Anatomic & metabolic brain markers of the m.3243A>G mutation: A multi-parametric 7T MRI study

One of the most common mitochondrial DNA (mtDNA) mutations, the A to G transition at base pair 3243, has been linked to changes in the brain, in addition to commonly observed hearing problems, diabetes and myopathy. However, a detailed quantitative description of m.3243A>G patients' brains has not been provided so far. In this study, ultra-high field MRI at 7T and volume- and surface-based data analyses approaches were used to highlight morphology (i.e. atrophy)-, microstructure (i.e. myelin and iron concentration)- and metabolism (i.e. cerebral blood flow)-related differences between patients (N = 22) and healthy controls (N = 15). The use of quantitative MRI at 7T allowed us to detect subtle changes of biophysical processes in the brain with high accuracy and sensitivity, in addition to typically assessed lesions and atrophy. Furthermore, the effect of m.3243A>G mutation load in blood and urine epithelial cells on these MRI measures was assessed within the patient population and revealed that blood levels were most indicative of the brain's state and disease severity, based on MRI as well as on neuropsychological data. Morphometry MRI data showed a wide-spread reduction of cortical, subcortical and cerebellar gray matter volume, in addition to significantly enlarged ventricles. Moreover, surface-based analyses revealed brain area-specific changes in cortical thickness (e.g. of the auditory cortex), and in T1, T2* and cerebral blood flow as a function of mutation load, which can be linked to typically m.3243A>G-related clinical symptoms (e.g. hearing impairment). In addition, several regions linked to attentional control (e.g. middle frontal gyrus), the sensorimotor network (e.g. banks of central sulcus) and the default mode network (e.g. precuneus) were characterized by alterations in cortical thickness, T1, T2* and/or cerebral blood flow, which has not been described in previous MRI studies. Finally, several hypotheses, based either on vascular, metabolic or astroglial implications of the m.3243A>G mutation, are discussed that potentially explain the underlying pathobiology. To conclude, this is the first 7T and also the largest MRI study on this patient population that provides macroscopic brain correlates of the m.3243A>G mutation indicating potential MRI biomarkers of mitochondrial diseases and might guide future (longitudinal) studies to extensively track neuropathological and clinical changes

Modelling human choices: MADeM and decision‑making

Research supported by FAPESP 2015/50122-0 and DFG-GRTK 1740/2. RP and AR are also part of the Research, Innovation and Dissemination Center for Neuromathematics FAPESP grant (2013/07699-0). RP is supported by a FAPESP scholarship (2013/25667-8). ACR is partially supported by a CNPq fellowship (grant 306251/2014-0)

The Functional Neuroanatomy of Target Detection: An fMRI Study of Visual and Auditory Oddball Tasks

The neuronal response patterns that are required for an adequate behavioural reaction to subjectively relevant changes in the environment are commonly studied by means of oddball paradigms, in which occasional ‘target' stimuli have to be detected in a train of frequent ‘non-target' stimuli. The detection of such task-relevant stimuli is accompanied by a parietocentral positive component of the event-related potential, the P300. We performed EEG recordings of visual and auditory event-related potentials and functional magnetic resonance imaging (fMRI) when healthy subjects performed an oddball task. Significant increases in fMRI signal for target versus non-target conditions were observed in the supramarginal gyrus, frontal operculum and insular cortex bilaterally, and in further circumscribed parietal and frontal regions. These effects were consistent over various stimulation and response modalities and can be regarded as specific for target detection in both the auditory and the visual modality. These results therefore contribute to the understanding of the target detection network in human cerebral cortex and impose constraints on attempts at localizing the neuronal P300 generator. This is of importance both from a neurobiological perspective and because of the widespread application of the physiological correlates of target detection in clinical P300 studie

www.elsevier.com/locate/ynimg Real-time independent component analysis of fMRI time-series

Real-time functional magnetic resonance imaging (fMRI) enables one to monitor a subject’s brain activity during an ongoing session. The availability of online information about brain activity is essential for developing and refining interactive fMRI paradigms in research and clinical trials and for neurofeedback applications. Data analysis for real-time fMRI has traditionally been based on hypothesis-driven processing methods. Off-line data analysis, conversely, may be usefully complemented by data-driven approaches, such as independent component analysis (ICA), which can identify brain activity without a priori temporal assumptions on brain activity. However, ICA is commonly considered a time-consuming procedure and thus unsuitable to process the high flux of fMRI data while they are acquired. Here, by specific choices regarding the implementation, we exported the ICA framework and implemented it into real-time fMRI data analysis. We show that, reducing the ICA input to a few points within a time-series in a sliding-window approach, computational times become compatible with real-time settings. Our technique produced accurate dynamic readouts of brain activity as well as a precise spatiotemporal history of quasistationary patterns in the form of cumulative activation maps and time courses. Results from real and simulated motor activation data show comparable performances for the proposed ICA implementation and standard linear regression analysis applied eithe

Functional fields in human auditory cortex revealed by time-resolved fMRI without interference of EPI noise

The gradient switching during fast echoplanar functional magnetic resonance imaging (EPI-fMRI) produces loud noises that may interact with the functional activation of the central auditory system induced by experimental acoustic stimuli. This interaction is unpredictable and is likely to confound the interpretation of functional maps of the auditory cortex. In the present study we used an experimental design which does not require the presentation of stimuli during EPI acquisitions and allows for mapping of the auditory cortex without the interference of scanner noise. The design relies on the physiological delays between the onset, or the end, of stimulation and the corresponding hemodynamic response. Owing to these delays and through a time-resolved acquisition protocol it is possible to analyze the decay of the stimulus-specific signal changes after the cessation of the stimulus itself and before the onset of the EPI-acoustic noise related activation (decay-sampling technique). This experimental design, which might permit a more detailed insight in the auditory cortex, has been applied to the study of the cortical responses to pulsed 1000 Hz sine tones. Distinct activation clusters were detected in the Heschl's gyri and the planum temporale, with an increased extension compared to a conventional block-design paradigm. Furthermore, the comparison of the hemodynamic response of the most anterior and the posterior clusters of activation highlighted differential response patterns to the sound stimulation and to the EPI-noise. These differences, attributable to reciprocal saturation effects unevenly distributed over the superior temporal cortex, provided evidence for functionally distinct auditory fields

Accelerated estimation and permutation inference for ACE modeling

There are a wealth of tools for fitting linear models at each location in the brain in neuroimaging analysis, and a wealth of genetic tools for estimating heritability for a small number of phenotypes. But there remains a need for computationally efficient neuroimaging genetic tools that can conduct analyses at the brain-wide scale. Here we present a simple method for heritability estimation on twins that replaces a variance component model-which requires iterative optimisation-with a (noniterative) linear regression model, by transforming data to squared twin-pair differences. We demonstrate that the method has comparable bias, mean squared error, false positive risk, and power to best practice maximum-likelihood-based methods, while requiring a small fraction of the computation time. Combined with permutation, we call this approach "Accelerated Permutation Inference for the ACE Model (APACE)" where ACE refers to the additive genetic (A) effects, and common (C), and unique (E) environmental influences on the trait. We show how the use of spatial statistics like cluster size can dramatically improve power, and illustrate the method on a heritability analysis of an fMRI working memory dataset

The spatiotemporal pattern of auditory cortical responses during verbal hallucinations

Functional magnetic resonance imaging (fMRI) studies can provide insight into the neural correlates of hallucinations. Commonly, such studies require self-reports about the timing of the hallucination events. While many studies have found activity in higher-order sensory cortical areas, only a few have demonstrated activity of the primary auditory cortex during auditory verbal hallucinations. In this case, using self-reports as a model of brain activity may not be sensitive enough to capture all neurophysiological signals related to hallucinations. We used spatial independent component analysis (sICA) to extract the activity patterns associated with auditory verbal hallucinations in six schizophrenia patients. SICA decomposes the functional data set into a set of spatial maps without the use of any input function. The resulting activity patterns from auditory and sensorimotor components were further analyzed in a single-subject fashion using a visualization tool that allows for easy inspection of the variability of regional brain responses. We found bilateral auditory cortex activity, including Heschl's gyrus, during hallucinations of one patient, and unilateral auditory cortex activity in two more patients. The associated time courses showed a large variability in the shape, amplitude, and time of onset relative to the self-reports. However, the average of the time courses during hallucinations showed a clear association with this clinical phenomenon. We suggest that detection of this activity may be facilitated by examining hallucination epochs of sufficient length, in combination with a data-driven approach