Sven Haller

Regional Gray Matter Volume Abnormalities in the At Risk Mental State

BACKGROUND Individuals with an At Risk Mental State (ARMS) have a very high risk of developing a psychotic disorder but the basis of this risk is unclear. We addressed this issue by studying gray matter volume in this group with magnetic resonance imaging (MRI). METHODS Thirty-five individuals with an ARMS, 25 patients with first episode schizophrenia, and 22 healthy volunteers were studied using a 1.5T MRI scanner. Twelve (34%) of the ARMS group developed schizophrenia in the 2 years subsequent to scanning. RESULTS There were significant volumetric differences between the three groups in the left insula, superior temporal gyrus, cingulate gyrus and precuneus. In these regions, the volume in the ARMS group was smaller than in volunteers but not significantly different from that in the first episode (FE) group. Direct comparison of the ARMS and control groups revealed additional areas of reduced volume in the left medial temporal cortex. Within the ARMS group, those subjects who later developed psychosis had less gray matter than subjects who did not in the right insula, inferior frontal and superior temporal gyrus. CONCLUSIONS The ARMS was associated with reductions in gray matter volume in areas that are also reduced in schizophrenia, suggesting that these are a correlate of an increased vulnerability to psychosis. Volumetric differences within the ARMS group may be related to the subsequent onset of schizophrenia in a subset of those at high risk.

Real-time fMRI neurofeedback: progress and challenges.

In February of 2012, the first international conference on real time functional magnetic resonance imaging (rtfMRI) neurofeedback was held at the Swiss Federal Institute of Technology Zurich (ETHZ), Switzerland. This review summarizes progress in the field, introduces current debates, elucidates open questions, and offers viewpoints derived from the conference. The review offers perspectives on study design, scientific and clinical applications, rtfMRI learning mechanisms and future outlook.

Resting-State Functional MR Imaging: A New Window to the Brain

Resting-state (RS) functional magnetic resonance (MR) imaging constitutes a novel paradigm that examines spontaneous brain function by using blood oxygen level-dependent contrast in the absence of a task. Spatially distributed networks of temporal synchronization can be detected that can characterize RS networks (RSNs). With a short acquisition time of less than 10 minutes, RS functional MR imaging can be applied in special populations such as children and patients with dementia. Some RSNs are already present in utero, while others mature in childhood. Around 10 major RSNs are consistently found in adults, but their exact spatial extent and strength of coherence are affected by physiologic parameters and drugs. Though the acquisition and analysis methods are still evolving, new disease insights are emerging in a variety of neurologic and psychiatric disorders. The default mode network is affected in Alzheimer disease and various other diseases of cognitive impairment. Alterations in RSNs have been identified in many diseases, in the absence of evident structural modifications, indicating a high sensitivity of the method. Moreover, there is evidence of correlation between RSN alterations and disease progression and severity. However, different diseases often affect the same RSN, illustrating the limited specificity of the findings. This suggests that neurologic and psychiatric diseases are characterized by altered interactions between RSNs and therefore the whole brain should be examined as an integral network (with subnetworks), for example, using graph analysis. A challenge for clinical applications of RS functional MR imaging is the potentially confounding effect of aging, concomitant vascular diseases, or medication on the neurovascular coupling and consequently the functional MR imaging response. Current investigation combines RS functional MR imaging and other methods such as electroencephalography or magnetoencephalography to better understand the vascular and neuronal contributions to alterations in functional connectivity.

Real-time fMRI feedback training may improve chronic tinnitus

ObjectivesTinnitus consists of a more or less constant aversive tone or noise and is associated with excess auditory activation. Transient distortion of this activation (repetitive transcranial magnetic stimulation, rTMS) may improve tinnitus. Recently proposed operant training in real-time functional magnetic resonance imaging (rtfMRI) neurofeedback allows voluntary modification of specific circumscribed neuronal activations. Combining these observations, we investigated whether patients suffering from tinnitus can (1) learn to voluntarily reduce activation of the auditory system by rtfMRI neurofeedback and whether (2) successful learning improves tinnitus symptoms.MethodsSix participants with chronic tinnitus were included. First, location of the individual auditory cortex was determined in a standard fMRI auditory block-design localizer. Then, participants were trained to voluntarily reduce the auditory activation (rtfMRI) with visual biofeedback of the current auditory activation.ResultsAuditory activation significantly decreased after rtfMRI neurofeedback. This reduced the subjective tinnitus in two of six participants.ConclusionThese preliminary results suggest that tinnitus patients learn to voluntarily reduce spatially specific auditory activations by rtfMRI neurofeedback and that this may reduce tinnitus symptoms. Optimized training protocols (frequency, duration, etc.) may further improve the results.

Combined analysis of grey matter voxel-based morphometry and white matter tract-based spatial statistics in late-life bipolar disorder.

BACKGROUND Previous magnetic resonance imaging (MRI) studies in young patients with bipolar disorder indicated the presence of grey matter concentration changes as well as microstructural alterations in white matter in various neocortical areas and the corpus callosum. Whether these structural changes are also present in elderly patients with bipolar disorder with long-lasting clinical evolution remains unclear. METHODS We performed a prospective MRI study of consecutive elderly, euthymic patients with bipolar disorder and healthy, elderly controls. We conducted a voxel-based morphometry (VBM) analysis and a tract-based spatial statistics (TBSS) analysis to assess fractional anisotropy and longitudinal, radial and mean diffusivity derived by diffusion tensor imaging (DTI). RESULTS We included 19 patients with bipolar disorder and 47 controls in our study. Fractional anisotropy was the most sensitive DTI marker and decreased significantly in the ventral part of the corpus callosum in patients with bipolar disorder. Longitudinal, radial and mean diffusivity showed no significant between-group differences. Grey matter concentration was reduced in patients with bipolar disorder in the right anterior insula, head of the caudate nucleus, nucleus accumbens, ventral putamen and frontal orbital cortex. Conversely, there was no grey matter concentration or fractional anisotropy increase in any brain region in patients with bipolar disorder compared with controls. LIMITATIONS The major limitation of our study is the small number of patients with bipolar disorder. CONCLUSION Our data document the concomitant presence of grey matter concentration decreases in the anterior limbic areas and the reduced fibre tract coherence in the corpus callosum of elderly patients with long-lasting bipolar disorder.

Individual Prediction of Cognitive Decline in Mild Cognitive Impairment Using Support Vector Machine-Based Analysis of Diffusion Tensor Imaging Data

Although cross-sectional diffusion tensor imaging (DTI) studies revealed significant white matter changes in mild cognitive impairment (MCI), the utility of this technique in predicting further cognitive decline is debated. Thirty-five healthy controls (HC) and 67 MCI subjects with DTI baseline data were neuropsychologically assessed at one year. Among them, there were 40 stable (sMCI; 9 single domain amnestic, 7 single domain frontal, 24 multiple domain) and 27 were progressive (pMCI; 7 single domain amnestic, 4 single domain frontal, 16 multiple domain). Fractional anisotropy (FA) and longitudinal, radial, and mean diffusivity were measured using Tract-Based Spatial Statistics. Statistics included group comparisons and individual classification of MCI cases using support vector machines (SVM). FA was significantly higher in HC compared to MCI in a distributed network including the ventral part of the corpus callosum, right temporal and frontal pathways. There were no significant group-level differences between sMCI versus pMCI or between MCI subtypes after correction for multiple comparisons. However, SVM analysis allowed for an individual classification with accuracies up to 91.4% (HC versus MCI) and 98.4% (sMCI versus pMCI). When considering the MCI subgroups separately, the minimum SVM classification accuracy for stable versus progressive cognitive decline was 97.5% in the multiple domain MCI group. SVM analysis of DTI data provided highly accurate individual classification of stable versus progressive MCI regardless of MCI subtype, indicating that this method may become an easily applicable tool for early individual detection of MCI subjects evolving to dementia.

Closed-loop brain training: the science of neurofeedback

Neurofeedback is a psychophysiological procedure in which online feedback of neural activation is provided to the participant for the purpose of self-regulation. Learning control over specific neural substrates has been shown to change specific behaviours. As a progenitor of brain–machine interfaces, neurofeedback has provided a novel way to investigate brain function and neuroplasticity. In this Review, we examine the mechanisms underlying neurofeedback, which have started to be uncovered. We also discuss how neurofeedback is being used in novel experimental and clinical paradigms from a multidisciplinary perspective, encompassing neuroscientific, neuroengineering and learning-science viewpoints.

Black holes in multiple sclerosis: definition, evolution, and clinical correlations

Sahraian MA, Radue E‐W, Haller S, Kappos L. Black holes in multiple sclerosis: definition, evolution, and clinical correlations.
Acta Neurol Scand: 2010: 122: 1–8.
© 2009 The Authors Journal compilation

Cerebral Microhemorrhage and Iron Deposition in Mild Cognitive Impairment: Susceptibility-weighted MR Imaging Assessment

PURPOSE To test whether susceptibility-weighted magnetic resonance imaging at baseline may help predict cognitive decline. MATERIALS AND METHODS This prospective study was approved by the institutional review board, and written informed consent was obtained from all participants. Thirty-five healthy control subjects and 69 patients with mild cognitive impairment were included. Patients with mild cognitive impairment underwent neuropsychologic follow-up after 1 year (40 patients with stable mild cognitive impairment, 27 with progressive mild cognitive impairment, and two lost to follow-up). Cerebral microhemorrhages were visually analyzed by two experienced neuroradiologists in consensus. Iron deposition in deep gray matter was assessed with voxel-wise and region-of-interest analysis after nonlinear spatial registration. In addition, individual classification of mild cognitive impairment was analyzed by using a support vector machine (SVM). RESULTS At baseline, the number of cerebral microhemorrhages was significantly higher in the mild cognitive impairment group than in the control group (P < .01) but did not differ between the patients with stable and those with progressive mild cognitive impairment. Compared with the control group, patients with mild cognitive impairment had increased iron concentration in the right pallidum (P < .01) and right substantia nigra (P < .01) but decreased concentration in the right red nucleus (P < .05). The classification based on the SVM successfully helped discriminate patients with mild cognitive impairment from the healthy control subjects (accuracy, 84%; sensitivity, 89%; specificity, 85%) and those with stable from those with progressive mild cognitive impairment (accuracy, 85%; sensitivity, 84%; specificity, 83%). CONCLUSION The findings reveal an accumulation of cerebral microhemorrhage in patients with mild cognitive impairment that is present at baseline, independent of subsequent cognitive decline, as well as an altered iron distribution in subcortical nuclei between the healthy control subjects and patients with mild cognitive impairment. Analysis of iron deposition at baseline performed with an SVM might help identify individual patients with mild cognitive impairment at risk for cognitive decline. SUPPLEMENTAL MATERIAL http://radiology.rsna.org/lookup/suppl/doi:10.1148/radiol.10100612/-/DC1.

Pitfalls in fMRI

Several different techniques allow a functional assessment of neuronal activations by magnetic resonance imaging (fMRI). The by far most influential fMRI technique is based on a local T2*-sensitive hemodynamic response to neuronal activation, also known as the blood oxygenation level dependent or BOLD effect. Consequently, the term ‘fMRI’ is often used synonymously with BOLD imaging. Because interpretations of fMRI brain activation maps often appear intuitive and compelling, the reader might be tempted not to critically question the fundamental processes and assumptions. We review some essential processes and assumptions of BOLD fMRI and discuss related confounds and pitfalls in fMRI – from the underlying physiological effect, to data acquisition, data analysis and the interpretation of the results including clinical fMRI. A background framework is provided for the systematic and critical interpretation of fMRI results.