MRI-based prediction of conversion from clinically isolated syndrome to clinically definite multiple sclerosis using SVM and lesion geometry.

Neuroanatomical pattern classification using support vector machines (SVMs) has shown promising results in classifying Multiple Sclerosis (MS) patients based on individual structural magnetic resonance images (MRI). To determine whether pattern classification using SVMs facilitates predicting conversion to clinically definite multiple sclerosis (CDMS) from clinically isolated syndrome (CIS). We used baseline MRI data from 364 patients with CIS, randomised to interferon beta-1b or placebo. Non-linear SVMs and 10-fold cross-validation were applied to predict converters/non-converters (175/189) at two years follow-up based on clinical and demographic data, lesion-specific quantitative geometric features and grey-matter-to-whole-brain volume ratios. We applied linear SVM analysis and leave-one-out cross-validation to subgroups of converters (n = 25) and non-converters (n = 44) based on cortical grey matter segmentations. Highest prediction accuracies of 70.4% (p = 8e-5) were reached with a combination of lesion-specific geometric (image-based) and demographic/clinical features. Cortical grey matter was informative for the placebo group (acc.: 64.6%, p = 0.002) but not for the interferon group. Classification based on demographic/clinical covariates only resulted in an accuracy of 56% (p = 0.05). Overall, lesion geometry was more informative in the interferon group, EDSS and sex were more important for the placebo cohort. Alongside standard demographic and clinical measures, both lesion geometry and grey matter based information can aid prediction of conversion to CDMS.

Urgent challenges in quantification and interpretation of brain grey matter atrophy in individual MS patients using MRI.

Atrophy of the brain grey matter (GM) is an accepted and important feature of multiple sclerosis (MS). However, its accurate measurement is hampered by various technical, pathological and physiological factors. As a consequence, it is challenging to investigate the role of GM atrophy in the disease process as well as the effect of treatments that aim to reduce neurodegeneration. In this paper we discuss the most important challenges currently hampering the measurement and interpretation of GM atrophy in MS. The focus is on measurements that are obtained in individual patients rather than on group analysis methods, because of their importance in clinical trials and ultimately in clinical care. We discuss the sources and possible solutions of the current challenges, and provide recommendations to achieve reliable measurement and interpretation of brain GM atrophy in MS.

Hippocampal volume in subjects at clinical high-risk for psychosis: A systematic review and meta-analysis.

Several magnetic resonance imaging studies have reported reductions in hippocampal volume in patients with psychosis. It is unclear whether structural abnormalities predate illness onset. We conducted a detailed, systematic literature search for studies reporting hippocampal volume in subjects with clinical high-risk, compared to healthy controls. The overall sample size comprised 1429 subjects. Meta-analysis revealed no difference for left, but a small, albeit significant, difference for right hippocampal volume, such that clinical high-risk patients had slightly smaller hippocampal volume than healthy controls (g=0.24, p=0.0418). Meta-regression indicated a moderating effect of manual tracing approach, due to one outlying site. The small difference on the right side did not remain significant (g=0.14, 95%CI=[-0.03-0.32], p=0.11) after removal of this outlier. This meta-analysis suggests that there is no reduction in hippocampal volume before transition to psychosis and hippocampal volume cannot be used as a biomarker in clinical high-risk individuals.

Alterations in the hippocampus and thalamus in individuals at high risk for psychosis.

Reduction in hippocampal volume is a hallmark of schizophrenia and already present in the clinical high-risk state. Nevertheless, other subcortical structures, such as the thalamus, amygdala and pallidum can differentiate schizophrenia patients from controls. We studied the role of hippocampal and subcortical structures in clinical high-risk individuals from two cohorts. High-resolution T1-weighted structural MRI brain scans of a total of 91 clinical high-risk individuals and 64 healthy controls were collected in two centers. The bilateral volume of the hippocampus, the thalamus, the caudate, the putamen, the pallidum, the amygdala, and the accumbens were automatically segmented using FSL-FIRST. A linear mixed-effects model and a prospective meta-analysis were applied to assess group-related volumetric differences. We report reduced hippocampal and thalamic volumes in clinical high-risk individuals compared to healthy controls. No volumetric alterations were detected for the caudate, the putamen, the pallidum, the amygdala, or the accumbens. Moreover, we found comparable medium effect sizes for group-related comparison of the thalamus in the two analytical methods. These findings underline the relevance of specific alterations in the hippocampal and subcortical volumes in the high-risk state. Further analyses may allow hippocampal and thalamic volumes to be used as biomarkers to predict psychosis.

Classifying individuals at high-risk for psychosis based on functional brain activity during working memory processing.

The psychosis high-risk state is accompanied by alterations in functional brain activity during working memory processing. We used binary automatic pattern-classification to discriminate between the at-risk mental state (ARMS), first episode psychosis (FEP) and healthy controls (HCs) based on n-back WM-induced brain activity. Linear support vector machines and leave-one-out-cross-validation were applied to fMRI data of matched ARMS, FEP and HC (19 subjects/group). The HC and ARMS were correctly classified, with an accuracy of 76.2% (sensitivity 89.5%, specificity 63.2%, p = 0.01) using a verbal working memory network mask. Only 50% and 47.4% of individuals were classified correctly for HC vs. FEP (p = 0.46) or ARMS vs. FEP (p = 0.62), respectively. Without mask, accuracy was 65.8% for HC vs. ARMS (p = 0.03) and 65.8% for HC vs. FEP (p = 0.0047), and 57.9% for ARMS vs. FEP (p = 0.18). Regions in the medial frontal, paracingulate, cingulate, inferior frontal and superior frontal gyri, inferior and superior parietal lobules, and precuneus were particularly important for group separation. These results suggest that FEP and HC or FEP and ARMS cannot be accurately separated in small samples under these conditions. However, ARMS can be identified with very high sensitivity in comparison to HC. This might aid classification and help to predict transition in the ARMS.

Age of second language acquisition in multilinguals has an impact on gray matter volume in language-associated brain areas.

Numerous structural studies have established that experience shapes and reshapes the brain throughout a lifetime. The impact of early development, however, is still a matter of debate. Further clues may come from studying multilinguals who acquired their second language at different ages. We investigated adult multilinguals who spoke three languages fluently, where the third language was learned in classroom settings, not before the age of 9 years. Multilinguals exposed to two languages simultaneously from birth (SiM) were contrasted with multinguals who acquired their first two languages successively (SuM). Whole brain voxel based morphometry revealed that, relative to SuM, SiM have significantly lower gray matter volume in several language-associated cortical areas in both hemispheres: bilaterally in medial and inferior frontal gyrus, in the right medial temporal gyrus and inferior posterior parietal gyrus, as well as in the left inferior temporal gyrus. Thus, as shown by others, successive language learning increases the volume of language-associated cortical areas. In brains exposed early on and simultaneously to more than one language, however, learning of additional languages seems to have less impact. We conclude that - at least with respect to language acquisition - early developmental influences are maintained and have an effect on experience-dependent plasticity well into adulthood.

Modulation of motivational salience processing during the early stages of psychosis.

BACKGROUND: Deficits in motivational salience processing have been related to psychotic symptoms and disturbances in dopaminergic neurotransmission. We aimed at exploring changes in salience processing and brain activity during different stages of psychosis and antipsychotic medication effect. METHODS: We used fMRI during the Salience Attribution Task to investigate hemodynamic differences between 19 healthy controls (HCs), 34 at-risk mental state (ARMS) individuals and 29 individuals with first-episode psychosis (FEP), including a subgroup of 17 FEP without antipsychotic medication (FEP-UM) and 12 FEP with antipsychotic medication (FEP-M). Motivational salience processing was operationalized by brain activity in response to high-probability rewarding cues (adaptive salience) and in response to low-probability rewarding cues (aberrant salience). RESULTS: Behaviorally, adaptive salience response was not accelerated in FEP, although they correctly distinguished between trials with low and high reward probability. In comparison to HC, ARMS exhibited a lower hemodynamic response during adaptive salience in the right inferior parietal lobule and FEP-UM in the left dorsal cingulate gyrus. The FEP-M group exhibited a lower adaptive salience response than HC in the right insula and than ARMS in the anterior cingulate gyrus. In unmedicated individuals, the severity of hallucinations and delusions correlated negatively with the insular- and anterior cingulate hemodynamic response during adaptive salience. We found no differences in aberrant salience processing associated with behavior or medication. CONCLUSION: The changes in adaptive motivational salience processing during psychosis development reveal neurofunctional abnormalities in the somatosensory and premotor cortex. Antipsychotic medication seems to modify hemodynamic responses in the anterior cingulate and insula.

Subcortical brain segmentation of two dimensional T1-weighted data sets with FMRIB's Integrated Registration and Segmentation Tool (FIRST).

Brain atrophy has been identified as an important contributing factor to the development of disability in multiple sclerosis (MS). In this respect, more and more interest is focussing on the role of deep grey matter (DGM) areas. Novel data analysis pipelines are available for the automatic segmentation of DGM using three-dimensional (3D) MRI data. However, in clinical trials, often no such high-resolution data are acquired and hence no conclusions regarding the impact of new treatments on DGM atrophy were possible so far. In this work, we used FMRIB's Integrated Registration and Segmentation Tool (FIRST) to evaluate the possibility of segmenting DGM structures using standard two-dimensional (2D) T1-weighted MRI. In a cohort of 70 MS patients, both 2D and 3D T1-weighted data were acquired. The thalamus, putamen, pallidum, nucleus accumbens, and caudate nucleus were bilaterally segmented using FIRST. Volumes were calculated for each structure and for the sum of basal ganglia (BG) as well as for the total DGM. The accuracy and reliability of the 2D data segmentation were compared with the respective results of 3D segmentations using volume difference, volume overlap and intra-class correlation coefficients (ICCs). The mean differences for the individual substructures were between 1.3% (putamen) and -25.2% (nucleus accumbens). The respective values for the BG were -2.7% and for DGM 1.3%. Mean volume overlap was between 89.1% (thalamus) and 61.5% (nucleus accumbens); BG: 84.1%; DGM: 86.3%. Regarding ICC, all structures showed good agreement with the exception of the nucleus accumbens. The results of the segmentation were additionally validated through expert manual delineation of the caudate nucleus and putamen in a subset of the 3D data. In conclusion, we demonstrate that subcortical segmentation of 2D data are feasible using FIRST. The larger subcortical GM structures can be segmented with high consistency. This forms the basis for the application of FIRST in large 2D MRI data sets of clinical trials in order to determine the impact of therapeutic interventions on DGM atrophy in MS.

Spatial modeling of multiple sclerosis for disease subtype prediction.

Magnetic resonance imaging (MRI) has become an essential tool in the diagnosis and managing of Multiple Sclerosis (MS). Currently, the assessment of MS is based on a combination of clinical scores and subjective rating of lesion images by clinicians. In this work we present an objective 5-way classification of MS disease subtype as well as a comparison between three different approaches. First we propose two spatially informed models, a Bayesian Spatial Generalized Linear Mixed Model (BSGLMM) and a Log Gaussian Cox Process (LGCP). The BSGLMM accounts for the binary nature of lesion maps and the spatial dependence between neighboring voxels, and the LGCP accounts for the random spatial variation in lesion location. Both improve upon mass univariate analyses that ignore spatial dependence and rely on some level of arbitrarily defined smoothing of the data. As a comparison, we consider a machine learning approach based on multi-class support vector machine (SVM). For the SVM classification scheme, unlike previous work, we use a large number of quantitative features derived from three MRI sequences in addition to traditional demographic and clinical measures. We show that the spatial models outperform standard approaches with average prediction accuracies of up to 85%.

Association of cognitive impairment and lesion volumes in multiple sclerosis--a MRI study.

UNLABELLED: Cognitive impairment (CI) can be demonstrated in 40-65% of multiple sclerosis (MS) patients, sometimes starting from the early stages of the disease. The objective of this study was a community-based investigation of FLAIR-hyperintense lesion volumes (LV) and their association with CI in patients with relapsing remitting (RR) MS. The neurocognitive assessment was conducted with the brief cognitive screening instrument, MUSIC. Magnetic resonance imaging (MRI) scans were obtained with a 1.5 Tesla Sigma Magnetom MRI scanner. We conducted a stepwise multiple regression analysis to assess the relative contribution of the main clinical, demographic and MRI-variables in predicting cognitive impairment. We recruited 78 patients with RRMS. The mean MUSIC score was 20.6 ± 5.4. Forty five percent of patients (n = 35, mean score 15.1 ± 3.3) had CI and 55% (n = 43, mean score 24.4 ± 2.5) had no sign of CI. In the correlation analysis of the MUSIC subtests only the interference test correlated negatively with the LV (r = -0.23). Multivariate linear regression analysis using MUSIC as the dependent continuous variable revealed LV and disability severity as independent factors associated with MUSIC (p value of the regression model < 0.001; adjusted R-square = 0.11). The results of the present study suggest an association between white matter damage and CI in MS. We could demonstrate an association between attention difficulties and the LV in MS patients. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT01250665 and NCT01250678.

ANALYSIS OF MULTIPLE SCLEROSIS LESIONS VIA SPATIALLY VARYING COEFFICIENTS.

Magnetic resonance imaging (MRI) plays a vital role in the scientific investigation and clinical management of multiple sclerosis. Analyses of binary multiple sclerosis lesion maps are typically "mass univariate" and conducted with standard linear models that are ill suited to the binary nature of the data and ignore the spatial dependence between nearby voxels (volume elements). Smoothing the lesion maps does not entirely eliminate the non-Gaussian nature of the data and requires an arbitrary choice of the smoothing parameter. Here we present a Bayesian spatial model to accurately model binary lesion maps and to determine if there is spatial dependence between lesion location and subject specific covariates such as MS subtype, age, gender, disease duration and disease severity measures. We apply our model to binary lesion maps derived from T2-weighted MRI images from 250 multiple sclerosis patients classified into five clinical subtypes, and demonstrate unique modeling and predictive capabilities over existing methods.

Altered prefrontal connectivity after acute heroin administration during cognitive control.

Neuroimaging studies have reported reduced activity in a broad network of brain regions during response inhibition in heroin-dependent patients. However, how heroin in an acute dose modulates the neural correlates of response inhibition and the underlying brain connectivity has not yet been investigated. In this double-blind placebo-controlled study, we used functional magnetic resonance imaging to examine whether acute heroin administration changed whole brain activity during response inhibition in 26 heroin-dependent patients. We then applied dynamic causal modelling to investigate the effect of an acute dose of heroin on the functional interactions between the dorsal anterior cingulate cortex (dACC) and the bilateral inferior frontal gyri (IFG). Heroin acutely reduced dACC activity, as well as the inhibition-induced modulation of connectivity from the dACC to the right IFG compared with placebo. Furthermore, dACC activity was positively related to false alarm rates after placebo but not heroin administration. These results suggest that acute heroin administration impairs cognitive control in dependent patients by reducing the activity in the dACC activity and the functional connectivity from the dACC to the right IFG.

Abnormal effective connectivity and psychopathological symptoms in the psychosis high-risk state.

BACKGROUND: Recent evidence has revealed abnormal functional connectivity between the frontal and parietal brain regions during working memory processing in patients with schizophrenia and first-episode psychosis. However, it still remains unclear whether abnormal frontoparietal connectivity during working memory processing is already evident in the psychosis high-risk state and whether the connection strengths are related to psychopathological outcomes. METHODS: Healthy controls and antipsychotic-naive individuals with an at-risk mental state (ARMS) performed an n-back working memory task while undergoing functional magnetic resonance imaging. Effective connectivity between frontal and parietal brain regions during working memory processing were characterized using dynamic causal modelling. RESULTS: Our study included 19 controls and 27 individuals with an ARMS. In individuals with an ARMS, we found significantly lower task performances and reduced activity in the right superior parietal lobule and middle frontal gyrus than in controls. Furthermore, the working memory-induced modulation of the connectivity from the right middle frontal gyrus to the right superior parietal lobule was significantly reduced in individuals with an ARMS, while the extent of this connectivity was negatively related to the Brief Psychiatric Rating Scale total score. LIMITATIONS: The modest sample size precludes a meaningful subgroup analysis for participants with a later transition to psychosis. CONCLUSION: This study demonstrates that abnormal frontoparietal connectivity during working memory processing is already evident in individuals with an ARMS and is related to psychiatric symptoms. Thus, our results provide further insight into the pathophysiological mechanisms of the psychosis high-risk state by linking functional brain imaging, computational modelling and psychopathology.

Effects of cannabis on impulsivity: a systematic review of neuroimaging findings.

We conducted a systematic review to assess the evidence for specific effects of cannabis on impulsivity, disinhibition and motor control. The review had a specific focus on neuroimaging findings associated with acute and chronic use of the drug and covers literature published up until May 2012. Seventeen studies were identified, of which 13 met the inclusion criteria; three studies investigated acute effects of cannabis (1 fMRI, 2 PET), while six studies investigated non-acute functional effects (4 fMRI, 2 PET), and four studies investigated structural alterations. Functional imaging studies of impulsivity studies suggest that prefrontal blood flow is lower in chronic cannabis users than in controls. Studies of acute administration of THC or marijuana report increased brain metabolism in several brain regions during impulsivity tasks. Structural imaging studies of cannabis users found differences in reduced prefrontal volumes and white matter integrity that might mediate the abnormal impulsivity and mood observed in marijuana users. To address the question whether impulsivity as a trait precedes cannabis consumption or whether cannabis aggravates impulsivity and discontinuation of usage more longitudinal study designs are warranted.

Progression in disability and regional grey matter atrophy in relapsing-remitting multiple sclerosis.

BACKGROUND: In multiple sclerosis (MS) regional grey matter (GM) atrophy has been associated with disability progression. OBJECTIVE: The aim of this study was to compare regional GM volume changes in relapsing-remitting MS (RRMS) patients with progressive and stable disability, using voxel-based morphometry (VBM). METHODS: We acquired baseline and 1-year follow-up 3-dimensional (3D) T1-weighted magnetic resonance imaging (MRI) data of RRMS patients, using two 1.5-Tesla scanners. Patients were matched pair-wise with respect to age, gender, disease duration, medication, scanner and baseline Expanded Disability Status Scale (EDSS) into 13 pairs, with either progressive EDSS (≥ 1 point change y(-1)) or stable EDSS, as well as into 29 pairs with either progressive Multiple Sclerosis Functional Composite (MSFC) at ≥ 0.25% decrease in y(-1) in any component, or stable MSFC. We analysed longitudinal regional differences in GM volumes in the progressive and stable EDSS and MSFC groups, respectively, using VBM. RESULTS: Significant GM volume reductions occurred in the right precuneus, in the progressive EDSS group. Differential between-group effects occurred in the right precuneus and in the postcentral gyrus. Further longitudinal GM volume reductions occurred in the right orbicular gyrus, in the progressive MSFC group, but no between-group differences were observed (non-stationary cluster-wise inference, all P(corrected) < 0.05). CONCLUSION: These results suggested a direct association of disability progression and regional GM atrophy in RRMS.

Brain atrophy: an in-vivo measure of disease activity in multiple sclerosis.

Multiple sclerosis (MS) has traditionally been considered to be primarily an inflammatory demyelinating disorder. Nowadays it is recognised as both an inflammatory and a neurodegenerative condition. This recognition is reflected in the development of new disease-modifying therapies that may offer the potential to reduce axon damage, either by inhibiting neurodegeneration or by promoting endogenous repair mechanisms. Since there is only a limited correlation between the clinical features of MS and findings on conventional magnetic resonance imaging (MRI), for the evaluation of such therapies new outcome measures are warranted. Grey matter atrophy occurs in the earliest stages of MS, progresses faster than in healthy individuals, and shows significant correlations with MRI lesion load, cognitive function and measures of physical disability; indeed, brain atrophy is the best predictor of subsequent disability and can be readily measured using MRI. Furthermore, it is becoming clear that currently available therapies differ in their effects on brain atrophy, and this may have important implications for the management of MS. New MRI techniques and advances in software development offer an opportunity to extend brain atrophy measurements beyond research studies to the routine management of MS patients.

Global N-acetylaspartate concentration in benign and non-benign multiple sclerosis patients of long disease duration.

BACKGROUND AND OBJECTIVE: To examine whether clinically benign multiple sclerosis patients (BMS) show similar losses of their global N-acetylaspartate (NAA) neuronal marker relative to more clinically disabled patients of similar disease duration. METHODS: The whole-brain NAA concentration (WBNAA) was acquired with whole-head non-localizing proton MR spectroscopy. Fractional brain parenchymal volume (fBPV), T2 and T1 lesion loads, were obtained from the MRI in: (i) 24 BMS patients: 23.1 ± 7.2 years disease duration, median Expanded Disability Status Scale (EDSS) score of 2.0 (range: 0-3); (ii) 26 non-benign MS patients (non-BMS), 24.5 ± 7.4 years disease duration, median EDSS of 4.0 (range: 3.5-6.5); (iii) 15 healthy controls. RESULTS: Controls' 12.4 ± 2.3mM WBNAA was significantly higher than the BMS's and non-BMS's 10.5 ± 2.4 and 9.9 ± 2.1mM (both p<0.02), but the difference between the patients' groups was not (p>0.4). Likewise, the controls' 81.2 ± 4.5% fBPV exceeded the BMS and non-BMS's 77.0 ± 5.8% and 76.3 ± 8.6% (p<0.03), which were also not different from one another (p>0.7). BMS patients' T1-hypointense lesion load, 2.1 ± 2.2 cm(3), was not significantly different than the non-BMS's 4.1 ± 5.4 cm(3) (p>0.08) and T2-hyperintense loads: 6.0 ± 5.7 cm(3) and 8.7 ± 7.8 cm(3), were also not different (p>0.1). CONCLUSIONS: WBNAA differentiates normal controls from MS patients but does not distinguish BMS from more disabled MS patients of similar disease duration. Nevertheless, all MS patients who remain RR for 15+ years suffered WBNAA loss similar to the average RR MS population at fourfold shorter disease duration suggesting relative global neuronal sparing or leveling-off of the neurodegeneration rate.

Relevance of spinal cord abnormalities to clinical disability in multiple sclerosis: MR imaging findings in a large cohort of patients.

PURPOSE: To determine whether spinal cord atrophy differs among disease subtypes in multiple sclerosis (MS) and whether it offers diagnostic and clinical correlative information beyond that provided by other magnetic resonance (MR) imaging markers. MATERIALS AND METHODS: The institutional review board approved the study; all subjects gave written informed consent. Upper cervical cord cross-sectional area (UCCA), brain and spinal cord lesion loads, and brain atrophy were measured in 440 patients with MS (311 with relapsing-remitting [RR] MS, 92 with secondary-progressive [SP] MS, and 37 with primary-progressive [PP] MS) studied in two centers. Disability was scored with the Expanded Disability Status Scale (EDSS), the timed 25-foot walk test (TWT), and the nine-hole peg test. UCCA was compared between groups with the Mann-Whitney U test. Correlations were assessed with the Spearman ρ test. Multivariate associations between UCCA and clinical and other MR imaging parameters, including number of hypointense brain lesions on T1-weighted MR images, presence of diffuse abnormalities, and number of involved segments in the spinal cord, were assessed by using multiple linear regression, adjusted for study center site. RESULTS: The UCCA in patients with SP MS (median, 79 mm(2); interquartile range, 72.4-84.9 mm(2)) and PP MS (median, 77.3 mm(2); interquartile range, 69-82.5 mm(2)) was significantly smaller (P < .001) than that in patients with RR MS (median, 84 mm(2); interquartile range, 78.7-89.3 mm(2)). UCCA was inversely correlated with EDSS score, TWT, and nine-hole peg test findings (ρ ≤ -0.29, P < .001 for all comparisons). UCCA, number of hypointense brain lesions on T1-weighted MR images, presence of diffuse abnormalities, and number of involved segments in the spinal cord were found to be significant explanatory factors for clinical disability (R(2) = 0.564). The UCCA and the number of hypointense brain lesions on T1-weighted images were the strongest MR imaging parameters for explaining physical disability, as measured with the EDSS. CONCLUSION: Spinal cord abnormalities have a strong effect on clinical disability in MS. MR imaging-derived UCCA was found to be the most significant spinal cord parameter for explaining EDSS score.

Inferior frontal cortex modulation with an acute dose of heroin during cognitive control.

Impairments in inhibitory control and in stimulus-driven attention are hallmarks of drug addiction and are associated with decreased activation in the right inferior frontal gyrus (IFG). Although previous studies indicate that the response inhibition function is impaired in abstinent heroin dependents, and that this is mediated by reduced IFG activity, it remains completely unknown whether and how an acute dose of heroin modulates IFG activity during cognitive control in heroin-dependent patients. This study investigates the acute effects of heroin administration on IFG activity during response inhibition and stimulus-driven attention in heroin-dependent patients. Using a cross-over, double-blind, placebo-controlled design, saline and heroin were administered to 26 heroin-dependent patients from stable heroin-assisted treatment, while performing a Go/No-Go event-related functional magnetic resonance imaging task to assess right IFG activity during motor response inhibition, as well as during oddball-driven attention allocation. Relative to saline, heroin significantly reduced right IFG activity during both successful response inhibition and oddball-driven attention allocation, whereas it did not change right IFG activity during response inhibition after correction for the effect of attention allocation. These heroin-induced effects were not related to changes in drug craving, state anxiety, behavioral performance, or co-consumption of psychostimulant drugs. This study demonstrates that heroin administration acutely impairs stimulus-driven attention allocation, as indicated by reduced IFG activity in response to infrequently presented stimuli, and does not specifically modulate IFG activity during response inhibition.

Contribution of cortical and white matter lesions to cognitive impairment in multiple sclerosis.

BACKGROUND: Cortical lesions (CLs) have been reported to be a better predictor for cognitive impairment than white matter (WM) lesions in relapsing-remitting multiple sclerosis (RRMS). OBJECTIVES: The objectives of this article are to investigate the contribution of CLs and WM lesions to cognitive impairment in 91 patients with MS and clinically isolated syndrome, and to test potential associations of CLs and WM lesions with fatigue and depression. METHODS: Lesions were scored and segmented on 3D double inversion recovery sequences, according to their location (cortical, WM). Normalised grey matter volume was also determined. Cognitive performance was assessed with the SDMT and PASAT-3, fatigue with the FSMC and depression with the German version of the CES-D. RESULTS: CL volume did not correlate with fatigue or depression, but correlated significantly with both neuropsychological outcome measures: PASAT-3 (r = -0.275, p = 0.009) and SDMT (r = -0.377, p < 0.001). Multiple regression analyses with age, WM lesions, CLs and GM volume as independent variables, however, did not reveal CL volume as a significant predictor of neuropsychological outcomes, whereas WM lesion volume significantly predicted SDMT and by trend PASAT performance. CONCLUSIONS: These findings suggest a role of WM lesions in the development of cognitive deficits, especially information-processing speed, which may be higher than previously assumed. ABBREVIATIONS CES-D: Center for Epidemiologic Studies Depression scale (ADS-L: Allgemeine Depressions Skala-L, German version of CES-D), CIS: clinically isolated syndrome, CL: cortical lesion, DIR: double inversion recovery, EDSS: Expanded Disability Status Scale, FSMC: fatigue scale for motor and cognitive functions, GM: grey matter, MRI: magnetic resonance imaging, MS: multiple sclerosis, PASAT-3: paced auditory serial addition test 3s, PPMS: primary progressive multiple sclerosis, RRMS: relapsing-remitting multiple sclerosis, SDMT: symbol digit modalities test, SPM: statistical parametric mapping, SPMS: secondary progressive multiple sclerosis, WM: white matter.