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Purpose To extend a previously developed machine learning algorithm for harmonizing brain volumetric data of individuals undergoing neuroradiologic assessment of Alzheimer disease not encountered during model training. Materials and Methods Neuroharmony is a recently developed method that uses image quality metrics as predictors to remove scanner-related effects in brain-volumetric data using random forest regression. To account for the interactions between Alzheimer disease pathology and image quality metrics during harmonization, the authors developed a multiclass extension of Neuroharmony for individuals with and without cognitive impairment. Cross-validation experiments were performed to benchmark performance against other available strategies using data from 20 864 participants with and without cognitive impairment, spanning 11 prospective and retrospective cohorts and 43 scanners. Evaluation metrics assessed the ability to remove scanner-related variations in brain volumes (marker concordance between scanner pairs) while retaining the ability to delineate different diagnostic groups (preserving disease-related signal). Results For each strategy, marker concordances between scanners were significantly better ( P < .001) compared with preharmonized data. The proposed multiclass model achieved significantly higher concordance (mean, 0.75 ± 0.09 [SD]) than the Neuroharmony model trained on individuals without cognitive impairment (mean, 0.70 ± 0.11) and preserved disease-related signal (∆AUC [area under the receiver operating characteristic curve] = -0.006 ± 0.027) better than the Neuroharmony model trained on individuals with and without cognitive impairment that did not use the proposed extension (∆AUC = -0.091 ± 0.036). The marker concordance was better in scanners seen during training (concordance > 0.97) than unseen (concordance < 0.79), independent of cognitive status. Conclusion In a large-scale multicenter dataset, the proposed multiclass Neuroharmony model outperformed other available strategies for harmonizing brain volumetric data from unseen scanners in a clinical setting. Keywords: Image Postprocessing, MR Imaging, Dementia, Random Forest Supplemental material is available for this article. Published under a CC BY 4.0 license See also commentary by Haller in this issue.

The unprecedented increase in life expectancy presents a unique opportunity and the necessity to explore both healthy and pathological aspects of ageing. Electroencephalography (EEG) has been widely used to identify neuromarkers of cognitive ageing due to its affordability and richness in information. However, despite the growing volume of data and methodological advancements, the abundance of contradictory and non-reproducible findings has hindered clinical translation. To address these challenges, our study introduces a comprehensive workflow expanding on previous EEG studies and investigates various static and dynamic power and connectivity estimates as potential neuromarkers of cognitive ageing in a large dataset. We also assess the robustness of our findings by testing their susceptibility to band specification. Finally, we characterise our findings using functionally annotated brain networks to improve their interpretability and multi-modal integration. Our analysis demonstrates the effect of methodological choices on findings and that dynamic rather than static neuromarkers are not only more sensitive but also more robust. Consequently, they emerge as strong candidates for cognitive ageing neuromarkers. Moreover, we were able to replicate the most established EEG findings in cognitive ageing, such as alpha oscillation slowing, increased beta power, reduced reactivity across multiple bands, and decreased delta connectivity. Additionally, when considering individual variations in the alpha band, we clarified that alpha power is characteristic of memory performance rather than ageing, highlighting its potential as a neuromarker for cognitive ageing. Finally, our approach using functionally annotated source reconstruction allowed us to provide insights into domain-specific electrophysiological mechanisms underlying memory performance and ageing. HIGHLIGHTS: We provide an open and reproducible pipeline with a comprehensive workflow to investigate static and dynamic EEG neuromarkers. Neuromarkers related to neural dynamics are sensitive and robust. Individualised alpha power characterises cognitive performance rather than ageing. Functional annotation allows cross-modal interpretation of EEG findings., (© 2024 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)

The Brain Imaging Data Structure (BIDS) is a community-driven standard for the organization of data and metadata from a growing range of neuroscience modalities. This paper is meant as a history of how the standard has developed and grown over time. We outline the principles behind the project, the mechanisms by which it has been extended, and some of the challenges being addressed as it evolves. We also discuss the lessons learned through the project, with the aim of enabling researchers in other domains to learn from the success of BIDS., Competing Interests: Gaia Rizzo is an employee of Invicro. No other authors have competing interests to declare., (© 2024 Massachusetts Institute of Technology. Published under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.)

The Brain Imaging Data Structure (BIDS) is a community-driven standard for the organization of data and metadata from a growing range of neuroscience modalities. This paper is meant as a history of how the standard has developed and grown over time. We outline the principles behind the project, the mechanisms by which it has been extended, and some of the challenges being addressed as it evolves. We also discuss the lessons learned through the project, with the aim of enabling researchers in other domains to learn from the success of BIDS., Competing Interests: Declaration of Competing Interests GR is an employee of Invicro. No other authors have competing interests to declare.

Magnetic Resonance Imaging (MRI) provides a unique opportunity to investigate neural changes in healthy and clinical conditions. Its large inherent susceptibility to motion, however, often confounds the measurement. Approaches assessing, correcting, or preventing motion corruption of MRI measurements are under active development, and such efforts can greatly benefit from carefully controlled datasets. We present a unique dataset of structural brain MRI images collected from 148 healthy adults which includes both motion-free and motion-affected data acquired from the same participants. This matched dataset allows direct evaluation of motion artefacts, their impact on derived data, and testing approaches to correct for them. Our dataset further stands out by containing images with different levels of motion artefacts from the same participants, is enriched with expert scoring characterizing the image quality from a clinical point of view and is also complemented with standard image quality metrics obtained from MRIQC. The goal of the dataset is to raise awareness of the issue and provide a useful resource to assess and improve current motion correction approaches., (© 2022. The Author(s).)

Real-time quality assessment (rtQA) of functional magnetic resonance imaging (fMRI) based on blood oxygen level-dependent (BOLD) signal changes is critical for neuroimaging research and clinical applications. The losses of BOLD sensitivity because of different types of technical and physiological noise remain major sources of fMRI artifacts. Due to difficulty of subjective visual perception of image distortions during data acquisitions, a comprehensive automatic rtQA is needed. To facilitate rapid rtQA of fMRI data, we applied real-time and recursive quality assessment methods to whole-brain fMRI volumes, as well as time-series of target brain areas and resting-state networks. We estimated recursive temporal signal-to-noise ratio (rtSNR) and contrast-to-noise ratio (rtCNR), and real-time head motion parameters by a framewise rigid-body transformation (translations and rotations) using the conventional current to template volume registration. In addition, we derived real-time framewise (FD) and micro (MD) displacements based on head motion parameters and evaluated the temporal derivative of root mean squared variance over voxels (DVARS). For monitoring time-series of target regions and networks, we estimated the number of spikes and amount of filtered noise by means of a modified Kalman filter. Finally, we applied the incremental general linear modeling (GLM) to evaluate real-time contributions of nuisance regressors (linear trend and head motion). Proposed rtQA was demonstrated in real-time fMRI neurofeedback runs without and with excessive head motion and real-time simulations of neurofeedback and resting-state fMRI data. The rtQA was implemented as an extension of the open-source OpenNFT software written in Python, MATLAB and C++ for neurofeedback, task-based, and resting-state paradigms. We also developed a general Python library to unify real-time fMRI data processing and neurofeedback applications. Flexible estimation and visualization of rtQA facilitates efficient rtQA of fMRI data and helps the robustness of fMRI acquisitions by means of substantiating decisions about the necessity of the interruption and re-start of the experiment and increasing the confidence in neural estimates., (© 2022. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Depression is the leading cause of disability worldwide, with prevalence rates rising. Despite the scale of the problem, available pharmacological and psychological interventions only have limited efficacy. The National Institute of Health's Science of Behaviour Change framework proposes to address this issue by capitalising on insights from basic science to identify mechanisms that can be targeted by novel interventions. The current study evaluated the potential of a computerized programme aimed at improving affective control, a mechanistic target involved in both risk and maintenance of depression. In a first phase the cognitive profiles of 48 depressed individuals (mean age: 39 years, 75 % female) were compared to cognitive functioning in 16 never-depressed individuals (mean age: 31 years, 56 % female). The sole index of functioning that differed between diagnostic groups was reaction time across negative and positively valanced trials on an affective Stroop task (d = 0.58). This index was then used to evaluate an affective control training (AffeCT) against a placebo training. Results showed no significant changes on tasks that showed no differences with never-depressed individuals in Phase I. However, compared to placebo training, AffeCT led to significantly greater improvement in the target index, affective Stroop performance (d = 1.17). Importantly, AffeCT led to greater reductions in negative affect as measured by the Positive Affect and Negative Affect Schedule compared to the placebo training (d = 0.98). This proof-of-concept study shows promising benefits of AffeCT on depressed individuals' affect, but not depressive symptoms. It further supports the utility of the Science of Behaviour Change framework, highlighting the need for determining meaningful assays of target mechanisms when evaluating novel interventions., (Copyright © 2022 The Authors. Published by Elsevier B.V. All rights reserved.)

The Brain Imaging Data Structure (BIDS) established community consensus on the organization of data and metadata for several neuroimaging modalities. Traditionally, BIDS had a strong focus on functional magnetic resonance imaging (MRI) datasets and lacked guidance on how to store multimodal structural MRI datasets. Here, we present and describe the BIDS Extension Proposal 001 (BEP001), which adds a range of quantitative MRI (qMRI) applications to the BIDS. In general, the aim of qMRI is to characterize brain microstructure by quantifying the physical MR parameters of the tissue via computational, biophysical models. By proposing this new standard, we envision standardization of qMRI through multicenter dissemination of interoperable datasets. This way, BIDS can act as a catalyst of convergence between qMRI methods development and application-driven neuroimaging studies that can help develop quantitative biomarkers for neural tissue characterization. In conclusion, this BIDS extension offers a common ground for developers to exchange novel imaging data and tools, reducing the entrance barrier for qMRI in the field of neuroimaging., (© 2022. The Author(s).)

Low-intensity transcranial electrical stimulation (tES), including alternating or direct current stimulation, applies weak electrical stimulation to modulate the activity of brain circuits. Integration of tES with concurrent functional MRI (fMRI) allows for the mapping of neural activity during neuromodulation, supporting causal studies of both brain function and tES effects. Methodological aspects of tES-fMRI studies underpin the results, and reporting them in appropriate detail is required for reproducibility and interpretability. Despite the growing number of published reports, there are no consensus-based checklists for disclosing methodological details of concurrent tES-fMRI studies. The objective of this work was to develop a consensus-based checklist of reporting standards for concurrent tES-fMRI studies to support methodological rigor, transparency and reproducibility (ContES checklist). A two-phase Delphi consensus process was conducted by a steering committee (SC) of 13 members and 49 expert panelists through the International Network of the tES-fMRI Consortium. The process began with a circulation of a preliminary checklist of essential items and additional recommendations, developed by the SC on the basis of a systematic review of 57 concurrent tES-fMRI studies. Contributors were then invited to suggest revisions or additions to the initial checklist. After the revision phase, contributors rated the importance of the 17 essential items and 42 additional recommendations in the final checklist. The state of methodological transparency within the 57 reviewed concurrent tES-fMRI studies was then assessed by using the checklist. Experts refined the checklist through the revision and rating phases, leading to a checklist with three categories of essential items and additional recommendations: (i) technological factors, (ii) safety and noise tests and (iii) methodological factors. The level of reporting of checklist items varied among the 57 concurrent tES-fMRI papers, ranging from 24% to 76%. On average, 53% of checklist items were reported in a given article. In conclusion, use of the ContES checklist is expected to enhance the methodological reporting quality of future concurrent tES-fMRI studies and increase methodological transparency and reproducibility., (© 2022. © The Author(s).)

As the global health crisis unfolded, many academic conferences moved online in 2020. This move has been hailed as a positive step towards inclusivity in its attenuation of economic, physical, and legal barriers and effectively enabled many individuals from groups that have traditionally been underrepresented to join and participate. A number of studies have outlined how moving online made it possible to gather a more global community and has increased opportunities for individuals with various constraints, e.g., caregiving responsibilities. Yet, the mere existence of online conferences is no guarantee that everyone can attend and participate meaningfully. In fact, many elements of an online conference are still significant barriers to truly diverse participation: the tools used can be inaccessible for some individuals; the scheduling choices can favour some geographical locations; the set-up of the conference can provide more visibility to well-established researchers and reduce opportunities for early-career researchers. While acknowledging the benefits of an online setting, especially for individuals who have traditionally been underrepresented or excluded, we recognize that fostering social justice requires inclusivity to actively be centered in every aspect of online conference design. Here, we draw from the literature and from our own experiences to identify practices that purposefully encourage a diverse community to attend, participate in, and lead online conferences. Reflecting on how to design more inclusive online events is especially important as multiple scientific organizations have announced that they will continue offering an online version of their event when in-person conferences can resume., (© The Author(s) 2021. Published by Oxford University Press on behalf of GigaScience. 2021.)

Real-time fMRI neurofeedback is an increasingly popular neuroimaging technique that allows an individual to gain control over his/her own brain signals, which can lead to improvements in behavior in healthy participants as well as to improvements of clinical symptoms in patient populations. However, a considerably large ratio of participants undergoing neurofeedback training do not learn to control their own brain signals and, consequently, do not benefit from neurofeedback interventions, which limits clinical efficacy of neurofeedback interventions. As neurofeedback success varies between studies and participants, it is important to identify factors that might influence neurofeedback success. Here, for the first time, we employed a big data machine learning approach to investigate the influence of 20 different design-specific (e.g. activity vs. connectivity feedback), region of interest-specific (e.g. cortical vs. subcortical) and subject-specific factors (e.g. age) on neurofeedback performance and improvement in 608 participants from 28 independent experiments. With a classification accuracy of 60% (considerably different from chance level), we identified two factors that significantly influenced neurofeedback performance: Both the inclusion of a pre-training no-feedback run before neurofeedback training and neurofeedback training of patients as compared to healthy participants were associated with better neurofeedback performance. The positive effect of pre-training no-feedback runs on neurofeedback performance might be due to the familiarization of participants with the neurofeedback setup and the mental imagery task before neurofeedback training runs. Better performance of patients as compared to healthy participants might be driven by higher motivation of patients, higher ranges for the regulation of dysfunctional brain signals, or a more extensive piloting of clinical experimental paradigms. Due to the large heterogeneity of our dataset, these findings likely generalize across neurofeedback studies, thus providing guidance for designing more efficient neurofeedback studies specifically for improving clinical neurofeedback-based interventions. To facilitate the development of data-driven recommendations for specific design details and subpopulations the field would benefit from stronger engagement in open science research practices and data sharing., Competing Interests: Declaration of Competing Interest SHK receives payment from Mendi Innovations AB, Stockholm, Sweden., (Copyright © 2021 The Author(s). Published by Elsevier Inc. All rights reserved.)

Neurofeedback (NF) is a complex learning scenario, as the task consists of trying out mental strategies while processing a feedback signal that signifies activation in the brain area to be self-regulated and acts as a potential reward signal. In an attempt to dissect these subcomponents, we obtained whole-brain networks associated with efficient self-regulation in two paradigms: parallel, where the task was performed concurrently, combining feedback with strategy execution; and serial, where the task was performed consecutively, separating feedback processing from strategy execution. Twenty participants attempted to control their anterior midcingulate cortex (aMCC) using functional magnetic resonance imaging (fMRI) NF in 18 sessions over 2 weeks, using cognitive and emotional mental strategies. We analyzed whole-brain fMRI activations in the NF training runs with the largest aMCC activation for the serial and parallel paradigms. The equal length of the strategy execution and the feedback processing periods in the serial paradigm allows a description of the two task subcomponents with equal power. The resulting activation maps were spatially correlated with functionally annotated intrinsic connectivity brain maps (BMs). Brain activation in the parallel condition correlates with the basal ganglia (BG) network, the cingulo-opercular network (CON), and the frontoparietal control network (FPCN); brain activation in the serial strategy execution condition with the default mode network (DMN), the FPCN, and the visual processing network; while brain activation in the serial feedback processing condition predominantly with the CON, the DMN, and the FPCN. Additional comparisons indicate that BG activation is characteristic to the parallel paradigm, while supramarginal gyrus (SMG) and superior temporal gyrus (STG) activations are characteristic to the serial paradigm. The multifaceted view of the subcomponents allows describing the cognitive processes associated with strategy execution and feedback processing independently in the serial feedback task and as combined processes in the multitasking scenario of the conventional parallel feedback task., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Dewiputri, Schweizer and Auer.)

Neurofeedback training has been shown to influence behavior in healthy participants as well as to alleviate clinical symptoms in neurological, psychosomatic, and psychiatric patient populations. However, many real-time fMRI neurofeedback studies report large inter-individual differences in learning success. The factors that cause this vast variability between participants remain unknown and their identification could enhance treatment success. Thus, here we employed a meta-analytic approach including data from 24 different neurofeedback studies with a total of 401 participants, including 140 patients, to determine whether levels of activity in target brain regions during pretraining functional localizer or no-feedback runs (i.e., self-regulation in the absence of neurofeedback) could predict neurofeedback learning success. We observed a slightly positive correlation between pretraining activity levels during a functional localizer run and neurofeedback learning success, but we were not able to identify common brain-based success predictors across our diverse cohort of studies. Therefore, advances need to be made in finding robust models and measures of general neurofeedback learning, and in increasing the current study database to allow for investigating further factors that might influence neurofeedback learning., (© 2020 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)

Neurofeedback has begun to attract the attention and scrutiny of the scientific and medical mainstream. Here, neurofeedback researchers present a consensus-derived checklist that aims to improve the reporting and experimental design standards in the field., (© The Author(s) (2020). Published by Oxford University Press on behalf of the Guarantors of Brain.)

In recent years, deep learning (DL) has become more widespread in the fields of cognitive and clinical neuroimaging. Using deep neural network models to process neuroimaging data is an efficient method to classify brain disorders and identify individuals who are at increased risk of age-related cognitive decline and neurodegenerative disease. Here we investigated, for the first time, whether structural brain imaging and DL can be used for predicting a physical trait that is of significant clinical relevance-the body mass index (BMI) of the individual. We show that individual BMI can be accurately predicted using a deep convolutional neural network (CNN) and a single structural magnetic resonance imaging (MRI) brain scan along with information about age and sex. Localization maps computed for the CNN highlighted several brain structures that strongly contributed to BMI prediction, including the caudate nucleus and the amygdala. Comparison to the results obtained via a standard automatic brain segmentation method revealed that the CNN-based visualization approach yielded complementary evidence regarding the relationship between brain structure and BMI. Taken together, our results imply that predicting BMI from structural brain scans using DL represents a promising approach to investigate the relationship between brain morphological variability and individual differences in body weight and provide a new scope for future investigations regarding the potential clinical utility of brain-predicted BMI., (Copyright © 2020 Vakli, Deák-Meszlényi, Auer and Vidnyánszky.)

Neurofeedback (NFB) allows subjects to learn self-regulation of neuronal brain activation based on information about the ongoing activation. The implementation of real-time functional magnetic resonance imaging (rt-fMRI) for NFB training now facilitates the investigation into underlying processes. Our study involved 16 control and 16 training right-handed subjects, the latter performing an extensive rt-fMRI NFB training using motor imagery. A previous analysis focused on the targeted primary somato-motor cortex (SMC). The present study extends the analysis to the supplementary motor area (SMA), the next higher brain area within the hierarchy of the motor system. We also examined transfer-related functional connectivity using a whole-volume psycho-physiological interaction (PPI) analysis to reveal brain areas associated with learning. The ROI analysis of the pre- and post-training fMRI data for motor imagery without NFB (transfer) resulted in a significant training-specific increase in the SMA. It could also be shown that the contralateral SMA exhibited a larger increase than the ipsilateral SMA in the training and the transfer runs, and that the right-hand training elicited a larger increase in the transfer runs than the left-hand training. The PPI analysis revealed a training-specific increase in transfer-related functional connectivity between the left SMA and frontal areas as well as the anterior midcingulate cortex (aMCC) for right- and left-hand trainings. Moreover, the transfer success was related with training-specific increase in functional connectivity between the left SMA and the target area SMC. Our study demonstrates that NFB training increases functional connectivity with non-targeted brain areas. These are associated with the training strategy (i.e., SMA) as well as with learning the NFB skill (i.e., aMCC and frontal areas). This detailed description of both the system to be trained and the areas involved in learning can provide valuable information for further optimization of NFB trainings., (Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Purpose: Neuroimaging studies in persistent developmental stuttering repeatedly report altered basal ganglia functions. Together with thalamus and cerebellum, these structures mediate sensorimotor functions and thus represent a plausible link between stuttering and neuroanatomy. However, stuttering is a complex, multifactorial disorder. Besides sensorimotor functions, emotional and social-motivational factors constitute major aspects of the disorder. Here, we investigated cortical and subcortical gray matter regions to study whether persistent developmental stuttering is also linked to alterations of limbic structures., Methods: The study included 33 right-handed participants who stutter and 34 right-handed control participants matched for sex, age, and education. Structural images were acquired using magnetic resonance imaging to estimate volumetric characteristics of the nucleus accumbens, hippocampus, amygdala, pallidum, putamen, caudate nucleus, and thalamus., Results: Volumetric comparisons and vertex-based shape comparisons revealed structural differences. The right nucleus accumbens was larger in participants who stutter compared to controls., Conclusion: Recent theories of basal ganglia functions suggest that the nucleus accumbens is a motivation-to-movement interface. A speaker intends to reach communicative goals, but stuttering can derail these efforts. It is therefore highly plausible to find alterations in the motivation-to-movement interface in stuttering. While behavioral studies of stuttering sought to find links between the limbic and sensorimotor system, we provide the first neuroimaging evidence of alterations in the limbic system. Thus, our findings might initialize a unified neurobiological framework of persistent developmental stuttering that integrates sensorimotor and social-motivational neuroanatomical circuitries., (Copyright © 2017 Elsevier Inc. All rights reserved.)

Background: Neurofeedback training (NFT) to decrease the theta/beta ratio (TBR) has been used for treating hyperactivity and impulsivity in attention deficit hyperactivity disorder (ADHD); however, often with low efficiency. Individual variance in EEG profile can confound NFT, because it may lead to influencing non-relevant activity, if ignored. More importantly, it may lead to influencing ADHD-related activities adversely, which may even result in worsening ADHD symptoms. Electromyogenic (EMG) signal resulted from forehead muscles can also explain the low efficiency of the NFT in ADHD from both practical and psychological point-of-view. The first aim of this study was to determine EEG and EMG biomarkers most related to the main ADHD characteristics, such as impulsivity and hyperactivity. The second aim was to confirm our hypothesis that the efficiency of the TBR NFT can be increased by individual adjustment of the frequency bands and simultaneous training on forehead muscle tension. Methods: We recruited 94 children diagnosed with ADHD (ADHD) and 23 healthy controls (HC). All participants were male and aged between six and nine. Impulsivity and attention were assessed with Go/no-Go task and delayed gratification task, respectively; and 19-channel EEG and forehead EMG were recorded. Then, the ADHD group was randomly subdivided into (1) standard, (2) individualized, (3) individualized+EMG, and (4) sham NFT (control) groups. The groups were compared based on TBR and EEG alpha activity, as well as hyperactivity and impulsivity three times: pre-NFT, post-NFT and 6 months after the NFT (follow-up). Results: ADHD children were characterized with decreased individual alpha peak frequency, alpha bandwidth and alpha amplitude suppression magnitude, as well as with increased alpha1/alpha2 (a1/a2) ratio and scalp muscle tension when c (η 2 ≥ 0.212). All contingent TBR NFT groups exhibited significant NFT-related decrease in TBR not evident in the control group. Moreover, we detected a higher overall alpha activity in the individualized but not in the standard NFT group. Mixed MANOVA considering between-subject factor GROUP and within-subject factor TIME showed that the individualized+EMG group exhibited the highest level of clinical improvement, which was associated with increase in the individual alpha activity at the 6 months follow-up when comparing with the other approaches (post hoc t = 3.456, p = 0.011). Conclusions: This study identified various (adjusted) alpha activity metrics as biomarkers with close relationship with ADHD symptoms, and demonstrated that TBR NFT individually adjusted for variances in alpha activity is more successful and clinically more efficient than standard, non-individualized NFT. Moreover, these training effects of the individualized TBR NFT lasted longer when combined with EMG.

Persistent developmental stuttering is associated with basal ganglia dysfunction or dopamine dysregulation. Here, we studied whole-brain functional connectivity to test how basal ganglia structures coordinate and reorganize sensorimotor brain networks in stuttering. To this end, adults who stutter and fluent speakers (control participants) performed a response anticipation paradigm in the MRI scanner. The preparation of a manual Go/No-Go response reliably produced activity in the basal ganglia and thalamus and particularly in the substantia nigra. Strikingly, in adults who stutter, substantia nigra activity correlated positively with stuttering severity. Furthermore, functional connectivity analyses yielded altered task-related network formations in adults who stutter compared to fluent speakers. Specifically, in adults who stutter, the globus pallidus and the thalamus showed increased network synchronization with the inferior frontal gyrus. This implies dynamic shifts in the response preparation-related network organization through the basal ganglia in the context of a non-speech motor task in stuttering. Here we discuss current findings in the traditional framework of how D1 and D2 receptor activity shapes focused movement selection, thereby suggesting a disproportional involvement of the direct and the indirect pathway in stuttering.

The rate of progress in human neurosciences is limited by the inability to easily apply a wide range of analysis methods to the plethora of different datasets acquired in labs around the world. In this work, we introduce a framework for creating, testing, versioning and archiving portable applications for analyzing neuroimaging data organized and described in compliance with the Brain Imaging Data Structure (BIDS). The portability of these applications (BIDS Apps) is achieved by using container technologies that encapsulate all binary and other dependencies in one convenient package. BIDS Apps run on all three major operating systems with no need for complex setup and configuration and thanks to the comprehensiveness of the BIDS standard they require little manual user input. Previous containerized data processing solutions were limited to single user environments and not compatible with most multi-tenant High Performance Computing systems. BIDS Apps overcome this limitation by taking advantage of the Singularity container technology. As a proof of concept, this work is accompanied by 22 ready to use BIDS Apps, packaging a diverse set of commonly used neuroimaging algorithms.

This paper describes the data repository for the Cambridge Centre for Ageing and Neuroscience (Cam-CAN) initial study cohort. The Cam-CAN Stage 2 repository contains multi-modal (MRI, MEG, and cognitive-behavioural) data from a large (approximately N=700), cross-sectional adult lifespan (18-87years old) population-based sample. The study is designed to characterise age-related changes in cognition and brain structure and function, and to uncover the neurocognitive mechanisms that support healthy cognitive ageing. The database contains raw and preprocessed structural MRI, functional MRI (active tasks and resting state), and MEG data (active tasks and resting state), as well as derived scores from cognitive behavioural experiments spanning five broad domains (attention, emotion, action, language, and memory), and demographic and neuropsychological data. The dataset thus provides a depth of neurocognitive phenotyping that is currently unparalleled, enabling integrative analyses of age-related changes in brain structure, brain function, and cognition, and providing a testbed for novel analyses of multi-modal neuroimaging data., (Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.)

Only a tiny fraction of the data and metadata produced by an fMRI study is finally conveyed to the community. This lack of transparency not only hinders the reproducibility of neuroimaging results but also impairs future meta-analyses. In this work we introduce NIDM-Results, a format specification providing a machine-readable description of neuroimaging statistical results along with key image data summarising the experiment. NIDM-Results provides a unified representation of mass univariate analyses including a level of detail consistent with available best practices. This standardized representation allows authors to relay methods and results in a platform-independent regularized format that is not tied to a particular neuroimaging software package. Tools are available to export NIDM-Result graphs and associated files from the widely used SPM and FSL software packages, and the NeuroVault repository can import NIDM-Results archives. The specification is publically available at: http://nidm.nidash.org/specs/nidm-results.html., Competing Interests: The authors declare no competing financial interests.

The development of magnetic resonance imaging (MRI) techniques has defined modern neuroimaging. Since its inception, tens of thousands of studies using techniques such as functional MRI and diffusion weighted imaging have allowed for the non-invasive study of the brain. Despite the fact that MRI is routinely used to obtain data for neuroscience research, there has been no widely adopted standard for organizing and describing the data collected in an imaging experiment. This renders sharing and reusing data (within or between labs) difficult if not impossible and unnecessarily complicates the application of automatic pipelines and quality assurance protocols. To solve this problem, we have developed the Brain Imaging Data Structure (BIDS), a standard for organizing and describing MRI datasets. The BIDS standard uses file formats compatible with existing software, unifies the majority of practices already common in the field, and captures the metadata necessary for most common data processing operations., Competing Interests: The authors declare no competing financial interests.

Psychophysical sensitivity to red-green chromatic modulation decreases with visual eccentricity, compared to sensitivity to luminance modulation, even after appropriate stimulus scaling. This is likely to occur at a central, rather than a retinal, site. Blood-oxygenation-level-dependent (BOLD) functional magnetic resonance imaging (fMRI) responses to stimuli designed to separately stimulate different afferent channels' [red-green, luminance, and short-wavelength (S)-cone] circular gratings were recorded as a function of visual eccentricity (±10  deg) and spatial frequency (SF) in human primary visual cortex (V1) and further visual areas (V2v, V3v). In V1, the SF tuning of BOLD fMRI responses became coarser with eccentricity. For red-green and luminance gratings, similar SF tuning curves were found at all eccentricities. The pattern for S-cone modulation differed, with SF tuning changing more slowly with eccentricity than for the other two modalities. This may be due to the different retinal distribution with eccentricity of this receptor type. A similar pattern held in V2v and V3v. This would suggest that transformation or spatial filtering of the chromatic (red-green) signal occurs beyond these areas.

In humans, both language and fine motor skills are associated with left-hemisphere specialization, whereas visuospatial skills are associated with right-hemisphere specialization. Individuals with autism spectrum conditions (ASC) show a profile of deficits and strengths that involves these lateralized cognitive functions. Here we test the hypothesis that regions implicated in these functions are atypically rightward lateralized in individuals with ASC and, that such atypicality is associated with functional performance. Participants included 67 male, right-handed adults with ASC and 69 age- and IQ-matched neurotypical males. We assessed group differences in structural asymmetries in cortical regions of interest with voxel-based analysis of grey matter volumes, followed by correlational analyses with measures of language, motor and visuospatial skills. We found stronger rightward lateralization within the inferior parietal lobule and reduced leftward lateralization extending along the auditory cortex comprising the planum temporale, Heschl's gyrus, posterior supramarginal gyrus, and parietal operculum, which was more pronounced in ASC individuals with delayed language onset compared to those without. Planned correlational analyses showed that for individuals with ASC, reduced leftward asymmetry in the auditory region was associated with more childhood social reciprocity difficulties. We conclude that atypical cerebral structural asymmetry is a potential candidate neurophenotype of ASC., (© 2015 Wiley Periodicals, Inc.)

This study investigated the level of self-regulation of the somatomotor cortices (SMCs) attained by an extended functional magnetic resonance imaging (fMRI) neurofeedback training. Sixteen healthy subjects performed 12 real-time functional magnetic resonance imaging neurofeedback training sessions within 4 weeks, involving motor imagery of the dominant right as well as the non-dominant left hand. Target regions of interests in the SMC were individually localized prior to the training by overt finger movements. The feedback signal (FS) was defined as the difference between fMRI activation in the contra- and ipsilateral SMC and visually presented to the subjects. Training efficiency was determined by an off-line general linear model analysis determining the fMRI percent signal changes in the SMC target areas accomplished during the neurofeedback training. Transfer success was assessed by comparing the pre- and post-training transfer task, i.e., the neurofeedback paradigm without the presentation of the FS. Group results show a distinct increase in feedback performance (FP) in the transfer task for the trained group compared to a matched untrained control group, as well as an increase in the time course of the training, indicating an efficient training and a successful transfer. Individual analysis revealed that the training efficiency was not only highly correlated to the transfer success but also predictive. Trainings with at least 12 efficient training runs were associated with a successful transfer outcome. A group analysis of the hemispheric contributions to the FP showed that it is mainly driven by increased fMRI activation in the contralateral SMC, although some individuals relied on ipsilateral deactivation. Training and transfer results showed no difference between left- and right-hand imagery, with a slight indication of more ipsilateral deactivation in the early right-hand trainings.

Resting-state studies conducted with stroke patients are scarce. The study of brain activity and connectivity at rest provides a unique opportunity for the investigation of brain rewiring after stroke and plasticity changes. This study sought to identify dynamic changes in the functional organization of the default mode network (DMN) of stroke patients at three months after stroke. Eleven patients (eight male and three female; age range: 48-72) with right cortical and subcortical ischemic infarctions and 17 controls (eleven males and six females; age range: 57-69) were assessed by neurological and neuropsychological examinations and scanned with resting-state functional magnetic ressonance imaging. First, we explored group differences in functional activity within the DMN by means of probabilistic independent component analysis followed by a dual regression approach. Second, we estimated functional connectivity between 11 DMN nodes both locally by means of seed-based connectivity analysis, as well as globally by means of graph-computation analysis. We found that patients had greater DMN activity in the left precuneus and the left anterior cingulate gyrus when compared with healthy controls (P < 0.05 family-wise error corrected). Seed-based connectivity analysis showed that stroke patients had significant impairment (P = 0.014; threshold = 2.00) in the connectivity between the following five DMN nodes: left superior frontal gyrus (lSFG) and posterior cingulate cortex (t = 2.01); left parahippocampal gyrus and right superior frontal gyrus (t = 2.11); left parahippocampal gyrus and lSFG (t = 2.39); right parietal and lSFG (t = 2.29). Finally, mean path length obtained from graph-computation analysis showed positive correlations with semantic fluency test (r(s)  = 0.454; P = 0.023), phonetic fluency test (r(s)  = 0.523; P = 0.007) and the mini mental state examination (r(s)  = 0.528; P = 0.007). In conclusion, the ability to regulate activity of the DMN appears to be a central part of normal brain function in stroke patients. Our study expands the understanding of the changes occurring in the brain after stroke providing a new avenue for investigating lesion-induced network plasticity., (© 2014 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.)

Recent years have seen neuroimaging data sets becoming richer, with larger cohorts of participants, a greater variety of acquisition techniques, and increasingly complex analyses. These advances have made data analysis pipelines complicated to set up and run (increasing the risk of human error) and time consuming to execute (restricting what analyses are attempted). Here we present an open-source framework, automatic analysis (aa), to address these concerns. Human efficiency is increased by making code modular and reusable, and managing its execution with a processing engine that tracks what has been completed and what needs to be (re)done. Analysis is accelerated by optional parallel processing of independent tasks on cluster or cloud computing resources. A pipeline comprises a series of modules that each perform a specific task. The processing engine keeps track of the data, calculating a map of upstream and downstream dependencies for each module. Existing modules are available for many analysis tasks, such as SPM-based fMRI preprocessing, individual and group level statistics, voxel-based morphometry, tractography, and multi-voxel pattern analyses (MVPA). However, aa also allows for full customization, and encourages efficient management of code: new modules may be written with only a small code overhead. aa has been used by more than 50 researchers in hundreds of neuroimaging studies comprising thousands of subjects. It has been found to be robust, fast, and efficient, for simple-single subject studies up to multimodal pipelines on hundreds of subjects. It is attractive to both novice and experienced users. aa can reduce the amount of time neuroimaging laboratories spend performing analyses and reduce errors, expanding the range of scientific questions it is practical to address.

To elucidate basic mechanisms underlying neurofeedback we investigated neural mechanisms of training of slow cortical potentials (SCPs) by considering EEG- and fMRI. Additionally, we analyzed the feasibility of a double-blind, placebo-controlled design in NF research based on regulation performance during treatment sessions and self-assessment of the participants. Twenty healthy adults participated in 16 sessions of SCPs training: 9 participants received regular SCP training, 11 participants received sham feedback. At three time points (pre, intermediate, post) fMRI and EEG/ERP-measurements were conducted during a continuous performance test (CPT). Performance-data during the sessions (regulation performance) in the treatment group and the placebo group were analyzed. Analysis of EEG-activity revealed in the SCP group a strong enhancement of the CNV (electrode Cz) at the intermediate assessment, followed by a decrease back to baseline at the post-treatment assessment. In contrast, in the placebo group a continuous but smaller increase of the CNV could be obtained from pre to post assessment. The increase of the CNV in the SCP group at intermediate testing was superior to the enhancement in the placebo group. The changes of the CNV were accompanied by a continuous improvement in the test performance of the CPT from pre to intermediate to post assessment comparable in both groups. The change of the CNV in the SCP group is interpreted as an indicator of neural plasticity and efficiency while an increase of the CNV in the placebo group might reflect learning and improved timing due to the frequent task repetition. In the fMRI analysis evidence was obtained for neuronal plasticity. After regular SCP neurofeedback activation in the posterior parietal cortex decreased from the pre- to the intermediate measurement and increased again in the post measurement, inversely following the U-shaped increase and decrease of the tCNV EEG amplitude in the SCP-trained group. Furthermore, we found a localized increase of activity in the anterior cingulate cortex (ACC). Analyses of the estimation of treatment assignment by the participants indicate feasibility of blinding. Participants could not assess treatment assignment confidently. Participants of the SCP-group improved regulation capability during treatment sessions (in contrast to the participants of the placebo-group), although regulation capability appeared to be instable, presumably due to diminished confidence in the training (SCP- or sham-training). Our results indicate that SCP training in healthy adults might lead to functional changes in neuronal circuits serving cognitive preparation even after a limited number of sessions.

After stroke, white matter integrity can be affected both locally and distally to the primary lesion location. It has been shown that tract disruption in mirror's regions of the contralateral hemisphere is associated with degree of functional impairment. Fourteen patients suffering right hemispheric focal stroke (S) and eighteen healthy controls (HC) underwent Diffusion Weighted Imaging (DWI) and neuropsychological assessment. The stroke patient group was divided into poor (SP; n = 8) and good (SG; n = 6) cognitive recovery groups according to their cognitive improvement from the acute phase (72 hours after stroke) to the subacute phase (3 months post-stroke). Whole-brain DWI data analysis was performed by computing Diffusion Tensor Imaging (DTI) followed by Tract Based Spatial Statistics (TBSS). Assessment of effects was obtained computing the correlation of the projections on TBSS skeleton of Fractional Anisotropy (FA) and Radial Diffusivity (RD) with cognitive test results. Significant decrease of FA was found only in right brain anatomical areas for the S group when compared to the HC group. Analyzed separately, stroke patients with poor cognitive recovery showed additional significant FA decrease in several left hemisphere regions; whereas SG patients showed significant decrease only in the left genu of corpus callosum when compared to the HC. For the SG group, whole brain analysis revealed significant correlation between the performance in the Semantic Fluency test and the FA in the right hemisphere as well as between the performance in the Grooved Pegboard Test (GPT) and the Trail Making Test-part A and the FA in the left hemisphere. For the SP group, correlation analysis revealed significant correlation between the performance in the GPT and the FA in the right hemisphere.

Neurofeedback (NFB) allows subjects to learn how to volitionally influence the neuronal activation in the brain by employing real-time neural activity as feedback. NFB has already been performed with electroencephalography (EEG) since the 1970s. Functional MRI (fMRI), offering a higher spatial resolution, has further increased the spatial specificity. In this paper, we briefly outline the general principles behind NFB, the implementation of fMRI-NFB studies, the feasibility of fMRI-NFB, and the application of NFB as a supplementary therapy tool.

Most people are left-hemisphere dominant for language. However the neuroanatomy of language lateralization is not fully understood. By combining functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI), we studied whether language lateralization is associated with cerebral white-matter (WM) microstructure. Sixteen healthy, left-handed women aged 20-25 were included in the study. Left-handers were targeted in order to increase the chances of involving subjects with atypical language lateralization. Language lateralization was determined by fMRI using a verbal fluency paradigm. Tract-based spatial statistics analysis of DTI data was applied to test for WM microstructural correlates of language lateralization across the whole brain. Fractional anisotropy and mean diffusivity were used as indicators of WM microstructural organization. Right-hemispheric language dominance was associated with reduced microstructural integrity of the left superior longitudinal fasciculus and left-sided parietal lobe WM. In left-handed women, reduced integrity of the left-sided language related tracts may be closely linked to the development of right hemispheric language dominance. Our results may offer new insights into language lateralization and structure-function relationships in human language system., (Copyright © 2013 Elsevier Inc. All rights reserved.)

Objectives: Current thresholding strategies for the analysis of functional MRI (fMRI) datasets may suffer from specific limitations (e.g. with respect to the required smoothness) or lead to reduced performance for a low signal-to-noise ratio (SNR). Although a previously proposed two-threshold (TT) method offers a promising solution to these problems, the use of preset settings limits its performance. This work presents an optimised TT approach that estimates the required parameters in an iterative manner., Methods: The iterative TT (iTT) method is compared with the original TT method, as well as other established voxel-based and cluster-based thresholding approaches and spatial mixture modelling (SMM) for both simulated data and fMRI of a hometown walking task at different experimental settings (spatial resolution, filtering and SNR)., Results: In general, the iTT method presents with remarkable sensitivity and good specificity that outperforms all conventional approaches tested except for SMM in a few cases. This also holds true for challenging conditions such as high spatial resolution, the absence of filtering, high noise level, or a low number of task repetitions., Conclusion: Thus, iTT emerges as a good candidate for both scientific fMRI studies at high spatial resolution and more routine applications for clinical purposes.

Psychophysical sensitivity to isoluminant chromatic modulation declines at temporal frequencies beyond 4 Hz, whereas chromatically opponent cells of the afferent visual pathway (long- to middle-wavelength (L-M) cone-opponent or short-wavelength (S) cone cells) show responses at much higher temporal frequencies, indicating a central limitation in temporal processing capacity. Here, we sought to localize this limit in cortical retinotopic visual areas. We used fMRI to investigate responses of lateral geniculate nucleus and cortical visual areas in humans to isoluminant chromatic modulation as a function of temporal frequency (2-12 Hz). Our results suggest that L-M cone-opponent and S-cone signals are processed in LGN up to 12 Hz. In all visual areas except MT (middle temporal) and V3a, S-cone responses declined steeply with temporal frequency, implying that psychophysical sensitivity loss to blue-yellow modulation might occur early within these areas. While V1 showed robust L-M responses up to 12 Hz, there was a progressive falloff of responses with temporal frequency as information is transferred from V1 to higher areas (V2, V3, and V4), suggesting that, in humans, temporal limitation in perception of red-green chromatic modulation likely results from limited processing capacity of higher ventral extrastriate areas.

The authors report functional magnetic resonance imaging (fMRI) study data of a 60-year-old patient having short-lasting unilateral neuralgiform headache attacks with conjunctival injection and tearing (SUNCT) syndrome. Three consecutive pain attacks were detected during the imaging session and strong brainstem activation was found. It was concluded that the brainstem can be involved in the pain signal transmission in SUNCT syndrome.

Objective: The cause or the physiological role of déjà vu (DV) in healthy people is unknown. The pathophysiology of DV-type epileptic aura is also unresolved. Here we describe a 22-year-old woman treated with deep brain stimulation (DBS) of the left internal globus pallidus for hemidystonia. At certain stimulation settings, DBS elicited reproducible episodes of DV., Methods: Neuropsychological tests and single-photon-emission computed tomography (SPECT) were performed during DBS-evoked DV and during normal DBS stimulation without DV., Results: SPECT during DBS-evoked DV revealed hyperperfusion of the right (contralateral to the electrode) hippocampus and other limbic structures. Neuropsychological examinations performed during several evoked DV episodes revealed disturbances in nonverbal memory., Conclusion: Our results confirm the role of mesiotemporal structures in the pathogenesis of DV. We hypothesize that individual neuroanatomy and disturbances in gamma oscillations or in the dopaminergic system played a role in DBS-elicited DV in our patient.

Objective: Right-handedness and left-sided language lateralization is an unresolved mystery with unknown cause/effect relations. Most studies suggest that the language lateralization is related to a fundamental brain asymmetry: right-handedness may be secondary. We analyzed the possibility of an opposite cause/effect relation: whether asymmetric hand usage (as a cause) can influence language lateralization (as a consequence)., Methods: We determined language lateralization by functional magnetic resonance imaging in 15 subjects whose upper limb (UL) had been injured at birth because of unilateral damage of the brachial plexus. These subjects were able to use only one (the noninjured) UL perfectly., Results: We found correlation between the severity of right-sided UL injuries and hand usage dysfunction and the degree of left-to-right shift of language lateralization. There was, however, not a complete switch of language lateralization., Interpretation: Right-sided UL injury can induce a left-to-right shift in language lateralization, suggesting that hand usage can influence language lateralization. These findings may contradict the broadly accepted theory that right-handedness is a secondary phenomenon caused by left-sided hemispheric language lateralization. However, the cause/effect problem between asymmetric hand usage and language lateralization is not resolved in this study. Our findings may support the theory that gestures had a crucial role in human language evolution and is a part of the language system even today.

This study aimed to broaden the diagnostic possibilities of low-field MRI systems (i) by examining the feasibility of functional MRI of human brain activation at 1 T, and (ii) by assessing its reliability in comparison with acquisitions at 3 T. Eight subjects were studied at 1 T and 3T using standard echo-planar-imaging sequences at 3-mm isotropic spatial resolution. Paradigms included silent word generation, sequential finger-to-thumb opposition, and passive finger movements. Image post-processing was carried out either with statistical parametric mapping (SPM5, single-subject and group analysis) or with a two-threshold correlation (TTC, single-subject analysis only) analysis. Single-subject analysis with SPM5 resulted in 3-5 times more activated pixels at 3 T than at 1 T in the examined Broca and sensorimotor regions. By comparison, the TTC single-subject analysis yielded the same amount of activated pixels at 3 T and 1 T. Moreover, this number was identical to that obtained with SPM at 3 T. The group analysis with SPM5 resulted in very similar numbers of activated pixels at both field strengths. The present findings suggest that a field strength of 1 T combined with adequate post-processing allows for reliable functional MRI studies of human brain activation. High-field advantages are therefore best invested in higher spatial resolution.

We aimed to determine the functional localisation of right-left discrimination (RLD) by functional MRI (fMRI). In this study, 16 male volunteers were examined. There were three task sessions: one active and two baseline tasks. During the baseline tasks participants were instructed to show numbers with their fingers. The first baseline task was performed with the right hand, the second one with the left hand. During the active (RLD) task participants were also instructed to show numbers. The difference between baseline and active tasks was that during the active task the hand with which the participant should perform the instruction was assigned randomly. Thus, participants were unaware which hand should be used before the instruction command. During RLD, activations occurred in the right-sided frontal, precuneus, postcentral, angular, lingual, and superior temporal gyri. Activations also appeared in the left-sided temporal gyri and precuneus. Of the activations, 76.7% appeared in the right hemisphere, 23.3% in the left hemisphere. Conclusively, we found that RLD is mainly related to the right hemisphere, and requires activation of the parieto-temporo-occipital junction and the visual system including cuneus, precuneus, and gyrus lingualis.

Summary Background: It is unclear whether the hippocampal abnormality in temporal lobe epilepsy (TLE) is a consequence or the cause of afebrile or febrile seizures (FSs). We investigated whether hippocampal abnormalities are present in healthy adults>15 years after a simple FS., Methods: Eight healthy subjects (5 men) with a history of simple FS (FS+ group) and eight sex- and aged-matched control subjects (FS- group) were investigated by three MR methods: blinded visual inspection of the MRI pictures; automatic voxel-based volumetry; and T2 relaxation time measurements., Results: The mean total volume of the two hippocampi was 5.36 +/- 1.33 cm(3)in the FS+ group and 6.63 +/- 1.46 cm(3)in the FS- group (p = 0.069). The T2 values in the anterior part of the left hippocampus (p = 0.036) and in the middle part of the right hippocampus (p = 0.025) were elevated in the FS+ subjects. The mean volume of the right hippocampus was 3.05 +/- 0.8 cm(3)in the FS+ men and 4.05 +/- 0.48 cm(3)in the FS- men (p = 0.043). The mean total volume of the two hippocampi was 5.38 +/- 1.4 cm(3)in the FS+ men and 7.48 +/- 1.14 cm(3)in the FS- men (p = 0.043). There were three FS+ men in whom hippocampal abnormalities including hippocampal sclerosis (HS) and dysgenesis were observed on visual inspection., Conclusions: A history of simple FS in childhood can be associated with hippocampal abnormalities in adults. These abnormalities are probably more pronounced in men. Simple FS may not be as a benign event as previously thought. Our findings suggest that hippocampal abnormalities associated with FS are not necessarily epileptogenic.

Purpose: To provide a straightforward method for metabolite quantitation in the brain. Tissue water concentration can be determined in a voxel by measuring T(1) and it may provide an internal reference for the calculation of the metabolite concentrations., Materials and Methods: Water-suppressed stimulated echo acquisition mode spectra were obtained at 1.5T, and the tissue water content was calculated from T(1)., Results: The calculated water content values demonstrated very good agreement with literature data. Metabolite concentrations (mmol/liter) in the gray and white matter: N-acetyl-aspartate = 14.02 +/- 1.93, creatine = 9.98 +/- 1.03, and choline = 1.14 +/- 0.24; N-acetyl-aspartate = 11.08 +/- 2.24, creatine = 7.83 +/- 0.66, and choline = 2.05 +/- 0.38, respectively., Conclusion: The water content calculated from T(1) can yield an internal reference in MR spectroscopy, and the accurate measurement of metabolite concentrations is feasible. The proposed method is simple and can readily be applied in any MR center without the need for complicated corrections or calibration procedures., ((c) 2008 Wiley-Liss, Inc.)

Aim: To visualize by ictal, functional MRI, the initial haemodynamic change (i.e. putative seizure-onset zone) and subsequent seizure spread during an epileptic seizure., Methods: A 20-year-old woman was investigated during a simple partial seizure consisting of right-sided mouth clonus. An internal reference curve, correlated with signal change pixelwise, was applied to obtain correlation coefficient maps. The reference curve was shifted scan by scan to examine the correlation at each time point. To demonstrate seizure onset and propagation, a lag time map was produced showing the temporal sequence of activation in various brain regions., Results: fMRI analysis showed that the lower part of the insular cortex was activated first, and its signal alteration preceded the clinical beginning of the seizure (i.e. mouth clonus) by more than one minute. Most of the activations started before clinical seizure onset. The activation corresponding to the motor area of the right face showed only a 7.5 second-long, pre-ictal phase. BOLD signal alterations were also observed in the left caudate nucleus, left thalamus, along with various areas of the left cerebral and cerebellum hemispheres., Conclusions: The present study demonstrates a whole-brain activity simultaneously in time and space, during an epileptic seizure. Our results also support the existence of the pre-ictal state in epilepsy. Replication of our results would be of major interest for presurgical evaluation of patients with drug-resistant epilepsy.

Background: Sudden death appears in 8-17% of epilepsy patients non-responding to antiepileptic therapy. Some studies suggest that the most common cause of death is seizure-related cardiac arrhythmia., Aim of Study: To analyze the alteration of the heart rate six hours before and after the seizures., Methods: Eighteen patients suffering from focal epilepsy were examined before epilepsy surgery. Video-EEG-ECG was carried out for 2-10 days, and 32 seizures were registered. Analysis of the heart rate was based on the 5-minute-long epochs of the ECGs taken at the 5-10-15-30th minutes and at the 1-3-6th hours before and after seizures., Results: The heart rate increases (from an average of 69 beats/min to 92 beats/min, p<0.001) immediately after seizures, though significantly higher heart rate was observed 3 hours after seizures. There were no patients with severe peri-ictal bradycardia. In one of our patients, ectopic cardiac rhythm occurred after a generalized tonic-clonic seizure., Conclusions: We can conclude that the sympathetic activity increases while the parasympathetic activity decreases after seizures. The observed alterations lasted for a long time and predict fatal arrhythmias. These suggest that sudden death in epilepsy can be induced by cardiac arrhythmias connected with epileptic seizures.

The present contribution discusses the clinical use of functional MRI (fMRI) and its role in the most common neurological diseases. FMRI was found a reliable and reproducible examination tool resulting in a wide distribution of fMRI methods in presurgical evaluation of epilepsy in determining the relationship of eloquent areas and the epileptic focus. Preliminary data suggest that fMRI using memory paradigms can predict the postoperative memory decline in epilepsy surgery by determining whether a reorganization of memory functions took place. Speech-activated fMRI became the most used tool in determining hemispheric dominance. Visual and sensory-motor cortex can also be routinely investigated by fMRI which helps in decision on epilepsy surgery. FMRI combined with EEG is a new diagnostic tool in epilepsy and sleep disorders. FMRI can identify the penumbra after stroke and can provide an additional information on metabolic state of the threatened brain tissue. FMRI has a predictive role in post-stroke recovery. In relapsing-remitting MS an adaptive reorganization can be demonstrated by fMRI affecting the visual, motor, and memory systems, despite preserved functional performance. Much more extensive reorganization can be demonstrated in secondary progressive MS. These findings suggest that the different stages of MS are related to different stages of the reorganization and MS becomes progressive when there is no more reserve capacity in the brain for reorganization. FMRI offers the capability of detecting early functional hemodynamic alterations in Alzheimer's disease before morphological changes. FMRI can be a valuable tool to test and monitor treatment efficacy in AD. FMRI can also provide information about the mechanisms of different therapeutic approaches in Parkinson disorder including drug treatment and deep brain stimulation.

This work presents a new group analysis for functional MRI of human brain activation. The two-threshold correlation (TTC) method determines two statistical thresholds by estimating the noise distribution underlying the summed histogram of correlation coefficients (CC) from all sections and subjects. The probabilistic CC thresholds (p<0.0001 for the identification of highly significant activation centers and p<0.05 for limiting the iterative addition of directly neighboring voxels to these centers) are applied to the group CC maps for each section. These maps may be reconstructed by taking the maximum (MAX) or mean (MEAN) CC value of all subjects for a particular voxel. Experimental analyses involved functional echo-planar imaging of sequential finger-to-thumb opposition and silent word generation at 3T (eight subjects). Preprocessing included motion correction, spatial filtering, and normalization to MNI space. While the results for the TTC MAX approach were very similar to those obtained for a standard SPM analysis, the TTC MEAN approach turned out to be more conservative emphasizing voxels that are activated in most rather than in only a few subjects. The new method is simple, fast, and robust by linking two thresholds in a physiologically meaningful manner.

Aim of the Study: Presentation of diffusion tensor imaging (DTI) performed at low magnetic field (1 Tesla) in the algorithm of work-up of a patient suffering from severe traumatic brain injury (TBI)., Method: DTI and functional MRI (fMRI) were applied at 1 Tesla for visualization of neural pathways and examination of sensory functions of a patient with severe TBI. DTI-measurement was also performed on a healthy patient for comparison., Results: DTI acquired at low magnetic field yielded appropriate visualization of neural pathways. DTI confirmed the results of the clinical and fMRI examinations in the patient suffering from severe TBI., Conclusion: An optimized DTI can be useful in the examination of patients with TBI, moreover, it may also help in the establishment of diagnoses of other central nervous system diseases affecting neuronal pathways. The presented results suggest that DTI of appropriate quality can be performed at low magnetic field.

Juvenile myoclonic epilepsy (JME) typically begins at age 10-17 years. We present two patients, with no previous history of epileptic seizures, in whom JME began after the age of 70. The clinical picture of these patients did not differ from "typical" JME except for the patient's age and age at epilepsy-onset. We suggest that not only symptomatic epilepsy, but also some idiopathic epilepsies, can begin or can be reactivated in elderly people. This may be more evidence that susceptibility to epileptic seizures is increased after 60 years of age.

Aim of the Study: To perform functional MRI experiments at low magnetic field, and to set up routine protocol to help the planning of neurosurgical operations and the examination of epilepsy patients., Methods: An optimized 2D EPI sequence was applied to yield functional MR images in basic paradigms such as finger tapping and internal word generation. Further, activation was induced also by a task involving mental navigation based on the retrieval of individually familiar visuo-spatial knowledge., Results: Low resolution (matrix of 64x64) functional MR images satisfactorily visualized moto-sensor strip and speech centers. In the mental navigation task bilateral activation of the hippocampal formation was observed. Determination of motor area was also performed in an epilepsy patient whose seizure focus had been found in the area of pre- and post-central gyrus. The dislocation of the motor cortex was demonstrated., Conclusion: Functional MR images with fine quality can be obtained in basic paradigms even at low magnetic field if MR imaging parameters and paradigms are optimized.

Aim of the Study: Presentation of functional MRI performed at low magnetic field (1 Tesla) for planning microsurgical operation in a patient suffering from tumor close to an eloquent brain area., Methods: Microsurgical removal navigated by frameless stereotaxy of an intrinsic tumor located in eloquent area is indicated if speech function is not damaged, i.e. exact localisation and relationship of the tumor and speech area can be defined. Before operation an optimized EPI based 2D sequence was applied to yield functional MR images. At the planning of the operation the paradigm used for the localization of the sensory language cortex contained passive listening to a text. Control investigations were performed one month postoperatively. A specific psychological test, as an additional investigation to estimate the accurate level of the sensory language function, was also conducted., Results: Low resolution (matrix of 64x 64) functional MR images visualized sensory speech center and auditory cortex satisfactorily. The scans showed clearly that the Wernicke's region was situated just above the tumor (WHO grade II glioma), and this finding increased the safety of intraoperative localization and reduced the risk of morbidity. Control examinations revealed minimal decrease in sensory language function, however, it was not noticeable for either the patient or her surroundings., Conclusion: Optimized functional MR imaging performed at low magnetic field can support planning of neurosurgical operations and reduce the morbidity of microsurgical interventions.