance imaging (MRI) data sets. Moreover, the clinical interpretation of brain scans benefits from compare and contrast analyses of scans from patients with similar, and sometimes rare, demographic, diagnostic, and treatment status. A solution to both needs is to acquire standardized, research-ready clinical brain scans and to build the information technology infrastructure to share such scans, along with other pertinent information, across hospitals. This paper describes the design, deployment, and operation of a federated imaging system that captures and shares standardized, de-identified clinical brain images in a federation across multiple institutions. In addition to describing innovative aspects of the system architecture and our initial testing of the deployed infrastructure, we also describe the Standardized Imaging Protocol (SIP) developed for the project and our interactions with the Institutional Review Board (IRB) regarding handling patient data in the federated environment.
Architecture; MRI; brain; imaging; standardization; federation; sharing; open source; de-identification; protocol; health; pilot; infrastructure; hospitals
MiR-137 dysregulation has been implicated in the etiology of schizophrenia, but its functional role remains to be determined.
Functional magnetic resonance imaging scans were acquired on 48 schizophrenia patients and 63 healthy volunteers (total sample size n=111 subjects), with similar mean age and sex distribution, while subjects performed a Sternberg Item Response Paradigm with memory loads of 1, 3, and 5 numbers. Dorsolateral prefrontal cortex (DLPFC) retrieval activation for the working memory load of 3 numbers, for which hyperactivation had been shown in schizophrenia patients compared with controls, was extracted. The genome-wide association study confirmed schizophrenia risk SNP rs1625579 (miR-137 locus) was genotyped (schizophrenia: GG n=0, GT n=9, TT n=39; healthy volunteers: GG=2, GT n=15, and TT n=46). Fisher's Exact Test examined the effect of diagnosis on rs1625579 allele frequency distribution (p=ns). Mixed model regression analyses examined the effects of diagnosis and genotype on working memory performance measures and DLPFC activation.
Patients showed significantly higher left DLPFC retrieval activation on working memory load 3, lower working memory performance and longer response times compared with controls. There was no effect of genotype on working memory performance or response times in either group. However, individuals with the rs1625579 TT genotype had significantly higher left DLPFC activation than those with the GG/GT genotypes.
Our study suggests that the rs1625579 TT (miR-137 locus) schizophrenia risk genotype is associated with the schizophrenia risk phenotype DLPFC hyperactivation commonly considered a measure of brain inefficiency.
schizophrenia; rs1625579; mir137; imaging; working memory; genes
Previous fMRI studies of sensorimotor activation in schizophrenia have found in some cases hypoactivity, no difference, or hyperactivity when comparing patients with controls; similar disagreement exists in studies of motor laterality. In this multi-site fMRI study of a sensorimotor task in individuals with chronic schizophrenia and matched healthy controls, subjects responded with a right-handed finger press to an irregularly flashing visual checker board. The analysis includes eighty-five subjects with schizophrenia diagnosed according to the DSM-IV criteria and eighty-six healthy volunteer subjects. Voxel-wise statistical parametric maps were generated for each subject and analyzed for group differences; the percent Blood Oxygenation Level Dependent (BOLD) signal changes were also calculated over predefined anatomical regions of the primary sensory, motor, and visual cortex. Both healthy controls and subjects with schizophrenia showed strongly lateralized activation in the precentral gyrus, inferior frontal gyrus, and inferior parietal lobule, and strong activations in the visual cortex. There were no significant differences between subjects with schizophrenia and controls in this multi-site fMRI study. Furthermore, there was no significant difference in laterality found between healthy controls and schizophrenic subjects. This study can serve as a baseline measurement of schizophrenic dysfunction in other cognitive processes.
Motor Cortex; Schizophrenia; Motor Dysfunction; Functional MRI; Laterality Quotient; Power Analysis
Schizophrenia patients show significant subcortical brain abnormalities. We examined these abnormalities using automated image analysis software and provide effect size estimates for prospective multi-scanner schizophrenia studies. Subcortical and intracranial volumes were obtained using FreeSurfer 5.0.0 from high-resolution structural imaging scans from 186 schizophrenia patients (mean age±SD=38.9±11.6, 78% males) and 176 demographically similar controls (mean age±SD=37.5±11.2, 72% males). Scans were acquired from seven 3-Tesla scanners. Univariate mixed model regression analyses compared between-group volume differences. Weighted mean effect sizes (and number of subjects needed for 80% power at α=0.05) were computed based on the individual single site studies as well as on the overall multi-site study. Schizophrenia patients have significantly smaller intracranial, amygdala, and hippocampus volumes and larger lateral ventricle, putamen and pallidum volumes compared with healthy volunteers. Weighted mean effect sizes based on single site studies were generally larger than effect sizes computed based on analysis of the overall multi-site sample. Prospectively collected structural imaging data can be combined across sites to increase statistical power for meaningful group comparisons. Even when using similar scan protocols at each scanner, some between-site variance remains. The multi-scanner effect sizes provided by this study should help in the design of future multi-scanner schizophrenia imaging studies.
Psychosis; Magnetic resonance imaging (MRI); Effect size; Imaging
Bipolar I disorder is a highly heritable psychiatric illness with undetermined predisposing genetic and environmental risk factors. We examined familial contributions to hippocampal morphology in bipolar disorder, using a population-based twin cohort design.
We acquired high-resolution brain MRI scans from 18 adult patients with bipolar I disorder [BPI; mean age 45.6 ± 8.69 (SD); 10 lithium-treated], 14 non-bipolar co-twins, and 32 demographically matched healthy comparison twins. We used three-dimensional radial distance mapping techniques to visualize hippocampal shape differences between groups.
Lithium-treated BPI patients had significantly larger global hippocampal volume compared to both healthy controls (9%) and non-bipolar co-twins (12%), and trend-level larger volumes relative to non-lithium-treated BPI patients (8%). In contrast, hippocampal volumes in non-lithium-treated BPI patients did not differ from those of non-bipolar co-twins and control twins. 3D surface maps revealed thicker hippocampi in lithium-treated BPI probands compared with control twins across the entire anterior-to-posterior extent of the cornu ammonis (CA1 and 2) regions, and the anterior part of the subiculum. Unexpectedly, co-twins also showed significantly thicker hippocampi compared with control twins in regions that partially overlapped those showing effects in the lithium treated BPI probands.
These findings suggest that regionally thickened hippocampi in bipolar I disorder may be partly due to familial factors and partly due to lithium-induced neurotrophy, neurogenesis, or neuroprotection. Unlike schizophrenia, hippocampal alterations in co-twins of bipolar I disorder probands are likely to manifest as subtle volume excess rather than deficit, perhaps indicating protective rather than risk effects.
bipolar disorder; magnetic resonance imaging; hippocampus; shape; volume; mood disorders; twin; morphology
The Scale for the Assessment of Positive Symptoms (SAPS), the Scale for the Assessment of Negative Symptoms (SANS), and the Positive and Negative Syndrome Scale for Schizophrenia (PANSS) are the most widely used schizophrenia symptom rating scales, but despite their co-existence for 25 years no easily usable between-scale conversion mechanism exists. The aim of this study was to provide equations for between-scale symptom rating conversions. Two- hundred-and-five schizophrenia patients [mean age±SD=39.5±11.6), 156 males] were assessed with the SANS, SAPS, and PANSS. Pearson’s correlations between symptom scores from each of the scales were computed. Linear regression analyses, on data from 176 randomly selected patients, were performed to derive equations for converting ratings between the scales. Intraclass correlations, on data from the remaining 29 patients, not part of the regression analyses, were performed to determine rating conversion accuracy. Between-scale positive and negative symptom ratings were highly correlated. Intraclass correlations between the original positive and negative symptom ratings and those obtained via conversion of alternative ratings using the conversion equations were moderate to high (ICCs = 0.65 to 0.91). Regression-based equations may be useful for conversion between schizophrenia symptom severity as measured by the SANS/SAPS and PANSS, though additional validation is warranted. This study’s conversion equations, implemented at http::/converteasy.org, may aid in the comparison of medication efficacy studies, in meta- and mega-analyses examining symptoms as moderator variables, and in retrospective combination of symptom data in multi-center data sharing projects that need to pool symptom rating data when such data are obtained using different scales.
schizophrenia; symptoms; Marder; conversion; meta; multi-center
Clinical research employing functional magnetic resonance imaging (fMRI) is often conducted within the connectionist paradigm, focusing on patterns of connectivity between voxels, regions of interest (ROIs) or spatially distributed functional networks. Connectivity-based analyses are concerned with pairwise correlations of the temporal activation associated with restrictions of the whole-brain hemodynamic signal to locations of a priori interest. There is a more abstract question however that such spatially granular correlation-based approaches do not elucidate: Are the broad spatiotemporal organizing principles of brains in certain populations distinguishable from those of others? Global patterns (in space and time) of hemodynamic activation are rarely scrutinized for features that might characterize complex psychiatric conditions, aging effects or gender—among other variables of potential interest to researchers. We introduce a canonical, transparent technique for characterizing the role in overall brain activation of spatially scaled periodic patterns with given temporal recurrence rates. A core feature of our technique is the spatiotemporal spectral profile (STSP), a readily interpretable 2D reduction of the native four-dimensional brain × time frequency domain that is still “big enough” to capture important group differences in globally patterned brain activation. Its power to distinguish populations of interest is demonstrated on a large balanced multi-site resting fMRI dataset with nearly equal numbers of schizophrenia patients and healthy controls. Our analysis reveals striking differences in the spatiotemporal organization of brain activity that correlate with the presence of diagnosed schizophrenia, as well as with gender and age. To the best of our knowledge, this is the first demonstration that a 4D frequency domain analysis of full volume fMRI data exposes clinically or demographically relevant differences in resting-state brain function.
fMRI; spatiotemporal frequency domain; schizophrenia; multidimensional Fourier transform; brain dynamics
MicroRNAs (miRNAs) are small non-coding RNAs that act as potent regulators of gene expression. A recent GWAS reported the rs1625579 SNP, located downstream of miR-137, as the strongest new association with schizophrenia (Ripke et al., 2011). Prior to this GWAS finding, a schizophrenia imaging-genetic study found miR-137 target genes significantly enriched for association with activation in the dorsolateral prefrontal cortex (DLPFC) (Potkin et al., 2010).
We investigated the expression levels of miR-137 and three candidate target genes (ZNF804A, CACNA1C, TCF4) in the DLPFC of postmortem brain tissue from 2 independent cohorts: 1) 26 subjects (10 control (CTR), 7 schizophrenia (SZ), 9 bipolar disorder (BD)) collected at the UCI brain bank; and 2) 99 subjects (33 CTR, 35 SZ, 31 BD) obtained from the Stanley Medical Research Institute (SMRI). MiR-137 expression in the DLPFC did not differ between diagnoses. We also explored the relationship between rs1625579 genotypes and miR-137 expression. Significantly lower miR-137 expression levels were observed in the homozygous TT subjects compared to TG and GG subjects in the control group (30% decrease, p-value=0.03). Moreover, reduced miR-137 levels in TT subjects corresponded to increased levels of the miR-137 target gene TCF4. The miR-137 expression pattern in 9 brain regions was significant for regional effect (ANOVA p-value=1.83E-12), with amygdala and hippocampus having the highest miR-137 expression level. In conclusion, decreased miR-137 expression is associated with the SZ risk allele of rs1625579, and potential regulation of TCF4, another SZ candidate gene. This study offers additional support for involvement of miR-137 and downstream targets as mechanisms of risk for psychiatric disorders.
schizophrenia; bipolar disorder; rs1625579; miR-137; TCF4; gene expression
Multi-site longitudinal neuroimaging designs are used to identify differential brain structural change associated with onset or progression of disease. The reliability of neuroanatomical measurements over time and across sites is a crucial aspect of power in such studies. Prior work has found that while within-site reliabilities of neuroanatomical measurements are excellent, between-site reliability is generally more modest. Factors that may increase between-site reliability include standardization of scanner platform and sequence parameters and correction for between-scanner variations in gradient nonlinearities. Factors that may improve both between- and within-site reliability include use of registration algorithms that account for individual differences in cortical patterning and shape. In this study 8 healthy volunteers were scanned twice on successive days at 8 sites participating in the North American Prodrome Longitudinal Study (NAPLS). All sites employed 3 Tesla scanners and standardized acquisition parameters. Site accounted for 2 to 30% of the total variance in neuroanatomical measurements. However, site-related variations were trivial (<1%) among sites using the same scanner model and 12-channel coil or when correcting for between-scanner differences in gradient nonlinearity and scaling. Adjusting for individual differences in sulcal-gyral geometries yielded measurements with greater reliabilities than those obtained using an automated approach. Neuroimaging can be performed across multiple sites at the same level of reliability as at a single site, achieving within- and between-site reliabilities of 0.95 or greater for gray matter density in the majority of voxels in the prefrontal and temporal cortical surfaces as well as for the volumes of most subcortical structures.
Magnetic Resonance Imaging; Neuroanatomy; Reproducibility of Results; Computer-Assisted Image Processing; Cerebral Cortex; Thalamus; Hippocampus; Amygdala
The Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) Consortium is a collaborative network of researchers working together on a range of large-scale studies that integrate data from 70 institutions worldwide. Organized into Working Groups that tackle questions in neuroscience, genetics, and medicine, ENIGMA studies have analyzed neuroimaging data from over 12,826 subjects. In addition, data from 12,171 individuals were provided by the CHARGE consortium for replication of findings, in a total of 24,997 subjects. By meta-analyzing results from many sites, ENIGMA has detected factors that affect the brain that no individual site could detect on its own, and that require larger numbers of subjects than any individual neuroimaging study has currently collected. ENIGMA’s first project was a genome-wide association study identifying common variants in the genome associated with hippocampal volume or intracranial volume. Continuing work is exploring genetic associations with subcortical volumes (ENIGMA2) and white matter microstructure (ENIGMA-DTI). Working groups also focus on understanding how schizophrenia, bipolar illness, major depression and attention deficit/hyperactivity disorder (ADHD) affect the brain. We review the current progress of the ENIGMA Consortium, along with challenges and unexpected discoveries made on the way.
Genetics; MRI; GWAS; Consortium; Meta-analysis; Multi-site
There is a major resurgence of interest in brown adipose tissue (BAT) biology, particularly regarding its determinants and consequences in newborns and infants. Reliable methods for non-invasive BAT measurement in human infants have yet to be demonstrated. The current study first validates methods for quantitative BAT imaging of rodents post mortem followed by BAT excision and re-imaging of excised tissues. Identical methods are then employed in a cohort of in vivo infants to establish the reliability of these measures and provide normative statistics for BAT depot volume and fat fraction. Using multi-echo water-fat MRI, fat- and water-based images of rodents and neonates were acquired and ratios of fat to the combined signal from fat and water (fat signal fraction) were calculated. Neonatal scans (n = 22) were acquired during natural sleep to quantify BAT and WAT deposits for depot volume and fat fraction. Acquisition repeatability was assessed based on multiple scans from the same neonate. Intra- and inter-rater measures of reliability in regional BAT depot volume and fat fraction quantification were determined based on multiple segmentations by two raters. Rodent BAT was characterized as having significantly higher water content than WAT in both in situ as well as ex vivo imaging assessments. Human neonate deposits indicative of bilateral BAT in spinal, supraclavicular and axillary regions were observed. Pairwise, WAT fat fraction was significantly greater than BAT fat fraction throughout the sample (ΔWAT-BAT = 38%, p<10−4). Repeated scans demonstrated a high voxelwise correlation for fat fraction (Rall = 0.99). BAT depot volume and fat fraction measurements showed high intra-rater (ICCBAT,VOL = 0.93, ICCBAT,FF = 0.93) and inter-rater reliability (ICCBAT,VOL = 0.86, ICCBAT,FF = 0.93). This study demonstrates the reliability of using multi-echo water-fat MRI in human neonates for quantification throughout the torso of BAT depot volume and fat fraction measurements.
This report provides practical recommendations for the design and execution of Multi-Center functional Magnetic Resonance Imaging (MC-fMRI) studies based on the collective experience of the Function Biomedical Informatics Research Network (FBIRN). The paper was inspired by many requests from the fMRI community to FBIRN group members for advice on how to conduct MC-fMRI studies. The introduction briefly discusses the advantages and complexities of MC-fMRI studies. Prerequisites for MC-fMRI studies are addressed before delving into the practical aspects of carefully and efficiently setting up a MC-fMRI study. Practical multi-site aspects include: (1) establishing and verifying scan parameters including scanner types and magnetic fields, (2) establishing and monitoring of a scanner quality program, (3) developing task paradigms and scan session documentation, (4) establishing clinical and scanner training to ensure consistency over time, (5) developing means for uploading, storing, and monitoring of imaging and other data, (6) the use of a traveling fMRI expert and (7) collectively analyzing imaging data and disseminating results. We conclude that when MC-fMRI studies are organized well with careful attention to unification of hardware, software and procedural aspects, the process can be a highly effective means for accessing a desired participant demographics while accelerating scientific discovery.
Functional magnetic resonance imaging; fMRI; multi-center; multi-site; FIRST Biomedica Informatics Research Network; FBIRN
Structural brain measures are employed as endophenotypes in the search for schizophrenia susceptibility genes. We analyzed two independent structural imaging datasets with voxel-based morphometry and with source-based morphometry, a multivariate, independent components analysis, to determine the stability and heritability of regional gray matter concentration abnormalities in schizophrenia. The samples comprised 209 and 102 patients with schizophrenia and 208 and 96 healthy volunteers, respectively. The second sample additionally included non-ill siblings of participants with and without schizophrenia. A standard voxel-based analysis showed reproducible regional gray matter deficits in the affected participants compared with unrelated, unaffected controls in both datasets: patients showed significant gray matter concentration deficits in cortical frontal, temporal, and insular lobes. Source-based morphometry (SBM) was applied to the gray matter images of the entire sample to determine the effects of diagnosis on networks of covarying structures. The SBM analysis extracted 24 significant sets of covarying regions (components). Four of these components showed significantly lower gray matter concentrations in patients (p < .05). We determined the familiality of the observed SBM components based on 66 sibling pairs (25 discordant for schizophrenia). Two components, one including the medial frontal, insular, inferior frontal, and temporal lobes, and the other including the posterior occipital lobe, showed significant familiality (p < .05). We conclude that structural brain deficits in schizophrenia are replicable, and that SBM can extract unique familial and likely heritable components. SBM provides a useful data reduction technique that can provide measures that may serve as endophenotypes for schizophrenia.
Schizophrenia; brain structure; heritability; Independent Components Analysis
Identifying genetic variants influencing human brain structures may reveal new biological mechanisms underlying cognition and neuropsychiatric illness. The volume of the hippocampus is a biomarker of incipient Alzheimer’s disease1,2 and is reduced in schizophrenia3, major depression4 and mesial temporal lobe epilepsy5. Whereas many brain imaging phenotypes are highly heritable6,7, identifying and replicating genetic influences has been difficult, as small effects and the high costs of magnetic resonance imaging (MRI) have led to underpowered studies. Here we report genome-wide association meta-analyses and replication for mean bilateral hippocampal, total brain and intracranial volumes from a large multinational consortium. The intergenic variant rs7294919 was associated with hippocampal volume (12q24.22; N = 21,151; P = 6.70 × 10−16) and the expression levels of the positional candidate gene TESC in brain tissue. Additionally, rs10784502, located within HMGA2, was associated with intracranial volume (12q14.3; N = 15,782; P = 1.12 × 10−12). We also identified a suggestive association with total brain volume at rs10494373 within DDR2 (1q23.3; N = 6,500; P = 5.81 × 10−7).
Why do memory abilities vary so greatly across individuals and cognitive domains? Although memory functions are highly heritable, what exactly is being genetically transmitted? Here we review evidence for the contribution of both common and partially independent inheritance of distinct aspects of memory function. We begin by discussing the assessment of long-term memory and its underlying neural and molecular basis. We then consider evidence for both specialist and generalist genes underlying individual variability in memory, indicating that carving memory into distinct subcomponents may yield important information regarding its genetic architecture. And finally we review evidence from both complex and single-gene disorders, which provide insight into the molecular mechanisms underlying the genetic basis of human memory function.
heritability; declarative memory; schizophrenia; Alzheimer’s Disease; Neurofibromatosis I
Background: This multi-site study compares resting state fMRI amplitude of low frequency fluctuations (ALFF) and fractional ALFF (fALFF) between patients with schizophrenia (SZ) and healthy controls (HC).
Methods: Eyes-closed resting fMRI scans (5:38 min; n = 306, 146 SZ) were collected from 6 Siemens 3T scanners and one GE 3T scanner. Imaging data were pre-processed using an SPM pipeline. Power in the low frequency band (0.01–0.08 Hz) was calculated both for the original pre-processed data as well as for the pre-processed data after regressing out the six rigid-body motion parameters, mean white matter (WM) and cerebral spinal fluid (CSF) signals. Both original and regressed ALFF and fALFF measures were modeled with site, diagnosis, age, and diagnosis × age interactions.
Results: Regressing out motion and non-gray matter signals significantly decreased fALFF throughout the brain as well as ALFF in the cortical edge, but significantly increased ALFF in subcortical regions. Regression had little effect on site, age, and diagnosis effects on ALFF, other than to reduce diagnosis effects in subcortical regions. There were significant effects of site across the brain in all the analyses, largely due to vendor differences. HC showed greater ALFF in the occipital, posterior parietal, and superior temporal lobe, while SZ showed smaller clusters of greater ALFF in the frontal and temporal/insular regions as well as in the caudate, putamen, and hippocampus. HC showed greater fALFF compared with SZ in all regions, though subcortical differences were only significant for original fALFF.
Conclusions: SZ show greater eyes-closed resting state low frequency power in frontal cortex, and less power in posterior lobes than do HC; fALFF, however, is lower in SZ than HC throughout the cortex. These effects are robust to multi-site variability. Regressing out physiological noise signals significantly affects both total and fALFF measures, but does not affect the pattern of case/control differences.
resting state fMRI; LFO; ALFF; schizophrenia; multi-site studies; effect size
Callosal volume reduction has been observed in patients with bipolar disorder, but whether these deficits reflect genetic vulnerability to the illness remains unresolved. Here, we used computational methods to map corpus callosum abnormalities in a population-based sample of twin pairs discordant for bipolar disorder. Twenty-one probands with bipolar I disorder (mean age 44.4 ± 7.5 years; 48% female), 19 of their non-bipolar co-twins, and 34 demographically matched control twin individuals underwent magnetic resonance imaging. Three-dimensional callosal surface models were created to visualize its morphologic variability and to localize group differences. Neurocognitive correlates of callosal area differences were additionally investigated in a subsample of study participants. Bipolar (BPI) probands, but not their co-twins, showed significant callosal thinning and area reduction, most pronounced in the genu and splenium, relative to healthy twins. Altered callosal curvature was additionally observed in BPI probands. In bipolar probands and co-twins, genu and splenium midsagittal areas were significantly correlated with verbal processing speed and response inhibition. These findings suggest that aberrant connections between cortical regions—possibly reflecting decreased myelination of white matter tracts—may be involved in bipolar pathophysiology. However, findings of callosal thinning appear to be disease related, rather than reflecting genetic vulnerability to bipolar illness.
magnetic resonance imaging; mood disorders; myelination; processing speed; twin study
No objective diagnostic biomarkers or laboratory tests have yet been developed for psychotic illness. Magnetic resonance imaging (MRI) studies consistently find significant abnormalities in multiple brain structures in psychotic patients relative to healthy control subjects, but these abnormalities show substantial overlap with anatomic variation that is in the normal range and therefore nondiagnostic. Recently, efforts have been made to discriminate psychotic patients from healthy individuals using machine-learning-based pattern classification methods on MRI data.
Three-dimensional cortical gray matter density (GMD) maps were generated for 36 patients with recent-onset psychosis and 36 sex- and age-matched control subjects using a cortical pattern matching method. Between-group differences in GMD were evaluated. Second, the sparse multinomial logistic regression classifier included in the Multivariate Pattern Analysis in Python machine-learning package was applied to the cortical GMD maps to discriminate psychotic patients from control subjects.
Patients showed significantly lower GMD, particularly in prefrontal, cingulate, and lateral temporal brain regions. Pattern classification analysis achieved 86.1% accuracy in discriminating patients from controls using leave-one-out cross-validation.
These results suggest that even at the early stage of illness, psychotic patients present distinct patterns of regional cortical gray matter changes that can be discriminated from the normal pattern. These findings indicate that we can detect complex patterns of brain abnormality in early stages of psychotic illness, which has critical implications for early identification and intervention in individuals at ultra-high risk for developing psychosis/schizophrenia.
Classification; cortical pattern matching; MRI; psychosis; PyMVPA; schizophrenia
Although schizophrenia is highly heritable, the search for susceptibility genes has been challenging. The “endophenotype” approach is an alternative method for measuring phenotypic variation that may make it easier to identify susceptibility genes in the context of complexly inherited traits. Neuroimaging methods in particular offer a powerful way to bridge the neurobiology of genes and behavior. Such investigations may be further empowered by complementary strategies involving chromosomal abnormalities associated with schizophrenia, which can help to localize causative genes and better understand the genetic complexity of the illness. Here, we illustrate our use of these convergent approaches, with a focus on neuroimaging studies using novel computational brain mapping algorithms, to investigate genetic influences on brain structure in the development of psychosis. These studies provide compelling evidence that specific genetic loci suspected to predispose to schizophrenia may affect quantitative variation in neural indicators underlying the neurobehavioral phenotype, and illustrate how genetic-neuroimaging paradigms can improve our understanding of the pathogenesis of this highly disabling mental illness.
psychosis; brain mapping; genetic; neuroanatomy; chromosome 22q11.2; velocardiofacial syndrome; twin study; DISC1
Language processing abnormalities are a hallmark feature of schizophrenia. Yet, no study to date has investigated underlying neural networks associated with discourse processing in adolescents at clinical high risk (CHR) for developing psychosis.
Forty CHR youth and 24 demographically comparable healthy controls underwent functional magnetic resonance imaging while performing a naturalistic discourse processing paradigm. We assessed differences in blood oxygenation level-dependent (BOLD) activity between task conditions (Topic Maintenance vs. Reasoning) and between groups. Furthermore, we examined the association of regional brain activity with symptom severity and social outcome at follow-up, 6 to 24 months after the scan.
Relative to controls, CHR participants showed increased neural activity in a network of language-associated brain regions, including the medial prefrontal cortex bilaterally, left inferior frontal (LIFG; BA44/45, 47) and middle temporal gyri, and the anterior cingulate (BA24&32). Further, increased activity in the superior temporal gyrus (STG), caudate, and LIFG distinguished those who subsequently developed psychosis. Within the CHR sample, severity of positive formal thought disorder at follow-up was positively correlated with signal change in the LIFG, superior frontal gyrus, and inferior/middle temporal gyri, whereas social outcome was inversely correlated with signal change in the LIFG and anterior cingulate.
These findings are consistent with a neural inefficiency hypothesis in those at greatest risk for psychosis, and additionally suggest that baseline activation differences may predict symptomatic and functional outcome. These results highlight the need to further investigate the neural systems involved in conversion to psychosis, and how language disruption changes over time in at-risk adolescents.
fMRI; schizophrenia; inferior frontal gyrus; psychosis prodrome; discourse; functional neuroimaging
Patients with schizophrenia show altered patterns of functional activation during working memory processing; specifically, high-performing patients appear to hyper-activate and low-performing patients appear to hypo-activate when compared with controls. It remains unclear how these individual differences in neurophysiological activation relate to the clinical presentation of the syndrome. In this study, this relationship is examined using partial least squares (PLS), a multivariate statistical technique that selects underlying latent variables based on the covariance between two sets of variables, in this case, clinical variables and regional fMRI activations during a verbal working memory task. The PLS analysis extracted two latent variables, and the significance of these associations was confirmed through permutation. Lower levels of activation during task performance across frontal and parietal regions of interest in the left hemisphere was found to covary with poorer role functioning and greater severity of negative and disorganized symptoms, while lower activation in right frontal and subcortical regions of interest was found to covary with better social functioning and fewer positive symptoms. These results suggest that appropriately lateralized patterns of functional activation during working memory processing are related to the severity of negative and disorganized symptoms and level of role and social functioning in schizophrenia.
fMRI; partial least squares; verbal working memory
Previous neuroimaging studies of working memory (WM) in schizophrenia have generated conflicting findings of hypo- and hyper-frontality, discrepancies potentially driven by differences in task difficulty and/or performance. This study proposes and tests a new model of the performance-activation relationship in schizophrenia by combining changes by load with overall individual differences in performance. Fourteen patients with recent-onset schizophrenia and eighteen controls underwent functional magnetic resonance imaging while performing a parametric verbal WM task. Group level differences followed a linear “cross-over” pattern, such that in controls, activation in the dorsolateral prefrontal cortex (DLPFC) increased as performance decreased, while patients showed the opposite. Overall, low performing patients were hypoactive and high performing patients hyperactive relative to controls. However, patients and controls showed similar functions of activation by load in which activation rises with task difficulty but levels off or slightly decreases at higher loads. Moreover, across all loads and at their own WM capacity, higher performing patients showed greater DLPFC activation than controls, while lower performing patients activated least. This study establishes a novel framework for predicting the relationship between functional activation and WM performance by combining changes of activation by WM load occurring within each subject with the overall differences in activation associated with general WM performance. Essentially, increasing task difficulty correlates asymptotically with increasing activation in all subjects, but depending on their behavioral performance, patients show overall hyper-versus hypofrontality, a pattern potentially derived from individual differences in underlying cellular changes that may relate to levels of functional disability.
prefrontal cortex; magnetic resonance imaging; efficiency; task performance; memory; schizophrenia; brain; cognition
Schizophrenia and related psychoses are associated with brain structural abnormalities. Recent findings in ‘at risk’ populations have identified progressive changes in various brain regions preceding illness onset, while changes especially in prefrontal and superior temporal regions have been demonstrated in first-episode schizophrenia patients. However, the timing of the cortical changes and their regional extent, relative to the emergence of psychosis, has not been clarified. We followed individuals at high-risk for psychosis to determine whether structural changes in the cerebral cortex occur with the onset of psychosis. We hypothesized that progressive volume loss occurs in prefrontal regions during the transition to psychosis.
35 individuals at ultra-high risk (UHR) for developing psychosis, of whom 12 experienced psychotic onset by 1-year follow-up (‘converters’), participated in a longitudinal structural MRI study. Baseline and follow-up T1-weighted MR images were acquired and longitudinal brain surface contractions were assessed using Cortical Pattern Matching.
Significantly greater brain contraction was found in the right prefrontal region in the ‘converters’ compared with UHR cases who did not develop psychosis (‘non-converters’).
These findings show cortical volume loss is associated with the onset of psychosis, indicating ongoing pathological processes during the transition stage to illness. The prefrontal volume loss is in line with structural and functional abnormalities in schizophrenia, suggesting a critical role for this change in the development of psychosis.
schizophrenia; MRI; brain mapping; longitudinal; prodrome; ultra-high risk