Frontostriatal activation in patients with obsessive-compulsive disorder before and after cognitive behavioral therapy

BACKGROUND

Cognitive behavioral therapy (CBT) with exposure and response prevention (ERP) is the psychotherapeutic treatment of choice for obsessive-compulsive disorder (OCD). However, little is known about the impact of CBT on frontostriatal dysfunctioning, known to be the neuronal correlate of OCD.

METHOD

A probabilistic reversal learning (RL) task probing adaptive strategy switching capabilities was used in 10 unmedicated patients with OCD and 10 healthy controls during an event-related functional magnetic resonance imaging (fMRI) experiment. Patients were scanned before and after intensive CBT, controls twice at comparable intervals.

RESULTS

Strategy change within the RL task involved activity in a broad frontal network in patients and controls. No significant differences between the groups or in group by time interactions were detected in a whole-brain analysis corrected for multiple comparisons. However, a reanalysis with a more lenient threshold revealed decreased responsiveness of the orbitofrontal cortex and right putamen during strategy change before treatment in patients compared with healthy subjects. A group by time effect was found in the caudate nucleus, demonstrating increased activity for patients over the course of time. Patients with greater clinical improvement, reflected by greater reductions in Yale-Brown Obsessive Compulsive Scale (YBOCS) scores, showed more stable activation in the pallidum.

CONCLUSIONS

Although these findings are preliminary and need to be replicated in larger samples, they indicate a possible influence of psychotherapy on brain activity in core regions that have been shown to be directly involved both in acquisition of behavioral rules and stereotypes and in the pathophysiology of OCD, the caudate nucleus and the pallidum.

[New possibilities for automated diagnosis of dementia]

Dementia is underdiagnosed and undertreated in Germany. Automatic diagnosing of dementia based on standard magnetic resonance imaging has the capacity to reduce diagnostic uncertainties. The algorithm learns a disease specific pattern of atrophy from training samples. It is independent from radiological expertise which may be scarce outside specialised centres and can be installed on MRT-machines or desktop PCs. It can also play its part in planning and conducting treatment trials by recruiting a sample with predicted fast future decline. Extension, based e.g. on resting state functional imaging are possible but are further away from clinical routine.

New developments in the diagnosis of dementia

BACKGROUND

The terms "dementia" and "Alzheimer's disease" are often wrongly used as if they were synonyms. Dementia is a clinical syndrome whose main element is memory impairment; it is due to Alzheimer's disease in more than 75% of cases. Alzheimer's disease, on the other hand, is a neuropathological entity that is characterized by a protracted preclinical phase followed by the onset of slowly progressive dementia.

METHODS

We here review relevant literature that we retrieved by a selective Medline search (2005-2009), paying special attention to the early diagnosis of Alzheimer's disease, its clinical manifestations, and its relevance in primary care.

RESULTS

The early clinical manifestations of a dementing illness can be detected in primary care through the use of simple screening tests such as the mini mental state examination, clock drawing tests, and DemTect. A diminished concentration of Abeta-peptide and an increase of (phospho-)tau in the cerebrospinal fluid can suggest the presence of Alzheimer's disease even before the onset of dementia: these substances are components of amyloid plaques and neurofibrillary tangles, which are the characteristic neuropathological lesions of Alzheimer's disease. New types of morphological magnetic resonsance imaging (MRI), and automated analysis of the images obtained, can improve the consistency of radiological assessment over the traditional visual method and thus enable more secure diagnosis.

CONCLUSION

The early, preclinical phase of Alzheimer's disease involves what has been termed mild cognitive impairment and may last as long as five years until the onset of dementia. With the aid of the new biomarkers described here, the likelihood of diagnosing Alzheimer's disease correctly in this phase can be raised above 80%. Early detection of Alzheimer's disease before the onset of dementia provides an opportunity to study potential approaches for secondary prevention, which are now an object of intense clinical research.

Voxel-based morphometry in eating disorders: correlation of psychopathology with grey matter volume

Twenty-nine adult female patients with eating disorders (17 with bulimia nervosa, 12 with restrictive anorexia nervosa) were compared with 18 age-matched female healthy controls, using voxel-based morphometry. Restrictive anorexia nervosa patients showed a decrease of grey matter, particularly affecting the anterior cingulate cortex, frontal operculum, temporoparietal regions and the precuneus. By contrast, patients with bulimia nervosa did not differ from healthy controls. A positive correlation of "drive for thinness" and grey matter volume of the right inferior parietal lobe was found for both eating disorder groups. The strong reduction of grey matter volume in adult patients with restrictive anorexia nervosa is in line with results of adolescent patients. Contrary to other studies, this first voxel-based morphometry report of bulimic patients did not find any structural abnormalities. The inferior parietal cortex is a critical region for sensory integration of body and spatial perception, and the correlation of "drive for thinness" with grey matter volume of this region points to a neural correlate of this core psychopathological feature of eating disorders.

Multivariate models of inter-subject anatomical variability

This paper presents a very selective review of some of the approaches for multivariate modelling of inter-subject variability among brain images. It focusses on applying probabilistic kernel-based pattern recognition approaches to pre-processed anatomical MRI, with the aim of most accurately modelling the difference between populations of subjects. Some of the principles underlying the pattern recognition approaches of Gaussian process classification and regression are briefly described, although the reader is advised to look elsewhere for full implementational details. Kernel pattern recognition methods require matrices that encode the degree of similarity between the images of each pair of subjects. This review focusses on similarity measures derived from the relative shapes of the subjects' brains. Pre-processing is viewed as generative modelling of anatomical variability, and there is a special emphasis on the diffeomorphic image registration framework, which provides a very parsimonious representation of relative shapes. Although the review is largely methodological, excessive mathematical notation is avoided as far as possible, as the paper attempts to convey a more intuitive understanding of various concepts. The paper should be of interest to readers wishing to apply pattern recognition methods to MRI data, with the aim of clinical diagnosis or biomarker development. It also tries to explain that the best models are those that most accurately predict, so similar approaches should also be relevant to basic science. Knowledge of some basic linear algebra and probability theory should make the review easier to follow, although it may still have something to offer to those readers whose mathematics may be more limited.

Predicting clinical scores from magnetic resonance scans in Alzheimer's disease

Machine learning and pattern recognition methods have been used to diagnose Alzheimer's disease (AD) and Mild Cognitive Impairment (MCI) from individual MRI scans. Another application of such methods is to predict clinical scores from individual scans. Using relevance vector regression (RVR), we predicted individuals' performances on established tests from their MRI T1 weighted image in two independent datasets. From Mayo Clinic, 73 probable AD patients and 91 cognitively normal (CN) controls completed the Mini-Mental State Examination (MMSE), Dementia Rating Scale (DRS), and Auditory Verbal Learning Test (AVLT) within 3 months of their scan. Baseline MRI's from the Alzheimer's disease Neuroimaging Initiative (ADNI) comprised the other dataset; 113 AD, 351 MCI, and 122 CN subjects completed the MMSE and Alzheimer's Disease Assessment Scale—Cognitive subtest (ADAS-cog) and 39 AD, 92 MCI, and 32 CN ADNI subjects completed MMSE, ADAS-cog, and AVLT. Predicted and actual clinical scores were highly correlated for the MMSE, DRS, and ADAS-cog tests (P<.0001). Training with one dataset and testing with another demonstrated stability between datasets. DRS, MMSE, and ADAS-Cog correlated better than AVLT with whole brain grey matter changes associated with AD. This result underscores their utility for screening and tracking disease. RVR offers a novel way to measure interactions between structural changes and neuropsychological tests beyond that of univariate methods. In clinical practice, we envision using RVR to aid in diagnosis and predict clinical outcome.

Pitfalls in the use of voxel-based morphometry as a biomarker: examples from huntington disease

BACKGROUND AND PURPOSE

VBM is increasingly used in the study of neurodegeneration, and recently there has been interest in its potential as a biomarker. However, although it is largely "automated," VBM is rarely implemented consistently across studies, and changing user-specified options can alter the results in a way similar to the very biologic differences under investigation.

MATERIALS AND METHODS

This work uses data from patients with HD to demonstrate the effects of several user-specified VBM parameters and analyses: type and level of statistical correction, modulation, smoothing kernel size, adjustment for brain size, subgroup analysis, and software version.

RESULTS

The results demonstrate that changing these options can alter results in a way similar to the biologic differences under investigation.

CONCLUSIONS

If VBM is to be useful clinically or considered for use as a biomarker, there is a need for greater recognition of these issues and more uniformity in its application for the method to be both reproducible and valid.

Irritability in pre-clinical Huntington's disease

Irritability, together with depression and anxiety, form three salient clinical features of pre-symptomatic Huntington's disease (HD). To date, the understanding of irritability in HD suffers from a paucity of experimental data and is largely based on questionnaires or clinical anecdotes. Factor analysis suggests that irritability is related to impulsivity and aggression and is likely to engage the same neuronal circuits as these behaviours, including areas such as medial orbitofrontal cortex (OFC) and amygdala. 16 pre-symptomatic gene carriers (PSCs) and 15 of their companions were asked to indicate the larger of two squares consecutively shown on a screen while undergoing functional magnetic resonance imaging (fMRI). Despite correct identification of the larger square, participants were often told that they or their partner had given the wrong answer. Size differences were subtle to make negative feedback credible but detectable. Although task performance, baseline irritability, and reported task-induced irritation were the same for both groups, fMRI revealed distinct neuronal processing in those who will later develop HD. In controls but not PSCs, task-induced irritation correlated positively with amygdala activation and negatively with OFC activation. Repetitive negative feedback induced greater amygdala activations in controls than PSCs. In addition, the inverse functional coupling between amygdala and OFC was significantly weaker in PSCs compared to controls. Our results argue that normal emotion processing circuits are disrupted in PSCs via attenuated modulation of emotional status by external or internal indicators. At later stages, this dysfunction may increase the risk for developing recognised, HD-associated, psychiatric symptoms such as irritability.

Influence of dopaminergically mediated reward on somatosensory decision-making

This pharmacological fMRI study shows that during reward-based sensory decision-making, dopamine is crucially involved in reward-related modulation of human primary sensory cortex.

Reward-related dopaminergic influences on learning and overt behaviour are well established, but any influence on sensory decision-making is largely unknown. We used functional magnetic resonance imaging (fMRI) while participants judged electric somatosensory stimuli on one hand or other, before being rewarded for correct performance at trial end via a visual signal, at one of four anticipated financial levels. Prior to the procedure, participants received either placebo (saline), a dopamine agonist (levodopa), or an antagonist (haloperidol). Principal findings: higher anticipated reward improved tactile decisions. Visually signalled reward reactivated primary somatosensory cortex for the judged hand, more strongly for higher reward. After receiving a higher reward on one trial, somatosensory activations and decisions were enhanced on the next trial. These behavioural and neural effects were all enhanced by levodopa and attenuated by haloperidol, indicating dopaminergic dependency. Dopaminergic reward-related influences extend even to early somatosensory cortex and sensory decision-making.

The rewards one receives during decision-making has a profound impact on learning. Much recent interest has focused on the role of the neurotransmitter dopamine in the basal ganglia for influencing learning and behaviour. Here, we ask whether reward can influence low-level sensory processing, for instance in primary sensory cortex, and how dopamine mediates this process. We show in humans that dopamine level, as manipulated with a dopamine agonist and antagonist in a double-blind placebo-controlled design, is involved in reward modulation of primary somatosensory cortex. Higher anticipated reward improved tactile decisions, and receipt of visual reward signals reactivated primary somatosensory cortex for the judged hand as measured using functional neuroimaging. After receiving a higher reward on one trial, somatosensory activations and decisions were enhanced on the next trial, suggesting that reward outcome provides a form of teaching signal that may be fed back to task-relevant sensory cortex. All these behavioural and neural effects of reward on somatosensory decision-making were strongly modulated by the availability of dopamine as the mediating neurotransmitter. These findings raise the tantalising new possibility that reward manipulations in conjunction with dopaminergic drugs might be used to enhance pathologically deficient or lapsed sensory processes, analogous to how rewards can be used to shape or correct behaviour.

Functional compensation of motor function in pre-symptomatic Huntington's disease

Involuntary choreiform movements are a clinical hallmark of Huntington's disease. Studies in clinically affected patients suggest a shift of motor activations to parietal cortices in response to progressive neurodegeneration. Here, we studied pre-symptomatic gene carriers to examine the compensatory mechanisms that underlie the phenomenon of retained motor function in the presence of degenerative change. Fifteen pre-symptomatic gene carriers and 12 matched controls performed button presses paced by a metronome at either 0.5 or 2 Hz with four fingers of the right hand whilst being scanned with functional magnetic resonance imaging. Subjects pressed buttons either in the order of a previously learnt 10-item finger sequence, from left to right, or kept still. Error rates ranged from 2% to 7% in the pre-symptomatic gene carriers and from 0.5% to 4% in controls, depending on the condition. No significant difference in task performance was found between groups for any of the conditions. Activations in the supplementary motor area (SMA) and superior parietal lobe differed with gene status. Compared with healthy controls, gene carriers showed greater activations of left caudal SMA with all movement conditions. Activations correlated with increasing speed of movement were greater the closer the gene carriers were to estimated clinical diagnosis, defined by the onset of unequivocal motor signs. Activations associated with increased movement complexity (i.e. with the pre-learnt 10-item sequence) decreased in the rostral SMA with nearing diagnostic onset. The left superior parietal lobe showed reduced activation with increased movement complexity in gene carriers compared with controls, and in the right superior parietal lobe showed greater activations with all but the most demanding movements. We identified a complex pattern of motor compensation in pre-symptomatic gene carriers. The results show that preclinical compensation goes beyond a simple shift of activity from premotor to parietal regions involving multiple compensatory mechanisms in executive and cognitive motor areas. Critically, the pattern of motor compensation is flexible depending on the actual task demands on motor control.

Consensus paper: combining transcranial stimulation with neuroimaging

In the last decade, combined transcranial magnetic stimulation (TMS)-neuroimaging studies have greatly stimulated research in the field of TMS and neuroimaging. Here, we review how TMS can be combined with various neuroimaging techniques to investigate human brain function. When applied during neuroimaging (online approach), TMS can be used to test how focal cortex stimulation acutely modifies the activity and connectivity in the stimulated neuronal circuits. TMS and neuroimaging can also be separated in time (offline approach). A conditioning session of repetitive TMS (rTMS) may be used to induce rapid reorganization in functional brain networks. The temporospatial patterns of TMS-induced reorganization can be subsequently mapped by using neuroimaging methods. Alternatively, neuroimaging may be performed first to localize brain areas that are involved in a given task. The temporospatial information obtained by neuroimaging can be used to define the optimal site and time point of stimulation in a subsequent experiment in which TMS is used to probe the functional contribution of the stimulated area to a specific task. In this review, we first address some general methodologic issues that need to be taken into account when using TMS in the context of neuroimaging. We then discuss the use of specific brain mapping techniques in conjunction with TMS. We emphasize that the various neuroimaging techniques offer complementary information and have different methodologic strengths and weaknesses.

Genotype-phenotype interactions in primary dystonias revealed by differential changes in brain structure

Our understanding of how genotype determines phenotype in primary dystonia is limited. Familial young-onset primary dystonia is commonly due to the DYT1 gene mutation. A critical question, given the 30% penetrance of clinical symptoms in DYT1 mutation carriers, is why the same genotype leads to differential clinical expression and whether non-DYT1 adult-onset primary dystonia, with and without family history share pathophysiological mechanisms with DYT1 dystonia. This study examines the relationship between dystonic phenotype and the DYT1 gene mutation by monitoring whole-brain structure using voxel-based morphometry. We acquired magnetic resonance imaging data of symptomatic and asymptomatic DYT1 mutation carriers, of non-DYT1 primary dystonia patients, with and without family history and control subjects with normal DYT1 alleles. By crossing the factors genotype and phenotype we demonstrate a significant interaction in terms of brain anatomy confined to the basal ganglia bilaterally. The explanation for this effect differs according to both gene and dystonia status: non-DYT1 adult-onset dystonia patients and asymptomatic DYT1 carriers have significantly larger basal ganglia compared to healthy subjects and symptomatic DYT1 mutation carriers. There is a significant negative correlation between severity of dystonia and basal ganglia size in DYT1 mutation carriers. We propose that differential pathophysiological and compensatory mechanisms lead to brain structure changes in non-DYT1 primary adult-onset dystonias and DYT1 gene carriers. Given the range of age of onset, there may be differential genetic modulation of brain development that in turn determines clinical expression. Alternatively, a DYT1 gene dependent primary defect of motor circuit development may lead to stress-induced remodelling of the basal ganglia and hence dystonia.

Automatic detection of preclinical neurodegeneration: presymptomatic Huntington disease

BACKGROUND

Treatment of neurodegenerative diseases is likely to be most beneficial in the very early, possibly preclinical stages of degeneration. We explored the usefulness of fully automatic structural MRI classification methods for detecting subtle degenerative change. The availability of a definitive genetic test for Huntington disease (HD) provides an excellent metric for judging the performance of such methods in gene mutation carriers who are free of symptoms.

METHODS

Using the gray matter segment of MRI scans, this study explored the usefulness of a multivariate support vector machine to automatically identify presymptomatic HD gene mutation carriers (PSCs) in the absence of any a priori information. A multicenter data set of 96 PSCs and 95 age- and sex-matched controls was studied. The PSC group was subclassified into three groups based on time from predicted clinical onset, an estimate that is a function of DNA mutation size and age.

RESULTS

Subjects with at least a 33% chance of developing unequivocal signs of HD in 5 years were correctly assigned to the PSC group 69% of the time. Accuracy improved to 83% when regions affected by the disease were selected a priori for analysis. Performance was at chance when the probability of developing symptoms in 5 years was less than 10%.

CONCLUSIONS

Presymptomatic Huntington disease gene mutation carriers close to estimated diagnostic onset were successfully separated from controls on the basis of single anatomic scans, without additional a priori information. Prior information is required to allow separation when degenerative changes are either subtle or variable.

Magnetic resonance imaging of Huntington's disease: preparing for clinical trials

The known genetic mutation causing Huntington's disease (HD) makes this disease an important model to study links between gene and brain function. An autosomal dominant family history and the availability of a sensitive and specific genetic test allow pre-clinical diagnosis many years before the onset of any typical clinical signs. This review summarizes recent magnetic resonance imaging (MRI)–based findings in HD with a focus on the requirements if imaging is to be used in treatment trials. Despite its monogenetic cause, HD presents with a range of clinical manifestations, not explained by variation in the number of CAG repeats in the affected population. Neuroimaging studies have revealed a complex pattern of structural and functional changes affecting widespread cortical and subcortical regions far beyond the confines of the striatal degeneration that characterizes this disorder. Besides striatal dysfunction, functional imaging studies have reported a variable pattern of increased and decreased activation in cortical regions in both pre-clinical and clinically manifest HD-gene mutation carriers. Beyond regional brain activation changes, evidence from functional and diffusion-weighted MRI further suggests disrupted connectivity between corticocortical and corticostriatal areas. However, substantial inconsistencies with respect to structural and functional changes have been reported in a number of studies. Possible explanations include methodological factors and differences in study samples. There may also be biological explanations but these are poorly characterized and understood at present. Additional insights into this phenotypic variability derived from study of mouse models are presented to explore this phenomenon.

The NMDA agonist D-cycloserine facilitates fear memory consolidation in humans

Animal research suggests that the consolidation of fear and extinction memories depends on N-methyl D-aspartate (NMDA)-type glutamate receptors. Using a fear conditioning and extinction paradigm in healthy normal volunteers, we show that postlearning administration of the NMDA partial agonist D-cycloserine (DCS) facilitates fear memory consolidation, evidenced behaviorally by enhanced skin conductance responses, relative to placebo, for presentations of a conditioned stimulus (CS) at a memory test performed 72 h later. DCS also enhanced CS-evoked neural responses in a posterior hippocampus/collateral sulcus region and in the medial prefrontal cortex at test. Our data suggest a role for NMDA receptors in regulating fear memory consolidation in humans.

Accuracy of dementia diagnosis: a direct comparison between radiologists and a computerized method

There has been recent interest in the application of machine learning techniques to neuroimaging-based diagnosis. These methods promise fully automated, standard PC-based clinical decisions, unbiased by variable radiological expertise. We recently used support vector machines (SVMs) to separate sporadic Alzheimer's disease from normal ageing and from fronto-temporal lobar degeneration (FTLD). In this study, we compare the results to those obtained by radiologists. A binary diagnostic classification was made by six radiologists with different levels of experience on the same scans and information that had been previously analysed with SVM. SVMs correctly classified 95% (sensitivity/specificity: 95/95) of sporadic Alzheimer's disease and controls into their respective groups. Radiologists correctly classified 65-95% (median 89%; sensitivity/specificity: 88/90) of scans. SVM correctly classified another set of sporadic Alzheimer's disease in 93% (sensitivity/specificity: 100/86) of cases, whereas radiologists ranged between 80% and 90% (median 83%; sensitivity/specificity: 80/85). SVMs were better at separating patients with sporadic Alzheimer's disease from those with FTLD (SVM 89%; sensitivity/specificity: 83/95; compared to radiological range from 63% to 83%; median 71%; sensitivity/specificity: 64/76). Radiologists were always accurate when they reported a high degree of diagnostic confidence. The results show that well-trained neuroradiologists classify typical Alzheimer's disease-associated scans comparable to SVMs. However, SVMs require no expert knowledge and trained SVMs can readily be exchanged between centres for use in diagnostic classification. These results are encouraging and indicate a role for computerized diagnostic methods in clinical practice.