A Multivariate Electrophysiological Endophenotype—Are Four Waves Better Than One?
9 January 2006. The venerable electroencephalograph (EEG) has undergone some refinement since the German scientist Hans Berger first recorded human brain electrical activity from his young son's brain in the 1920s. Arrays of electrodes are now monitored, and the resultant data managed by powerful computer software, but the basic procedure hasn't changed much over the years: A small soft electrode is attached to the head with a sticky, highly conductive substance and the activity of countless neurons is generated as wave forms, whether scratched out on paper by a pen or rendered in bright colors on a monitor. When subjects are presented with sensory stimuli, the EEG shows event-related potentials (ERPs)—changes in the wave forms that are not always attributable to any particular neurons or structures in the brain, but which are nonetheless valuable for their consistency and, it is argued, ability to point out underlying abnormalities.
There is consistent evidence that certain EEG and other electrophysiological measures of brain activity are abnormal in schizophrenia patients, as well as in some of their first-degree relatives. Combining some of these measurements may enhance their use as endophenotypes for the purposes of genetic studies, argues a paper published online last December 17 in Biological Psychiatry. Greg Price and colleagues at the University of Western Australia in Perth and the University of Newcastle, Callaghan, Australia, report that combining components of EEG waveforms such as mismatch negativity, P50, P300, along with antisaccade responses, creates an electrophysiological endophenotype with greater value for research than any of these features alone.
Various research groups have found alterations in ERP components in people with schizophrenia. With the further finding that first-degree relatives of people with schizophrenia also show abnormal ERP components, their value as intermediate or "endophenotypes" has been enhanced. Irving Gottesman, at the University of Minnesota, Minneapolis, and colleagues have argued that endophenotypes may be "closer" to any underlying genetic abnormalities than the diagnostic phenotypes of the disorder such as psychosis or flattened affect (Gottesman and Gould, 2003). (For a great deal more reading on this topic, see Gottesman's preliminary text and commentary by other researchers for SRF's discussion of endophenotypes in psychosis research.)
Following the suggestion of William Iocono of the University of Minnesota (Iocono, 1998), Price and colleagues decided to examine whether they could get a greater correlation of schizophrenia-related ERP abnormalities with the disorder by combining four tests:
1. The mismatch negativity (MMN) response, which is a component of the ERP generated about 200 milliseconds after the brain detects a sound deviating from a familiar test pattern (an "oddball" paradigm). This is reduced in amplitude in people with schizophrenia and first-degree relatives.
2. The P50 (for an ERP waveform seen 50 microseconds post-stimulus), which is proposed to be a measure of auditory gating in the brain, a reflection of the fact that the brain pays less attention to the second of two identical sound stimuli (see SRF related news story). Normally, the P50 to the second sound is much lower than that to the first, but in people with schizophrenia and their first-degree relatives, the response is barely diminished.
3. The P300 ERP component, which is an increase seen about 300 milliseconds after a deviant tone that the subject has been asked to listen for in an oddball paradigm. Reduced amplitude of the increase has been reported in people with schizophrenia and first-degree relatives.
4. Antisaccades, which are eye movements away from a visual stimulus that suddenly appears in the visual field. Overriding the natural impulse to look at the new stimulus is more difficult for people with schizophrenia, as well as their close relatives.
Price and colleagues replicated the findings for these individual endophenotypes in their study subjects with schizophrenia (n = 60) versus controls (n = 44), and for MMN and antisaccades among family members of the probands (n = 53). However, the authors report that they were surprised to find that, with the exception of the antisaccade measures, there was little correlation among the different endophenotypes (similar to findings in schizophrenia by Javitt et al., 1995 and Louchart de la Chappelle et al., 2005, but in contrast to Cadenhead et al., 2002).
A multivariate model of all four endophenotypes, using data from just probands and controls (excluding family members of the probands), was 80 percent accurate in distinguishing between the two groups, a better performance than any of the endophenotypes alone. When the researchers reassigned proband family members into proband and control groups, where "proband" now signifies the presence of the abnormal electrophysiologic responses whether or not schizophrenia is present, they found a greater degree of group separation in the multivariate model. "This multivariate endophenotype might be used to increase the power in genetic linkage and association analyses," the authors conclude.—Hakon Heimer.
Price GW, Michie PT, Johnston J, Innes-Brown H, Kent A, Clissa P, Jablensky AV. A multivariate electrophysiological endophenotype, from a unitary cohort, shows greater research utility than any single feature in the Western Australian Family Study of Schizophrenia. Biol Psychiatry. 2005 Dec 17; [Epub ahead of print] Abstract
Comments on News and Primary Papers
Primary Papers: A multivariate electrophysiological endophenotype, from a unitary cohort, shows greater research utility than any single feature in the Western Australian family study of schizophrenia.Comment by: Robert Freedman
Submitted 9 January 2006
Posted 9 January 2006
Combined endophenotypes have frequently been raised as a possibility for the analysis of heritability in schizophrenia. They present practical as well as theoretical challenges. Practically, accurate determination of multiple phenotypes taxes the capability of both the laboratory and the subject. For example, among the endophenotypes in the Price et al. paper, some, like P50 suppression, require the subject to passively hear the sounds, without attempting to place significance on the first or second sound. Otherwise, the response to the second sound is enhanced. However, P300 paradigms often require that each sound be attended to carefully, to discern which are targets. Theoretically, if endophenotypes are proposed to be more closely related to a specific neuronal mechanism and its genetic determinants, then do multiple phenotypes represent different or overlapping sets of genes? Is combination of the endophenotypes implicitly endorsing an overlap hypothesis? If so, would larger correlation coefficients be expected? We have had experience with one such composite phenotype, which did detect evidence for linkage (Myles-Worsely et al., 1999).
Myles-Worsley M. Coon H. McDowell J. Brenner C. Hoff M. Lind P. Bennett P. Freedman R. Clementz B. Byerley W. Linkage of a composite inhibitory phenotype to a chromosome 22q locus in eight Utah families.
Am J Med Genet. 1999 Oct 15;88(5):544-50. Abstract
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Primary Papers: A multivariate electrophysiological endophenotype, from a unitary cohort, shows greater research utility than any single feature in the Western Australian family study of schizophrenia.
Comment by: Danielle Dick
Submitted 9 January 2006
Posted 9 January 2006
I recommend this paper
Although the concept of endophenotypes was first applied to the field of psychiatry by Gottesman and Shields in 1972, it has only been more recently that the utility of the endophenotype concept in psychiatry has been realized. Renewed enthusiasm for endophenotypes was likely brought about, in part, by the difficulties encountered in gene identification efforts for psychiatric disorders, despite considerable evidence for their heritability. Other fields have a longer history of using more readily quantifiable risk components to identify susceptibility genes for disease; for example, cholesterol levels, blood pressure, and body fat have been analyzed to identify genetic risk factors influencing cardiovascular disease. It is incredibly exciting to see this strategy also proving fruitful in the area of psychiatric genetics. This has been true not only in regard to schizophrenia, as nicely illustrated by an analysis of cognitive trait components of schizophrenia that yielded higher lod scores than analyses of diagnosis (Paunio et al., 2004), but also in finding genes involved in other psychiatric problems. The use of electrophysiological endophenotypes has been critical in the identification of several genes related to alcohol dependence in the Collaborative Study of the Genetics of Alcoholism (COGA); importantly, association with these genes and alcohol dependence has already replicated in independent studies, a difficult feat in the area of psychiatric genetics. (See Dick et al., 2005, for a review of the utility of endophenotypes in gene identification efforts in COGA).
This paper by Price and colleagues provides a very nice extension of endophenotypic studies in schizophrenia, as it assessed multiple electrophysiological endophenotypes in its sample and integrated information across these measures to form a multivariate composite endophenotype. This represents an important advance over the standard model of individually studying single endophenotypes in isolation. The finding that the four commonly studied endophenotypes measured across participants in this study are largely uncorrelated is particularly interesting, and perhaps somewhat surprising. That these measures could be combined to create a multivariate composite that was more closely related to diagnosis than any individual feature represents a very interesting result. The utility of this composite endophenotype for gene identification remains to be seen, but this paper clearly suggests that the use of multivariate endophenotypes in genetic studies is an interesting future direction for study. In addition, the predictive accuracy of the multivariate endophenotype, while necessitating replication in independent samples, also has interesting implications for the current NIH movement to identify clinically relevant biomarkers to improve the diagnosis and classification of psychiatric disorders.
Dick, D. M., Jones, K., Saccone, N., Hinrichs, A. L., Wang, J. C., Goate, A., Bierut, L., Almasy, L., Schuckit, M., Hesselbrock, V., Tischfield, J. A., Foroud, T., Edenberg, H. J., Porjesz, B. and Begleiter, H. (2005, December, Epub ahead of print). Endophenotypes Successfully Lead to Gene Identification: Results from the Collaborative Study on the Genetics of Alcoholism. Behavior Genetics.
Gottesman II, Shields J. (1972) Schizophrenia and Genetics; a Twin Study Vantage Point. Academic Press, Inc., New York.
Paunio, T., Tuulio-Henriksson, A., Hiekkalinna, T., Perola, M., Varilo, T., Partonen, T., Cannon, T. D., Lonnqvist, J. and Peltonen, L. (2004). Search for cognitive trait components of schizophrenia reveals a locus for verbal learning and memory on 4q and for visual working memory on 2q. Human Molecular Genetics 15: 1693-1702.
View all comments by Danielle DickComment by: Greg Price, Assen Jablensky
Submitted 18 January 2006
Posted 20 January 2006
I recommend the Primary Papers
We appreciate the SRF focus on our article (Price et al., 2005) and the comments by Robert Freedman, Danielle Dick, and other contributors to the general topic of endophenotypes. A couple of points raised call for a brief response.
Freedman’s query whether by combining several endophenotypes we implicitly assume that “overlapping sets of genes” are involved can be answered in the affirmative. It is now generally accepted that no 1:1 relationship exists between genes and phenotypes in the polygenic (or oligogenic) disorders. Similarly to the multiple interrelated neural systems, the sets of susceptibility and modifier genes operate as complex interacting networks that functional genomics is only now beginning to understand and tease out (see Liu et al., 2002; Jablensky, 2004). In this context, the requirement that the “genetic architecture” of the relevant endophenotypes should be simpler than that of the clinical phenotype of schizophrenia appears to be unwarranted and need not be a defining criterion of an endophenotype (see Michael Owen’s contribution to the SRF endophenotype discussion). Further, the seeming paradox of little correlation among the individual endophenotypes and the increase in relative risk when they are combined is compatible with their variable individual expression being offset by an upstream latent trait (see Deborah Levy’s comment in the SRF endophenotype discussion ).
In our experience, the main advantage of multivariate or composite endophenotypes is twofold, that is, (a) allowing a substantial increase in effect size, relative risk and, consequently, power for genetic analysis; and (b) providing tools for reducing the notorious heterogeneity of schizophrenia by parsing the broad clinical phenotype into relatively homogeneous subtypes that may have a distinct genetic basis. In the Western Australian Family Study of Schizophrenia, our research group systematically phenotyped, over the last 8 years, 388 members of 112 families with one or more affected members and over 150 population controls for multiple neurocognitive, neurological, electrophysiological, and personality features. By analyzing the whole-genome scan of these families using a composite neurocognitive endophenotype, which integrates performance measures across several domains, we recently demonstrated (Hallmayer et al., 2005) that a distinct subset of schizophrenia families (including ~30 percent of the probands in our sample) shares a pervasive cognitive deficit linked precisely (lod score 3.32) to the locus on 6p24-22 previously reported by Straub and colleagues (Straub et al., 1995) in a large sample of Irish multiplex schizophrenia families. Further genetic analyses of the Western Australian cohort will aim to explore the potential contribution of the multivariate electrophysiological endophenotype to a refined subclassification of schizophrenia.
Hallmayer JF, Kalaydjieva L, Badcock J, Dragovic M, Howell S, Michie PT, Rock D, Vile D, Williams R, Corder EH, Hollingsworth K, Jablensky A. Genetic evidence for a distinct subtype of schizophrenia characterized by pervasive cognitive deficit. Am J Hum Genet. 2005 Sep;77(3):468-76. Epub 2005 Jul 12.
Price GW, Michie PT, Johnston J, Innes-Brown H, Kent A, Clissa P, Jablensky A. A multivariate electrophysiological endophenotype, from a unitary cohort, shows greater research utility than any single feature in the Western Australian Family Study of Schizophrenia. Biol Psychiatry. 2005 Dec 17; [Epub ahead of print] Abstract
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Primary Papers: A multivariate electrophysiological endophenotype, from a unitary cohort, shows greater research utility than any single feature in the Western Australian family study of schizophrenia.
Comment by: Elvira Bramon
Submitted 20 February 2006
Posted 20 February 2006
Price and colleagues propose the use of a combination of multiple endophenotypes in schizophrenia research. Their article shows that their multivariate endophenotype provides the best method to distinguish affected from unaffected individuals with higher sensitivity and specificity than any individual measure of MMN or P300 amplitudes, P50 ratio or antisaccades.
At this stage, the article by Price et al. still awaits replication, but there are precedents where multiple rather than individual measures have improved the accuracy of tests. In pharmacogenetics, haplotypes are known to be more useful than individual single nucleotide polymorphisms in predicting clinical response as well as side effects to antipsychotics (Arranz et al., 2000; Malhotra et al., 2004) or antidepressants (Kirchheiner et al., 2004). A battery of multiple biological markers can better distinguish Alzheimer disease from other forms of dementia and can improve predictions about drug efficacy (Cacabelos et al., 2004).
As for the low correlations among P300, MMN, P50, and antisaccades, maybe this is not all that surprising. These tasks are very different in nature; P50 and MMN are passive, whilst P300 and antisaccades require the participant’s cooperation. In addition, they probably reflect very diverse neural processes from brain gating (P50) to response inhibition (antisaccades) and sustained attention and memory (P300) (Freedman et al., 1997; Maccabe et al., 2005; Umbricht and Krljes, 2005; Bramon et al., 2005). I would argue that highly correlated endophenotypes would be less useful in practice. It would take extra work to measure them, yet they would provide redundant information. Surely a multivariate endophenotype must include a number of independent, hence low to moderately correlated markers. The lack of correlations between ERP components found by Price et al. should not be an obstacle and may be an advantage.
This article has implications for the growing field of early detection of psychosis. A combination of endophenotypes using EEG, structural and functional imaging, and neuropsychological techniques as well as clinical information like family history and symptom dimensions may ultimately improve our accuracy discriminating patients from controls. If such a multivariate measure was developed, it would make sense to investigate its use in identifying people with high risk of developing psychosis (Pantelis et al., 2003; Yung et al., 2004; Brockhaus-Dumke et al., 2005), who could benefit from early therapeutic interventions.
So far there is no individual endophenotype fulfilling all the criteria outlined by Gottesman and Gould (2003). For example, the P300 waveform is one of the most heritable, yet its estimated heritability is around 60 percent only (Van Beijsterveldt et al., 2002). MMN is very reliable (Hall et al., 2004), but its heritability is only starting to be investigated (Hall et al., in press). There is a large overlap in performance between patients and controls for most if not all endophenotypes described. Thus, a combination of selected markers could at least theoretically get closer to the ideal endophenotype, one that can discriminate accurately between high and low liability to psychosis. The influence of schizophrenia-related genes like COMT, CHRNA7, or DISC1 has only been investigated for a few individual endophenotypes like cognitive function, P50 and P300 waveforms, and brain morphometry (Egan et al., 2001; Freedman et al., 2001; Blackwood et al., 2004; Ohnishi et al., 2006; Bramon et al., 2006). The findings from Price and colleagues open new, exciting possibilities using multivariate endophenotypes to try to understand the role of putative candidate genes in psychosis.
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Comments on Related News
Related News: Schizophrenia and Neurodegeneration—Case Bolstered by MRI, ElectrophysiologyComment by: Dan Javitt, SRF Advisor
Submitted 29 May 2007
Posted 29 May 2007
Salisbury et al., in the May 2007 issue of Archives of General Psychiatry, demonstrate associated progressive reductions in mismatch negativity (MMN) amplitude and Heschl’s gyrus reduction in schizophrenia. These findings provide strong support for involvement of auditory cortex in the pathogenesis of schizophrenia, and demonstrate that pathological changes in the illness are not confined to specific brain regions, such as prefrontal cortex, that receive the preponderance of attention.
Further, the manuscript helps resolve an important current controversy in the MMN literature. Deficits in MMN generation have been among the most consistent findings in chronic schizophrenia, with a recent meta-analysis showing large (~1 sd unit) effect size MMN reductions across studies (Umbricht et al., 2005). As noted by Salisbury et al., however, deficits have not been observed in first-episode patients (Salisbury et al., 2002; Umbricht et al., 2006). An unknown issue was whether the discrepancy between first-episode and chronic patients was due to within-subject change (the “degeneration” hypothesis), or whether those patients with small MMN at entry tended to be retained disproportionately in chronic samples because of the relationship between MMN generation and global outcome (e.g., Light and Braff, 2005) (the “distillation” hypothesis).
The present study suggests that at least some patients show reductions of both MMN amplitude and left HG volumes over time, lending at least partial support for the degeneration hypothesis. This finding is important in that it shows that the pathological process contributing to cognitive impairment in schizophrenia continues beyond first episode, and may be a target for pro-cognitive interventions. It should be noted that the degeneration continued despite treatment with atypical, as well as typical, antipsychotic medication.
As noted by Salisbury et al., the change in MR volume in schizophrenia is best conceived as atrophy of neurons, rather than degeneration. On a histological level, the volume reductions noted on MR correspond with reduced pyramidal cell size in postmortem tissue (e.g., Sweet et al., 2004). Interestingly, postmortem studies have yet to show volumetric reductions in HG despite the change in some compartments, suggesting that MR may be detecting changes in tissue parameters that are not apparent in postmortem histological examination. This study also complements a recent diffusion tensor imaging (DTI) study that showed correlations between white matter changes in auditory projection pathways and auditory processing deficits in schizophrenia (Leitman et al., 2007). The relationship between white matter and grey matter pathology requires further investigation.
There are additional lessons hidden in the Salisbury et al. study. Given the relationship between reduced MMN generation (a functional measure) and cortical volume (a structural measure), there is a strong tendency to assume that structural changes are the cause of functional changes. The findings by Salisbury et al., as well as the extrapolation to postmortem histological studies, argue strongly against such an interpretation. For example, in the Salisbury et al. study, the change in left HG volume from time 1 to time 2 was only 6 percent, whereas MMN declined by 33 percent over the same period of time. At time 2, HG volumes were only 2 percent smaller in schizophrenia patients vs. controls, whereas MMN was 35 percent smaller. These findings suggest that simple volume loss does not cause the reduction in MMN. Further, even though MMN reduction seems to stabilize following the first 1.5 years (e.g., Umbricht et al., 2006; Javitt et al., 1995), this may not be the case with volumetric deficits. Thus, in a prior sample of chronic patients, this same group reported reductions of 13 percent in HG volume (Hirayasu et al., 2000), as opposed to the 2 percent reduction observed in patients following 1.5-year follow-up. Rather than suggesting a primary role of degeneration, this suggests a “use it or lose it” relationship within auditory cortex, wherein persistent reduction of activity may lead over time to structural involution. Even in postmortem studies (e.g., Sweet et al., 2004), pyramidal cell volumes are reduced by only 10 percent, whereas MMN in chronic schizophrenia may be reduced by 40 percent or more (e.g., Salisbury et al., 2002; Umbricht et al., 2006).
As noted by Salisbury et al., acute treatment with NMDA antagonists leads to reduced MMN amplitude in both human (Umbricht et al., 2000) and animal (Javitt et al., 1996) models. NMDA receptors also play a critical role in synaptic spine development and maintenance (Matsuzaki et al., 2004). A possible explanation, therefore, is that reduced NMDA activity in auditory cortex leads to both MMN reductions and reductions in spine density. Alternatively, primary alteration in subpopulations of cortical glutamatergic cells could trigger the sequence of events leading to reduced MMN generation.
There are several other intriguing features to the dataset. For example, at baseline, there were several controls who had larger than median HG volumes, but nevertheless failed to generate MMN (i.e., <1 μV). In schizophrenia patients, this sector of the plot was entirely empty and the only subjects who failed to generate MMN were those with small HG volumes. This suggests that there may be fundamental differences in structure/function relationships. It is almost as interesting to know why some controls fail to generate MMN despite having adequate HG size, as it is to know why HG is reduced in schizophrenia.
The finding that the relationships hold only for left, not right, HG, also is worthy of further investigation, as is the finding that right HG volumes are reduced even at first episode and do not decline further. Finally, the correlation on reduced MMN amplitude at Fz with reduced HG volume reiterates once again the role of auditory, rather than frontal, cortices in mediating MMN generation deficits in schizophrenia.
View all comments by Dan Javitt
Related News: Schizophrenia and Neurodegeneration—Case Bolstered by MRI, Electrophysiology
Comment by: Lei Wang
Submitted 5 June 2007
Posted 5 June 2007
The authors reported a cross-sectional (first hospitalization or within 1 year of first hospitalization) and longitudinal (1.5-year follow-up) study of electrophysiologic testing (mismatch negativity, or MMN, amplitude) and high-resolution structural magnetic resonance imaging of Heschl gyrus and planum temporale gray matter volumes. Schizophrenia subjects showed longitudinal volume reduction of left hemisphere Heschl gyrus (P = .003), which was highly correlated with MMN reduction (r = 0.6; P = .04). The interrelated progressive reduction of functional and structural measures suggests progressive pathologic processes early in schizophrenia. The design of the study helped minimize the effect of medication, the authors commented, therefore allowing the interpretation that brain change is due to disease progression.
From an imaging perspective, this is a straightforward longitudinal study of brain structure following previously published image processing and measuring protocols (Kasai et al., 2003). T1- and T2-weighted MR scans were acquired using the same sequence and on the same scanner for all subjects and at all time points. All baseline and follow-up MR scans were bias-field corrected and used in a fully automated segmentation algorithm for tissue classification, and then realigned to standard coordinate space and re-sampled to isotropic voxel resolution for application of standard manual segmentation protocols. Intracranial content was also estimated. Inter-rater and intra-rater reliability for segmentation of the Heschl gyrus and planum temporale was very high (volume ICC ranging from 0.95 to 0.99) (Kasai et al., 2003).
The authors showed in their earlier paper (Kasai et al., 2003) that using this approach, the time-dependent change in the volume of intracranial content did not correlate with time-dependent volume changes of brain structures. While this is reassuring, a trend-level decrease of intracranial content in time (p = 0.065), however, does raise the possibility of some systematic bias such as scanner drift resulting in global scaling, especially considering the subjects’ ages of 21-24 years. Some solutions such as scaling the follow-up scans with respect to the baseline scans could be evaluated (Freeborough and Fox, 1997).
This well-designed and well-presented study adds to a growing body of evidence that longitudinal structural neuroimaging is an effective way to detect progressive changes in specific brain structure in patients with schizophrenia. The results of this study contribute to the debate over whether the pathogenesis of schizophrenia includes a neurodegenerative as well as neurodevelopmental component.
View all comments by Lei Wang
Related News: Schizophrenia and Neurodegeneration—Case Bolstered by MRI, Electrophysiology
Comment by: Robert McClure (Disclosure)
Submitted 10 June 2007
Posted 10 June 2007
Longitudinal increases in volume of the lateral ventricles and decreases in brain volume—progressive changes—are often observed over time early in the course of schizophrenia. There is not uniform agreement over the proper interpretation of these changes, prompting vigorous, healthy debate among investigators. A major point of contention appears to be whether these volume changes actually constitute evidence of active disease progression.
In the current study, the authors seek to bolster the case for structural progression by demonstrating evidence of interrelated progressive functional impairment. They buttress the case for structural progression by demonstrating a relationship between worsening deficit in mismatch negativity and auditory cortex volume decreases.
Identification of a direct causal relationship between the underlying pathophysiology of schizophrenia and volume losses observed early in the illness would conclusively demonstrate structural progression. Such a direct link has not yet been established, so the results of this study constitute only indirect evidence that structural progression is tied to the emergence of functional impairment. Results of longitudinal MRI studies are useful for identify factors potentially associated with these volume changes, including altered neurodevelopment, disease progression, mismatch negativity, antipsychotic medications, and yet unidentified factors. Until the underlying etiology of schizophrenia is known, what underlies longitudinal volume change in schizophrenia is unlikely to be determined.
Future research should focus on specifying the neurodevelopmental mechanisms that contribute to the cortical pathology central to schizophrenia.
View all comments by Robert McClure