Magnetic resonance imaging (MRI) techniques have been used to quantitatively assess focal and network abnormalities. Idiopathic generalized epilepsy (IGE) is characterized by bilateral synchronous spike-wave discharges on electroencephalography (EEG) but normal clinical MRI. Dysfunctions involving the neocortex, particularly the prefrontal cortex, and thalamus likely contribute to seizure activity. To identify possible morphometric and functional differences in the brains of IGE patients and normal controls, we employed measures of thalamic volumes, cortical thickness, gray-white blurring, fractional anisotropy (FA) measures from diffusion tensor imaging (DTI) and fractional amplitude of low frequency fluctuations (fALFF) in thalamic subregions from resting state functional MRI. Data from 27 patients with IGE and 27 age- and sex-matched controls showed similar thalamic volumes, cortical thickness and gray-white contrast. There were no differences in FA values on DTI in tracts connecting the thalamus and prefrontal cortex. Functional analysis revealed decreased fALFF in the prefrontal cortex (PFC) subregion of the thalamus in patients with IGE. We provide minimum detectable effect sizes for each measure used in the study. Our analysis indicates that fMRI-based methods are more sensitive than quantitative structural techniques for characterizing brain abnormalities in IGE.

Chorea is a hyperkinetic movement disorder consisting of involuntary irregular, flowing movements of the trunk, neck or face. Although Huntington's disease is the most common cause of chorea in adults, chorea can also result from many other neurodegenerative, metabolic, and autoimmune conditions. While the pathophysiology of these different conditions is quite variable, recent advances in functional imaging have enabled the development of new methods for analysis of brain activity and neuronal dysfunction. In this paper we review the growing body of functional imaging data that has been performed in chorea syndromes and identify particular trends, which can be used to better understand the underlying network changes within the basal ganglia. While it can be challenging to identify whether changes are primary, secondary, or compensatory, identification of these trends can ultimately be useful in diagnostic testing and treatment in many of the conditions that cause chorea.

Replicability and reproducibility of scientific findings is paramount for sustainable progress in neuroscience. Preregistration of the hypotheses and methods of an empirical study before analysis, the sharing of primary research data, and compliance with data standards such as the Brain Imaging Data Structure (BIDS), are considered effective practices to secure progress and to substantiate quality of research. We investigated the current level of adoption of open science practices in neuroimaging and the difficulties that prevent researchers from using them. Email invitations to participate in the survey were sent to addresses received through a PubMed search of human functional magnetic resonance imaging studies that were published between 2010 and 2020. 283 persons completed the questionnaire. Although half of the participants were experienced with preregistration, the willingness to preregister studies in the future was modest. The majority of participants had experience with the sharing of primary neuroimaging data. Most of the participants were interested in implementing a standardized data structure such as BIDS in their labs. Based on demographic variables, we compared participants on seven subscales, which had been generated through factor analysis. Exploratory analyses found that experienced researchers at lower career level had higher fear of being transparent and researchers with residence in the EU had a higher need for data governance. Additionally, researchers at medical faculties as compared to other university faculties reported a more unsupportive supervisor with regards to open science practices and a higher need for data governance. The results suggest growing adoption of open science practices but also highlight a number of important impediments.

Functional magnetic resonance imaging and electro-/magneto-encephalography are some of the main neuroimaging technologies used by cognitive neuroscientists to study how the brain works. However, the methods for analysing the rich spatial and temporal data they provide are constantly evolving, and these new methods in turn allow new scientific questions to be asked about the brain. In this brief review, we highlight a handful of recent analysis developments that promise to further advance our knowledge about the working of the brain. These include (1) multivariate approaches to decoding the content of brain activity, (2) time-varying approaches to characterising states of brain connectivity, (3) neurobiological modelling of neuroimaging data, and (4) standardisation and big data initiatives.

The field of functional neuroimaging has substantially advanced as a big data science in the past decade, thanks to international collaborative projects and community efforts. Here we conducted a literature review on functional neuroimaging, with focus on three general challenges in big data tasks: data collection and sharing, data infrastructure construction, and data analysis methods. The review covers a wide range of literature types including perspectives, database descriptions, methodology developments, and technical details. We show how each of the challenges was proposed and addressed, and how these solutions formed the three core foundations for the functional neuroimaging as a big data science and helped to build the current data-rich and data-driven community. Furthermore, based on our review of recent literature on the upcoming challenges and opportunities toward future scientific discoveries, we envisioned that the functional neuroimaging community needs to advance from the current foundations to better data integration infrastructure, methodology development toward improved learning capability, and multi-discipline translational research framework for this new era of big data.

Infancy is characterized by rapid neurological transformations leading to consolidation of lifelong function capabilities. Studying the infant brain is crucial for understanding how these mechanisms develop during this sensitive period. We review the neuroimaging modalities used with infants in stimulus-induced activity paradigms specifically, for the unique opportunity the latter provide for assessment of brain function.

The neurobiological basis of psychogenic movement disorders remains poorly understood and the management of these conditions difficult. Functional neuroimaging studies have provided some insight into the pathophysiology of disorders implicating particularly the prefrontal cortex, but there are no studies on psychogenic dystonia, and comparisons with findings in organic counterparts are rare. To understand the pathophysiology of these disorders better, we compared the similarities and differences in functional neuroimaging of patients with psychogenic dystonia and genetically determined dystonia, and tested hypotheses on the role of the prefrontal cortex in functional neurological disorders. Patients with psychogenic (n = 6) or organic (n = 5, DYT1 gene mutation positive) dystonia of the right leg, and matched healthy control subjects (n = 6) underwent positron emission tomography of regional cerebral blood flow. Participants were studied during rest, during fixed posturing of the right leg and during paced ankle movements. Continuous surface electromyography and footplate manometry monitored task performance. Averaging regional cerebral blood flow across all tasks, the organic dystonia group showed abnormal increases in the primary motor cortex and thalamus compared with controls, with decreases in the cerebellum. In contrast, the psychogenic dystonia group showed the opposite pattern, with abnormally increased blood flow in the cerebellum and basal ganglia, with decreases in the primary motor cortex. Comparing organic dystonia with psychogenic dystonia revealed significantly greater regional blood flow in the primary motor cortex, whereas psychogenic dystonia was associated with significantly greater blood flow in the cerebellum and basal ganglia (all P < 0.05, family-wise whole-brain corrected). Group × task interactions were also examined. During movement, compared with rest, there was abnormal activation in the right dorsolateral prefrontal cortex that was common to both organic and psychogenic dystonia groups (compared with control subjects, P < 0.05, family-wise small-volume correction). These data show a cortical-subcortical differentiation between organic and psychogenic dystonia in terms of regional blood flow, both at rest and during active motor tasks. The pathological prefrontal cortical activation was confirmed in, but was not specific to, psychogenic dystonia. This suggests that psychogenic and organic dystonia have different cortical and subcortical pathophysiology, while a derangement in mechanisms of motor attention may be a feature of both conditions.

The rapid growth of the literature on neuroimaging in humans has led to major advances in our understanding of human brain function but has also made it increasingly difficult to aggregate and synthesize neuroimaging findings. Here we describe and validate an automated brain-mapping framework that uses text-mining, meta-analysis and machine-learning techniques to generate a large database of mappings between neural and cognitive states. We show that our approach can be used to automatically conduct large-scale, high-quality neuroimaging meta-analyses, address long-standing inferential problems in the neuroimaging literature and support accurate 'decoding' of broad cognitive states from brain activity in both entire studies and individual human subjects. Collectively, our results have validated a powerful and generative framework for synthesizing human neuroimaging data on an unprecedented scale.

Mathematical co-registration of functional image data (e.g., positron emission tomography, PET) to anatomical magnetic resonance (MR) imaging data allows for objective associations between function and anatomy. Baboons are often used as non-human primate models for functional neuroimaging studies. In this work, a digital MR-based high-resolution atlas of the baboon brain was generated and evaluated for PET. The atlas was generated from six SPGR-MR datasets (centered at mid-sagittal line, AC-PC orientation) that were transformed into the space of one representative MR, averaged and resampled into PET space. The atlas was evaluated by comparing blood flow and dopamine receptor and serotonin transporter binding measures determined using regions-of-interest (ROIs) generated on each individual co-registered MR (ROI(i)) and the atlas-defined ROI template (ROI(ATLAS)). Common ROIs applied to all data included frontal cortex, temporal cortex, thalamus, caudate, putamen and cerebellum. High correlations (r(2)>0.87) were found between the ROI(i) and ROI(ATLAS) data for all radiotracers (linear regression across ROIs for each baboon). The average regression slope values ranged from 0.95 to 1.02 across radiotracers. Lastly, use of the atlas for statistical parametric mapping (SPM) of [15O]water data yielded good agreement with previous ROI(i) results. Overall, the digital MR-based atlas allowed for automatic co-registration, proved useful across a range of PET Studies, and is accessible electronically via the Internet.

New technologies are key to understanding the dynamic activity of neural circuits and systems in the brain. Here, we show that a minimally invasive approach based on ultrasound can be used to detect the neural correlates of movement planning, including directions and effectors. While non-human primates (NHPs) performed memory-guided movements, we used functional ultrasound (fUS) neuroimaging to record changes in cerebral blood volume with 100 μm resolution. We recorded from outside the dura above the posterior parietal cortex, a brain area important for spatial perception, multisensory integration, and movement planning. We then used fUS signals from the delay period before movement to decode the animals' intended direction and effector. Single-trial decoding is a prerequisite to brain-machine interfaces, a key application that could benefit from this technology. These results are a critical step in the development of neuro-recording and brain interface tools that are less invasive, high resolution, and scalable.

Humor is a ubiquitous aspect of human behavior that is infrequently the focus of neuroscience research. To localize human brain structures associated with the experience of humor, we conducted quantitative activation likelihood estimate (ALE) meta analyses of 57 fMRI studies (n = 1248) reporting enhanced regional brain activity evoked by humorous cues versus matched control cues. We performed separate ALE analyses of studies that employed picture-driven, text-based, and auditory laughter cues to evoke humor. A primary finding was that complex humor activates supramodal areas of the brain strongly associated with emotional processes, including bilateral amygdala and inferior frontal gyrus. Moreover, activation in brain regions associated with language, semantic knowledge, and theory of mind were differentially modulated by text and picture-driven humor cues, while hearing laughter enhances activation in auditory association cortex. The identification of humor-driven brain networks has the potential to expand brain-derived models of human emotion and could provide useful targets in translational research and therapy.

Neuroimaging results are typically graphically rendered and color-coded, which influences the process of knowledge generation within neuroscience as well as the public perception of brain research. Analyzing these issues requires empirical information on the display practice in neuroimaging. In our study we evaluated more than 9000 functional images (fMRI and PET) published between 1996 and 2009 with respect to the use of color, image structure, image production software and other factors that may determine the display practice. We demonstrate a variety of display styles despite a remarkable dominance of few image production sites and software systems, outline some tendencies of standardization, and identify shortcomings with respect to color scale explication in neuroimages. We discuss the importance of the finding for knowledge production in neuroimaging, and we make suggestions to improve the display practice in neuroimaging, especially on regimes of color coding.

A patient with motor conversion disorder presented with a functional paresis of the left hand. After exclusion of structural brain damage, she was repeatedly examined with whole-brain functional magnetic resonance imaging, while she performed visually paced finger-tapping tasks. The dorsal premotor cortex showed a bilateral deactivation in the acute-subacute phase. Recovery from unilateral hand paresis was associated with a gradual increase in task-based activation of the dorsal premotor cortex bilaterally. The right medial prefrontal cortex displayed the opposite pattern, showing initial task-based activation that gradually diminished with recovery. The inverse dynamics of premotor and medial prefrontal activity over time were found during unimanual finger-tapping with the affected and non-affected hand as well as during bimanual finger-tapping. These observations suggest that reduced premotor and increased medial prefrontal activity reflect an effector-independent cortical dysfunction in conversion paresis which gradually disappears in parallel with clinical remission of paresis. The results link the medial prefrontal and dorsal premotor areas to the generation of intentional actions. We hypothesise that an excessive 'veto' signal generated in medial prefrontal cortex along with decreased premotor activity might constitute the functional substrate of conversion disorder. This notion warrants further examination in a larger group of affected patients.

Object: A real-time functional magnetic resonance imaging (fMRI) feedback during ventral intermediate nucleus (VIM) deep brain stimulation (DBS) under general anesthesia (or "asleep" DBS) does not exist. We hypothesized that it was feasible to acquire a reliable and responsive fMRI during asleep VIM DBS surgery. Methods: We prospectively enrolled 10 consecutive patients who underwent asleep DBS for the treatment of medication-refractory essential tremor. Under general anesthesia, we acquired resting-state functional MRI immediately before and after the cannula insertion. Reliability was determined by a temporal signal-to-noise-ratio >100. Responsiveness was determined based on the fMRI signal change upon insertion of the cannula to the VIM. Results: It was feasible to acquire reliable fMRI during asleep DBS surgery. The fMRI signal was responsive to the brain cannula insertion, revealing a reduction in the tremor network's functional connectivity, which did not reach statistical significance in the group analysis. Conclusions: It is feasible to acquire a reliable and responsive fMRI signal during asleep DBS. The acquisition steps and the preprocessing pipeline developed in these experiments will be useful for future investigations to develop fMRI-based feedback for asleep DBS surgery.

The present study aims to extend the knowledge of the neural correlates of emotion processing in first episode subjects affected by anorexia nervosa (AN) or bulimia nervosa (BN). We applied an emotional distress paradigm targeting negative emotions thought to be relevant for interpersonal difficulties and therapeutic resistance mechanisms.

A significant challenge for fMRI research is statistically controlling for false positives without omitting true effects. Although a number of traditional methods for multiple comparison correction exist, several alternative tools have been developed that do not rely on strict parametric assumptions, but instead implement alternative methods to correct for multiple comparisons. In this study, we evaluated three of these methods, Statistical non-Parametric Mapping (SnPM), 3DClustSim, and Threshold Free Cluster Enhancement (TFCE), by examining which method produced the most consistent outcomes even when spatially-autocorrelated noise was added to the original images. We assessed the false alarm rate and hit rate of each method after noise was applied to the original images.

We present a computational framework suitable for a data-driven approach to structural equation modeling (SEM) and describe several workflows for modeling functional magnetic resonance imaging (fMRI) data within this framework. The Computational Neuroscience Applications Research Infrastructure (CNARI) employs a high-level scripting language called Swift, which is capable of spawning hundreds of thousands of simultaneous R processes (R Development Core Team, 2008), consisting of self-contained SEMs, on a high performance computing system (HPC). These self-contained R processing jobs are data objects generated by OpenMx, a plug-in for R, which can generate a single model object containing the matrices and algebraic information necessary to estimate parameters of the model. With such an infrastructure in place a structural modeler may begin to investigate exhaustive searches of the model space. Specific applications of the infrastructure, statistics related to model fit, and limitations are discussed in relation to exhaustive SEM. In particular, we discuss how workflow management techniques can help to solve large computational problems in neuroimaging.

Interoception is critically important for allostatic adaptation and emotional regulation, and aberrant interoceptive processing is increasingly recognized to be involved in the pathogenesis of neurological, psychiatric and cardiovascular diseases. Despite the fact that interoceptive abilities decline with age, the corresponding neural correlates and clinical consequences of these age-related changes have yet to be discovered. We present a dataset that contains task-based functional neuroimaging data from 50 adults aged 40-65 years and 12 adults aged 18-25 years who performed an fMRI-based heartbeat-detection task. Of the 62, 38 participants also took part in a rubber hand illusion experiment outside the scanner. While the dataset was mainly created to study age-related changes in interoception, it can also be used in body perception research in general. The provided group data may serve as a reference for clinical studies on interoception involving older adults.

The generation of creative visual imagery contributes to technological and scientific innovation and production of visual art. The underlying cognitive and neural processes are, however, poorly understood.

Antihistamine induced cognitive decline was evaluated using positron emission tomography (PET) measurement of histamine H1 receptor (H1R) occupancy and regional cerebral blood flow (rCBF). Cognitive performance in attention-demanding task deteriorated dose-dependently and the effects were statistically significant after the treatment of 2 mg of d-chlorpheniramine. There was no significant change in subjective sleepiness in the same dose. The regional blockade of H1R was observed mainly in the frontal, temporal and anterior cingulate cortices, and the intravenous administration of d-chlorpheniramine as a therapeutic dose (2 mg) blocked over 60% of H1R in the frontal cortices. The results from activation study using visual discrimination tasks demonstrated that enhanced activity in the right prefrontal and anterior cingulate cortices as well as a decreased activity in the left temporal and frontal cortices and midbrain after the treatment of d-chlorpheniramine. There were no changes in global CBF for the subjects treated with 2 mg d-chlorpheniramine (pre; 44.8+/-3.3 ml dl(-1) min(-1) vs post; 44.4+/-4.7 ml dl(-1) min(-1)). The results indicated that the attention system of human brain could be altered by therapeutic doses of H1R antagonists. These findings provide the information as to the potential risk of antihistamines in our daily activities. British Journal of Pharmacology (2000) 129, 115 - 123