Radek Mareček

The default mode network integrity in patients with Parkinson’s disease is levodopa equivalent dose-dependent

Disturbances in the default mode network (DMN) have been described in many neurological and psychiatric disorders including Parkinson’s disease (PD). The DMN is characterized by basal activity that increases during rest or passive visual fixation and decreases (“deactivates”) during cognitive tasks. The network is believed to be involved in cognitive processes. We examined the DMN in PD patients on dopaminergic medication with normal cognitive performance compared to age- and gender-matched healthy controls (HC) using fMRI and three methodological procedures: independent component analysis of resting-state data, analysis of deactivation during a complex visual scene-encoding task, and seed-based functional connectivity analysis. In the PD group, we also studied the effect of dopaminergic medication on the DMN integrity. We did not find any difference between the PD and HC groups in the DMN, but using the daily levodopa equivalent dose as a covariate, we observed an enhanced functional connectivity of the DMN in the posterior cingulate cortex and decreased activation in the left parahippocampal gyrus during the cognitive task. We conclude that dopaminergic therapy has a specific effect on both the DMN integrity and task-related brain activations in cognitively unimpaired PD patients, and these effects seem to be dose-dependent.

Effective connectivity in target stimulus processing: A dynamic causal modeling study of visual oddball task

PURPOSE To investigate the fundamental connectivity architecture of neural structures involved in the goal-directed processing of target events. METHODS Twenty healthy volunteers underwent event-related functional magnetic resonance imaging (fMRI) while performing a standard oddball task. In the task, two types of visual stimuli - rare (target) and frequent - were randomly presented, and subjects were instructed to mentally count the target stimuli. Dynamic causal modeling (DCM), in combination with Bayes factors was used to compare competing neurophysiological models with different intrinsic connectivity structures and input regions within the network of brain regions underlying target stimulus processing. RESULTS Conventional analysis of fMRI data revealed significantly greater activation in response to the target stimuli (in comparison to the frequent stimuli) in several brain regions, including the intraparietal sulci and supramarginal gyri, the anterior and posterior cingulate gyri, the inferior and middle frontal gyri, the superior temporal sulcus, the precuneus/cuneus, and the subcortical grey matter (caudate and thalamus). The most extensive cortical activations were found in the right intraparietal sulcus (IPS), the anterior cingulate cortex (ACC), and the right lateral prefrontal cortex (PFC). These three regions were entered into the DCM. A comparison on a group level revealed that the dynamic causal models in which the ACC and alternatively the IPS served as input regions were superior to a model in which the PFC was assumed to receive external inputs. No significant difference was observed between the fully connected models with ACC and IPS as input regions. Subsequent analysis of the intrinsic connectivity within two investigated models (IPS and ACC) disclosed significant parallel forward connections from the IPS to the frontal areas and from the ACC to the PFC and the IPS. CONCLUSION Our findings indicate that during target stimulus processing there is a bidirectional frontoparietal information flow, very likely reflecting parallel activation of two distinct but partially overlapping attentional or attentional/event-encoding neural systems. Additionally, a simple hierarchy within the right frontal lobe is suggested with the ACC exerting influence over the PFC.

Default Mode Network and Extrastriate Visual Resting State Network in Patients with Parkinson’s Disease Dementia

Aims: Using fMRI, we evaluated the default mode network (DMN) and the extrastriate visual resting state network (ESV-RSN) in 14 patients with Parkinson’s disease dementia (PDD) as compared with 18 patients with Parkinson’s disease (PD) without dementia and 18 healthy controls (HC). Methods: We analyzed the seed-based functional connectivity of both resting state data and deactivations during a visual complex scene-encoding task. Results: Using the posterior cingulate cortex/precuneus as a seed for the DMN analysis, we observed significant decreases of connectivity in the right inferior frontal gyrus in PDD as compared to PD and HC. Using the caudate nucleus as a seed for the ESV-RSN analysis, we found significant decreases of connectivity in the left and right inferior occipital gyrus in PDD as compared to HC. Conclusion: Differences in functional connectivity patterns between PDD and PD/HC were observed in areas known to be engaged in stimulus-driven reorienting of attention and in visual processing.

An optimized voxel-based morphometric study of gray matter changes in patients with left-sided and right-sided mesial temporal lobe epilepsy and hippocampal sclerosis (MTLE/HS)

Purpose:  To determine whether changes in gray matter volume (GMV) differ according to the affected side in mesial temporal lobe epilepsy/hippocampal sclerosis (MTLE/HS) syndrome, and moreover to test the hypothesis of more pronounced structural changes in right‐sided MTLE/HS. This hypothesis (especially that the contralateral thalamus is more affected in right‐sided MTLE/HS) arose from the results of our recent study, wherein more expressed structural and functional changes were observed in a small sample of patients with right‐sided MTLE/HS ( Brázdil et al., 2009 ).

Source-based morphometry of gray matter volume in men with first-episode schizophrenia.

Objectives: There is a lot of variability between the results of studies reporting the pattern of gray matter volume changes in schizophrenia. Methodological issues may play an important role in this heterogeneity. The aim of the present study was to replicate the better performance of multivariate “source‐based morphometry” (SBM) over the mass‐univariate approach. Experimental design: Voxel‐based morphometry of Jacobian‐modulated gray matter volume images, using voxel and cluster level inference, and SBM were performed in a group of first‐episode schizophrenia patients (N = 49) and healthy controls (N = 127). Results: Using SBM we were able to find a significant reduction of gray matter volume in fronto‐temporo‐cerebellar areas whereas no significant results were obtained using voxel‐based morphometry. Conclusion: Multivariate analysis of gray matter volume seems to be a suitable method for characterization of the pattern of changes at the beginning of the illness in schizophrenia subjects. Hum Brain Mapp, 2010.

Maximum-uncertainty linear discrimination analysis of first-episode schizophrenia subjects.

Recent techniques of image analysis brought the possibility to recognize subjects based on discriminative image features. We performed a magnetic resonance imaging (MRI)-based classification study to assess its usefulness for outcome prediction of first-episode schizophrenia patients (FES). We included 39 FES patients and 39 healthy controls (HC) and performed the maximum-uncertainty linear discrimination analysis (MLDA) of MRI brain intensity images. The classification accuracy index (CA) was correlated with the Positive and Negative Syndrome Scale (PANSS) and the Global Assessment of Functioning scale (GAF) at 1-year follow-up. The rate of correct classifications of patients with poor and good outcomes was analyzed using chi-square tests. MLDA classification was significantly better than classification by chance. Leave-one-out accuracy was 72%. CA correlated significantly with PANSS and GAF scores at the 1-year follow-up. Moreover, significantly more patients with poor outcome than those with good outcome were classified correctly. MLDA of brain MR intensity features is, therefore, able to correctly classify a significant number of FES patients, and the discriminative features are clinically relevant for clinical presentation 1 year after the first episode of schizophrenia. The accuracy of the current approach is, however, insufficient to be used in clinical practice immediately. Several methodological issues need to be addressed to increase the usefulness of this classification approach.

Non-invasive brain stimulation of the right inferior frontal gyrus may improve attention in early Alzheimer's disease: A pilot study

INTRODUCTION Repetitive transcranial magnetic stimulation (rTMS) is a noninvasive tool for modulating cortical activity. OBJECTIVES In this pilot study, we evaluated the effects of high frequency rTMS applied over the right inferior frontal gyrus (IFG) on cognitive functions in patients with amnestic mild cognitive impairment (MCI) or incipient dementia due to Alzheimers disease (AD). METHODS Ten patients (6 men; 4 women, mean age of 72 ± 8 years; MMSE 23 ± 3.56) were enrolled in a randomized, placebo-controlled study with a crossover design. All participants received 2 sessions of 10 Hz rTMS over the non-dominant right hemisphere in random order: IFG (active stimulation site) and vertex (control stimulation site). Intensities were adjusted to 90% of resting motor threshold. A total of 2250 pulses were applied in a session. The Trail Making Test (TMT), the Stroop test, and the complex visual scene encoding task (CVSET) were administered before and immediately after each session. The Wilcoxon paired test was used for data analysis. RESULTS Stimulation applied over the IFG induced improvement in the TMT parts A (p = 0.037) and B (p = 0.049). No significant changes were found in the Stroop test or the CVSET after the IFG stimulation. We observed no significant cognitive aftereffects of rTMS applied over the vertex. CONCLUSIONS High frequency rTMS of the right IFG induced significant improvement of attention and psychomotor speed in patients with MCI/mild dementia due to AD. This pilot study is part of a more complex protocol and ongoing research.

Grey Matter Changes in Cognitively Impaired Parkinson's Disease Patients

Background Cortical changes associated with cognitive decline in Parkinsons disease (PD) are not fully explored and require investigations with established diagnostic classification criteria. Objective We used MRI source-based morphometry to evaluate specific differences in grey matter volume patterns across 4 groups of subjects: healthy controls (HC), PD with normal cognition (PD-NC), PD with mild cognitive impairment (MCI-PD) and PD with dementia (PDD). Methods We examined 151 consecutive subjects: 25 HC, 75 PD-NC, 29 MCI-PD, and 22 PDD at an Italian and Czech movement disorder centre. Operational diagnostic criteria were applied to classify MCI-PD and PDD. All structural MRI images were processed together in the Czech centre. The spatial independent component analysis was used to assess group differences of local grey matter volume. Results We identified two independent patterns of grey matter volume deviations: a) Reductions in the hippocampus and temporal lobes; b) Decreases in fronto-parietal regions and increases in the midbrain/cerebellum. Both patterns differentiated PDD from all other groups and correlated with visuospatial deficits and letter verbal fluency, respectively. Only the second pattern additionally differentiated PD-NC from HC. Conclusion Grey matter changes in PDD involve areas associated with Alzheimer-like pathology while fronto-parietal abnormalities are possibly an early marker of PD cognitive decline. These findings are consistent with a non-linear cognitive progression in PD.

Functional Imaging of the Cerebellum and Basal Ganglia During Predictive Motor Timing in Early Parkinson's Disease

The basal ganglia and the cerebellum have both emerged as important structures involved in the processing of temporal information.

Gray matter morphology and the level of functioning in one-year follow-up of first-episode schizophrenia patients.

UNLABELLED Schizophrenia is a condition with a highly variable course that is hard to predict. The aim of the present study was to investigate if local gray matter volume (GMV) can differentiate poor (PF) and good (GF) functioning patients using voxel-wise analysis in a group of first-episode schizophrenia subjects (FES). METHOD 32 FES male patients were assessed twice: at the time of the first episode of schizophrenia and one year later. 18 healthy controls matched for age, gender, and handedness were also included. Local gray matter volume was analyzed using voxel-wise full-factorial design with factors group (GF, PF) and time. RESULTS FES subjects had bilateral gray matter reduction in the lateral prefrontal cortex as compared with healthy controls. PF subjects had smaller GMV in the left orbitofrontal and frontopolar cortex. CONCLUSION GMV in the left prefrontal cortex differentiates later poor and good functioning schizophrenia patients. Morphological analysis might be considered a candidate for a biological marker in outcome prediction. However, the small sample size, and the lack of female subjects limit generalization of results. Moreover, studies analyzing the predictive value of brain morphology on a single-subject level should be performed to assess its real usefulness in outcome prediction.