From Wikipedia, the free encyclopedia. Lewis Retrieved 22 April The American Journal of Psychiatry. Arch Gen Psychiatry. Journal of Abnormal Child Psychology. Ersche; P. Simon Jones; Guy B. Bullmore Feb 3, BMC Medical Genetics. Depression and Anxiety. Development and Psychopathology. Am J Psychiatry. Acta Psychiatr Scand. First, non-brain tissue was removed using an automated brain extraction procedure Smith, The T1-, T2- and PD-weighted images were then segmented using a multichannel tissue classification algorithm and probabilistic maps of grey matter, white matter, CSF and dural tissues were created by estimating the partial volume coefficient for each voxel, which represents the probability of each voxel belonging to one of four tissue classes Zhang et al.
The segmented grey matter partial-volume maps were registered in Montreal Neurological Institute MNI standard space by an affine transformation using a segmented grey matter template in FSL Sheehan et al. Briefly, over all participants, we estimated the correlations between log-transformed SSRT scores and normalized grey matter density at each voxel in the registered images where the probability of grey matter P GM was greater than 0. The normalization of grey matter density involved dividing each voxel's density estimate by the mean density of grey matter over all voxels in the brain thus correcting individual voxel values for between-subject differences in global grey matter volume.
The brain score for each participant was calculated as the sum of grey matter probabilities multiplied by the local weighted correlations with SSRT: i. The association between grey matter probability and SSRT was tested for statistical significance by a permutation test of d. The ordering of SSRT scores was randomly permuted before recalculation of the correlations with grey matter at each voxel, leading to an estimate of d under the null hypothesis.
Neurocognitive endophenotypes of obsessive-compulsive disorder | Brain | Oxford Academic
This cluster size threshold was chosen for illustrative purposes, to best demonstrate the large-scale anatomical covariation with SSRT scores. The choice of visualization thresholds makes no difference to the statistical significance of d the overall strength of correlation between grey matter density and SSRT or the calculation of brain scores for each participant. These measures of regional grey matter density were also compared between groups by ANOVA and post hoc testing.
We used two complementary techniques to assess the familiality of SSRT scores, or related grey matter systems brain score and grey matter density , in the OCD patients and their first-degree relatives. Then we randomly reassigned the observations to new pairs, so that each patient was now paired with a clinically unaffected individual to whom they were not personally related. Second, as another exploration of the similarity between patients and their relatives on behavioural and brain-based measures, we examined the strength and significance of the within-pair correlation of SSRT, brain score and grey matter densities between patients and their own relatives.
We used within-pair correlation rather than intraclass correlation because there was a natural ordering patient or relative within each pair. The three groups were well matched for age, verbal IQ, gender and handedness Table 1. Demographic, clinical and behavioural data for patients with OCD, their first-degree relatives and healthy, unrelated volunteers. An anatomical map of voxels strongly correlated with SSRT over all participants highlighted two extensive systems which were, respectively, positively and negatively correlated with latency of inhibitory processing Fig.
In the positively correlated system, longer SSRT was associated with increased grey matter probability. In the negatively correlated system, longer SSRT was associated with decreased grey matter probability. This predominantly frontal system comprised bilateral middle and medial orbitofrontal cortex BA 11, 47 , inferior frontal gyri BA 44, 45 , superior frontal and premotor cortices BA 6, 8, 9 , anterior cingulate cortex BA 32 and bilateral temporal cortical areas BA 21, 22, 37, 42 Table 2.
Anatomical details for brain regions where grey matter density was positively or negatively correlated with stop—signal reaction time SSRT.
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To explore these results further, we extracted grey matter values for the systems in Fig. Brain maps highlighting regions of most significant group difference in grey matter density between OCD patients and first-degree relatives compared to healthy volunteers. R marker indicates right side of the brain; x, y , and z indicate planes of brain maps; cross-hairs indicate point of peak correlation with the behavioural measure SSRT.
This observation is compatible with the alternative hypothesis that variance of true proband—relative pair differences is smaller because the phenotype in question, e. B Scatter plots exploring within-pair correlation between patients and their relatives for SSRT top panel and brain score bottom panel.
We applied the same permutation test to analyses of the variance of within-pair differences in grey matter density of the parieto-cingulo-striatal system positively correlated with SSRT and in grey matter density of the frontal system negatively correlated with SSRT. Taken together with the results on variance of within-pair differences in MRI and cognitive phenotypes, these data suggest that compared with a behavioural measure of response inhibition, the MRI systems correlated with response inhibitory processing are more strongly determined by familial factors shared between true proband—relative pairs.
These data provide empirical support for each of the four hypotheses motivating this study. We have identified extensive brain systems where grey matter density is positively or negatively correlated with variability in stop-signal task performance; and we have shown that patients with OCD and their relatives have structural abnormalities in these systems compared to healthy volunteers.
Finally, we have exploited our proband—relative pair design to assess the familiality of variation in cognitive and associated MRI phenotypes and shown that variation in brain systems correlated with inhibitory function is likely determined by familial factors in common between patients and their first-degree relative. In short, we have combined structural neuroimaging and cognitive testing to identify for the first time a neurocognitive endophenotype of OCD. The classical clinical symptoms of OCD are persistent, obsessional thoughts attended by an inability to inhibit compulsive behaviour repetition.
It therefore seems almost self-evident that inhibitory processes might be abnormal in OCD and there is empirical evidence in support of this hypothesis.
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Patients with OCD are impaired across a range of tests of inhibitory function including motor inhibition tasks, e. Our data, indicating that patients are impaired on a motor inhibition task, are consistent with this literature. Prior data on human brain systems underlying motor inhibition have been provided by lesion studies and neuroimaging. In a structural MRI study of patients following focal but variably located brain injuries, Aron et al.
Functional neuroimaging studies of motor inhibition have generally identified a more extensive but predominantly right-sided system of regions including orbitofrontal, dorsolateral and medial frontal, temporal and parietal cortices, the cerebellum and the basal ganglia Godefroy et al. In keeping with the focus on the RIFG in previous literature, we also found evidence that reduced grey matter density in this region was associated with prolonged SSRT. However, consistent with the functional neuroimaging data suggesting involvement of a network of regions in inhibition, we found that brain areas negatively correlated with latency of inhibitory processing in our data were not restricted to the RIFG but included regions such as bilateral orbitofrontal cortex, right premotor and anterior cingulate cortex, left dorsolateral prefrontal cortex and bilateral temporal cortex.
We also found a number of regions in cingulate cortex, parietal and dorsal occipital cortex, and basal ganglia where SSRT was positively correlated with grey matter density, i. As already discussed Introduction, Supplementary Figs 1 and 2 , there is somewhat inconsistent evidence in support of this hypothesis from structural MRI studies published to date. However, there is additional evidence for orbitofrontal dysfunction in OCD from positron emission tomography PET studies reporting abnormal resting or task-related orbitofrontal metabolism in OCD Baxter et al.
Functional MRI studies investigating executive function in OCD have also identified fronto-striatal abnormalities in patients Maltby et al. Moreover, fMRI studies have often also shown changes in activation of theoretically unanticipated regions such as the dorsolateral prefrontal cortex DLPFC and parietal cortex. For example, van den Heuvel et al. Thus our findings of extensive grey matter abnormality in orbitofrontal cortex, ventral and dorsal prefrontal cortex, cingulate cortex, parietal cortex, striatum and cerebellum include many of the regions anticipated by an orbitofronto-striatal model; but also include other regions such as parietal cortex or cerebellum , which have been reported in the functional neuroimaging literature and by some of the prior structural MRI studies Pujol et al.
As the neuroimaging evidence base grows and becomes more replicable, we predict that this will drive development of systems-level theory beyond the model of abnormality in a single cortico-striatal circuit. There is no universally accepted set of criteria to judge the validity of a candidate endophenotype. However, Gottesman proposed that endophenotypes are quantitative heritable traits that are abnormal in both probands and their relatives Gottesman and Gould, How well do our data on behavioural and MRI markers of inhibitory processing satisfy these criteria?
Both behavioural and MRI markers were quantitatively abnormal on average in both patients and relatives compared to healthy volunteers. However, the demonstration of strict sense heritability is impossible in the absence of a twin design controlling for shared environmental influences on trait variation in genetically related individuals. We have therefore adopted the logistically more feasible approach of assessing familial rather than strictly heritable effects on trait variation in a proband—relative pair design. We have used an innovative permutation test of the within-pair variance in trait differences, and within-pair correlations, to quantify familiality of variation in discordant proband—relative pairs, finding evidence for significant familial effects on variation in the MRI systems associated with inhibitory processing, but not on the behaviourally derived SSRT measure.
We conclude that the MRI markers of inhibitory processing more completely satisfy Gottesman's criteria by virtue of their greater familiality , perhaps reflecting the fact that structural variation in brain systems is more proximal to genetic effects than variation in task performance. This result strengthens the rationale for searching for neurocognitive endophenotypes in other complex behavioural disorders such as schizophrenia and bipolar disorder. Using probands and first-degree relatives to identify MRI endophenotypes has the immediate advantage of discounting any non-familial explanations such as exposure to psychotropic medication in the probands for abnormal patterns of brain structure.
Endophenotypes could also be used to refine diagnostic subclassification of patients based on the extent of their expression of endophenotypic abnormality , or to highlight additional abnormalities occurring only in patients not relatives , although these were not objectives of the current study. However, it is interesting also to consider how our results could be exploited in future to identify specific genes determining variation in brain systems important for motor inhibition.
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In principle, the grey matter density of the motor inhibitory system could be used as a quantitative trait in a genome-wide search for associated polymorphisms by quantitative trait locus QTL analysis. Although we currently lack sufficient experience of genome-wide QTL mapping based on human imaging, this has been successfully used to identify genetic markers associated with imaging measurements of cortical and subcortical grey matter volumes in inbred strains of mice Beatty and Laughlin, Another question concerns the diagnostic specificity of an inhibitory endophenotype for OCD.
Without such data, it is speculative but intriguing to consider that the same neurocognitive endophenotype might be related to important dimensions of these two traditionally distinct clinical syndromes. An innovative aspect of this study was the use of the PLS method, a technique previously employed mainly in the analysis of functional neuroimaging data McIntosh and Lobaugh, , to find structural brain systems optimally correlated with a behavioural variable. PLS was attractive for our purpose because there were prior theoretical reasons to expect that inhibitory deficits in OCD might be related to structural abnormalities at a systems level, rather than in a discrete brain region, and PLS is designed to optimize correlation between one or more exogenous behavioural variables and a set of correlated image voxels, without specifying a priori which voxels are likely to be components of the behaviourally correlated system.
Characterization of structure—function relationships at systems level has the considerable merit of minimizing the number of significance tests required.
In principle, partial least squares can be used as a multivariate analysis method to explore the relationships between multiple behavioural variables and multiple imaging variables. Here we have used it to test for association between a single behavioural variable and grey matter density at multiple voxels. To distinguish this application from the more general multivariate case, where PLS is used to find associations between multiple behavioural variables and imaging measures at multiple voxels, we have referred to our application as a multivoxel analysis because the non-parametric test for significant association is based on behavioural correlations summed across all voxels in the brain.
The main differences are that Soriano-Mas et al. More technically, the PLS approach has the relative merit of an entirely non-parametric resampling-based approach to significance testing which confers greater flexibility in choice of test statistics and greater robustness against violation of the conditions required for validity of parametric tests Worsley et al.
To the best of our knowledge, this is the first example of a potentially powerful experimental and data analytic strategy to identify cognitive and related brain structural endophenotypes of heritable but genetically complex neuropsychiatric disorders. In a sample of OCD patients, their first-degree relatives and unrelated healthy volunteers, we have found substantial evidence that variation in motor inhibitory control is correlated with grey matter density changes in an extensive system comprising orbitofrontal, cingulate and parietal cortical areas as well as striatal and other subcortical regions.
We have also tested rigorously by Gottesman's criteria the candidacy of these inhibition-related brain systems as the first neurocognitive endophenotype for obsessive-compulsive disorder. All other authors report no competing interests. Funding to pay the Open Access publication charges for this article was provided by the Wellcome Trust.
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Article Navigation. Close mobile search navigation Article Navigation. Personalized medicine in psychiatry is in need of biomarkers that resemble central nervous system function at the level of neuronal activity. Electroencephalography EEG during sleep or resting-state conditions and event-related potentials ERPs have not only been used to discriminate patients from healthy subjects, but also for the prediction of treatment outcome in various psychiatric diseases, yielding information about tailored therapy approaches for an individual.
This review focuses on baseline EEG markers for two psychiatric conditions, namely major depressive disorder and attention deficit hyperactivity disorder. It covers potential biomarkers from EEG sleep research and vigilance regulation, paroxysmal EEG patterns and epileptiform discharges, quantitative EEG features within the EEG main frequency bands, connectivity markers and ERP components that might help to identify favourable treatment outcome. Further, the various markers are discussed in the context of their potential clinical value and as research domain criteria, before giving an outline for future studies that are needed to pave the way to an electrophysiological biomarker-based personalized medicine.
In some fields of medicine, individualized and personalized treatment has become state of the art. Especially in oncology, the assessment of individual biological properties of the patient and the cancer cells helped to make treatment more efficient, reduce side effects and improve secondary prevention strategies [ 1 ]. However, a mental disorder is completely different from a well-observable and definable solid tumour: there is no clear organic correlate that is responsible for the symptoms; instead, multidimensional and possibly very heterogeneous alterations of brain function sum up to the clinical syndrome.
This underpins the suggested polygenetic nature of psychiatric disorders [ 4 ] and implies the need for endophenotypes that are seen as an intermediate step between genotype and behaviour. Endophenotypes are more closely related to genotype than behaviour alone and may be a possible way to stratify a population for genome-wide association studies [ 5 ].
Although some promising findings using different sets of clinical and neuroimaging endophenotypes in major depression have been reported [ 6 ], a recent study on psychophysiological endophenotypes can be seen as a drawback to this approach since the authors were unable to replicate significant associations between endophenotypes and candidate genes [ 7 ]. Given that the link between endophenotypes and genetics might not be that strong or simple as suggested, an association between endophenotypes and disorder might still be present and could help to improve treatment and diagnostic decisions.
The value of a personalized medicine approach is not determined by the association of a marker with genetic variants but by the improvement it yields for the diagnostic process by making it more objective and, probably more importantly, by the increased effectiveness of the treatment i. Currently several large-scale studies are ongoing that should be able to shed more light onto this development, such as the international Study to Predict Optimized Treatment Response iSPOT in 2, patients with MDD and children and adolescents with attention deficit hyperactivity disorder ADHD; see also Williams et al.
Sparked by the discovery that the mode of function of the human central nervous system is based on electric activity [ 11 , 12 ], the invention of electroencephalography EEG and its first description in humans [ 13 ] provided the possibility to analyse the brain at its core functional level. Taken as a tool for the assessment of biomarkers that, according to the definition, should be assessable objectively and provide information about physiological or pathological processes or responses to treatment interventions [ 14 ], EEG also fulfils the criteria of a cost-efficient, nowadays broadly available and already established tool in the diagnostic clinical practice.
Further, EEG captures ongoing neuronal activity with a temporal resolution that surpasses any other neuroimaging modality such as functional magnetic resonance imaging or positron emission tomography. Also, the electroencephalogram is not a surrogate marker of neuronal activity such as the blood deoxygenation level-dependent signal in functional magnetic resonance imaging or the glucose utilization in positron emission tomography; see Logothetis [ 15 ] but is a direct reflection of neuronal activity postsynaptic potentials [ 16 ].
It is therefore highly plausible that a personalized medicine approach in psychiatry could gain from electrophysiological markers. Although electrophysiological biomarkers have been studied throughout many psychiatric disorders, the current review is dedicated to only two of them: MDD and the ADHD.
The following work describes the current state of the art of baseline EEG parameters by means of their diagnostic and predictive prognostic value. Treatment-emergent biomarkers that yield information about changes in the early course of treatment will not be subject of the review, and the interested reader is referred to Olbrich and Arns [ 17 ] or Arns and Olbrich [ 18 ] for more coverage of those.
EEG biomarkers in sleep are robust, and their advantage can be found in the link between clinical symptoms such as sleep initiation problems, early awakening or disrupted sleep in both MDD and ADHD and electrophysiologically assessable parameters. In MDD, the most consistently reported findings include a disturbed sleep architecture, comprising an increased rapid eye movement density [ 19 , 20 ], decreased rapid eye movement sleep latency [ 21 , 22 ] and altered slow-wave sleep in MDD [ 23 , 24 ].
While slow-wave power seems to have a discriminative value between MDD and healthy controls HCs [ 23 , 25 ], a predictive value for recurrence of depressive symptoms was found for decreased slow-wave sleep, decreased sleep efficiency and delayed sleep onset [ 26 , 27 , 28 ]. Also the slow-wave activity itself seems to be important for treatment prediction. Luthringer et al. Still, Nissen et al. Besides classical sleep EEG parameters, also a decreased coherence within the beta, delta and theta bands in sleep EEG predicted non-response in adolescents and the occurrence of depressive episodes [ 33 ].
In ADHD there is a clear lack of studies that examine EEG-derived sleep parameters, although other measures such as actigraphy and salivary melatonin measurements suggest a delayed sleep onset in a majority of children and adults with ADHD, also termed sleep onset insomnia [ 34 , 35 , 36 ] characterized by a delayed melatonin onset. This delayed sleep onset results in reduced sleep duration - and thus chronic sleep restriction - in ADHD, which becomes visible as the typical drowsiness patterns that can be observed in the EEG such as impaired vigilance see EEG Vigilance below or excess theta waves see Frequency-Specific Biomarkers: Theta ; for a review, see also Arns et al.
Hegerl et al. The appeal of this marker reflecting a high tone of CNS arousal can be found in the linkage between clinical symptoms and EEG parameters of wakefulness regulation. A hyperstable vigilance regulation in MDD is interpreted as an electrophysiological correlate of the often reported sleep problems. Increased vigilance might further explain the behavioural withdrawal of patients suffering from MDD to avoid a further increase in arousal [ 40 ]. The EEG vigilance framework further suggests that a fast decline of EEG vigilance during rest might result in increased sensation seeking and hyperactive behaviour to stabilize wakefulness regulation.
Hegerl and Hensch [ 40 ] suppose that not only manic patients [ 41 ] reveal unstable EEG vigilance regulation patterns, but also patients suffering from ADHD [ 37 , 42 ]. In line with this, increased theta power as a marker of drowsiness has frequently been reported in patients with ADHD [ 43 ], and as described above the majority of patients with ADHD exhibit sleep onset insomnia [ 34 , 35 , 37 ], further supporting this notion.
The implications for treatment in psychiatric patients with paroxysmal patterns or epileptiform discharges - but without a history of seizures - remain unclear. It is still remarkable that several studies found that ADHD patients [ 56 , 57 , 58 ] do respond to anticonvulsant medication, e.
Furthermore, there is some evidence that antidepressant treatment augmentation with anti-epileptic drugs is effective in treatment resistant MDD [ 60 , 61 ], although data about the association of response and epileptiform discharges are lacking. As a further example, previous studies have demonstrated an association between paroxysmal EEG activity and panic attacks [for a review, see [ 62 ], [ 63 ]]. However, this requires further controlled research. Alpha activity in adults has a mean frequency around 10 Hz with a range between 7 and 13 Hz and has maximum amplitudes at parieto-occipital locations in the eyes-closed condition.
In MDD a consistent finding is an elevated absolute [ 66 , 67 , 68 , 69 , 70 ] or relative alpha power [ 71 , 72 ] at mainly parietal and frontal [ 68 , 73 ] or occipital sites [ 74 ]. The reason that some studies did not find alpha power differences between patients and HCs [ 75 , 76 ] or found decreased relative alpha activity in comparison to other patient groups [ 77 ] might be related to differences of recording periods, where shorter recording periods prevent the differences described above [ 38 , 39 ] in vigilance regulation to occur e.
In addition, there is some evidence that EEG alpha power can predict treatment outcome with low parieto-occipital [ 74 , 78 , 79 ] or lowered frontal alpha power [ 80 ] associated with non-response to antidepressants, although this could not be replicated in the recent multicentre iSPOT-D for depression study in 1, MDD patients [ 81 ]. However, for treatment with repetitive transcranial magnetic stimulation rTMS , the opposite was reported [ 79 , 80 ], maybe related to higher levels of treatment resistance in these rTMS studies.
EEG alpha asymmetry has been investigated as a biomarker for MDD with a decreased alpha power at right frontal sites relative to the left side [ 82 , 83 , 84 , 85 , 86 ], although many studies have failed to replicate these findings [ 81 , 87 , 88 , 89 , 90 , 91 ]. Two studies by the same group investigated the prognostic value of alpha asymmetry in MDD and found conflicting results [ 74 , 93 ]; however, in the iSPOT-D study it was found that frontal alpha asymmetry right frontal alpha dominance was specifically related to response to the selective serotonin reuptake inhibitors escitalopram and sertraline, but not to the serotonin norepinephrine reuptake inhibitor venlafaxine in females only [ 81 ], underscoring the importance of large samples that allow testing for gender- and drug class-specific predictors of treatment outcome.
A slow background rhythm, also called a slow alpha peak frequency, has been consistently found a predictor for non-response to several treatments such as stimulant medication in ADHD [ 94 ], rTMS in depression [ 95 , 96 ] and the antidepressants pirlindol and amitriptyline [ 79 ]; for a review, see Arns [ 97 ].
Several studies have reported elevated slow-wave activity in MDD [ 69 , 98 , 99 , , ], with the focus of this elevated theta activity localized to frontal areas and often to the anterior cingulate cortex ACC [ 68 , , ], though decreased ACC activity in MDD has also been reported [ ] and some studies found no differences between MDD and controls [ , ].