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Continued improvement in MRI acquisition technology has made functional MRI (fMRI) with small isotropic voxel sizes down to 1 mm and below more commonly available. Although many conventional fMRI studies seek to investigate regional patterns of cortical activation for which conventional voxel sizes of 3 mm and larger provide sufficient spatial resolution, smaller voxels can help avoid contamination from adjacent white matter (WM) and cerebrospinal fluid (CSF), and thereby increase the specificity of fMRI to signal changes within the gray matter. Unfortunately, temporal signal-to-noise ratio (tSNR), a metric of fMRI sensitivity, is reduced in high-resolution acquisitions, which offsets the benefits of small voxels. Here we introduce a framework that combines small, isotropic fMRI voxels acquired at 7 T field strength with a novel anatomically-informed, surface mesh-navigated spatial smoothing that can provide both higher detection power and higher resolution than conventional voxel sizes. Our smoothing approach uses a family of intracortical surface meshes and allows for kernels of various shapes and sizes, including curved 3D kernels that adapt to and track the cortical folding pattern. Our goal is to restrict smoothing to the cortical gray matter ribbon and avoid noise contamination from CSF and signal dilution from WM via partial volume effects. We found that the intracortical kernel that maximizes tSNR does not maximize percent signal change (ΔS/S), and therefore the kernel configuration that optimizes detection power cannot be determined from tSNR considerations alone. However, several kernel configurations provided a favorable balance between boosting tSNR and ΔS/S, and allowed a 1.1-mm isotropic fMRI acquisition to have higher performance after smoothing (in terms of both detection power and spatial resolution) compared to an unsmoothed 3.0-mm isotropic fMRI acquisition. Overall, the results of this study support the strategy of acquiring voxels smaller than the cortical thickness, even for studies not requiring high spatial resolution, and smoothing them down within the cortical ribbon with a kernel of an appropriate shape to achieve the best performance—thus decoupling the choice of fMRI voxel size from the spatial resolution requirements of the particular study. The improvement of this new intracortical smoothing approach over conventional surface-based smoothing is expected to be modest for conventional resolutions, however the improvement is expected to increase with higher resolutions. This framework can also be applied to anatomically-informed intracortical smoothing of higher-resolution data (e.g. along columns and layers) in studies with prior information about the spatial structure of activation. •We introduce an anatomically-informed intracortical spatial smoothing method.•Smoothing fMRI data while avoiding white matter and CSF has advantages.•Different smoothing kernels maximize SNR and percent signal change.•Smoothing small-voxel data can provide improved detection and comparable resolution.•This smoothing framework can also benefit laminar and columnar activation patterns.

We have recently performed simultaneous intracranial EEG and fMRI recordings (icEEG–fMRI) in patients with epilepsy. In this technical note, we examine limited thermometric data for potential electrode heating during our protocol and found that heating was ≤0.1°C in-vitro at least 10 fold less than in-vivo limits. We quantify EEG quality, which can be degraded by MRI scanner-induced artefacts, and fMRI image (gradient echo echo-planar imaging: GE-EPI) signal quality around the electrodes, which can be degraded by electrode interactions with B1 (radiofrequency) and B0 (static) magnetic fields. We recorded EEG outside and within the MRI scanner with and without scanning. EEG quality was largely preserved during scanning and in particular heartbeat-related artefacts were small compared to epileptic events. To assess the GE-EPI signal reduction around the electrodes, we compared image signal intensity along paths into the brain normal to its surface originating from the individual platinum–iridium electrode contacts. GE-EPI images were obtained at 1.5T with an echo time (TE) of 40ms and repetition time (TR) of 3000ms and a slice thickness of 2.5mm. We found that GE-EPI signal intensity reduction was confined to a 10mm radius and that it was reduced on average by less than 50% at 5mm from the electrode contacts. The GE-EPI image signal reduction also varied with electrode orientation relative to the MRI scanner axes; in particular, cortical grid contacts with a normal along the scanner's main magnetic field (B0) axis have higher artefact levels relative to those with a normal perpendicular to the z-axis. This suggests that the artefacts were predominantly susceptibility-related rather than due to B1 interactions. This information can be used to guide interpretation of results of icEEG–fMRI experiments proximal to the electrodes, and to optimise artefact reduction strategies.

•Presenting the first full iTBS protocol during continuous MRI imaging.•Robust bilateral DLPFC activation during iTBS in healthy with widespread engagement.•Notable intra-individual variability in a depressive patient across iTBS sessions. Left prefrontal intermittent theta-burst stimulation (iTBS) has emerged as a safe and effective transcranial magnetic stimulation (TMS) treatment protocol in depression. Though network effects after iTBS have been widely studied, the deeper mechanistic understanding of target engagement is still at its beginning. Here, we investigate the feasibility of a novel integrated TMS-fMRI setup and accelerated echo planar imaging protocol to directly observe the immediate effects of full iTBS treatment sessions. In our effort to explore interleaved iTBS-fMRI feasibility, we hypothesize that TMS will induce acute BOLD signal changes in both the stimulated area and interconnected neural regions. Concurrent TMS-fMRI with full sessions of neuronavigated iTBS (i.e. 600 pulses) of the left dorsolateral prefrontal cortex (DLPFC) was investigated in 18 healthy participants. In addition, we conducted four TMS-fMRI sessions in a single patient on long-term maintenance iTBS for bipolar depression to test the transfer to clinical cases. Concurrent TMS-fMRI was feasible for iTBS sequences with 600 pulses. During interleaved iTBS-fMRI, an increase of the BOLD signal was observed in a network including bilateral DLPFC regions. In the clinical case, a reduced BOLD response was found in the left DLPFC and the subgenual anterior cingulate cortex, with high variability across individual sessions. Full iTBS sessions as applied for the treatment of depressive disorders can be established in the interleaved iTBS-fMRI paradigm. In the future, this experimental approach could be valuable in clinical samples, for demonstrating target engagement by iTBS protocols and investigating their mechanisms of therapeutic action.

Resting‐state functional magnetic resonance imaging (rs‐fMRI) has been extensively used to study brain aging, but the age effect on the frequency content of the rs‐fMRI signal has scarcely been examined. Moreover, the neuronal implications of such age effects and age–sex interaction remain unclear. In this study, we examined the effects of age and sex on the rs‐fMRI signal frequency using the Leipzig mind–brain–body data set. Over a frequency band of up to 0.3 Hz, we found that the rs‐fMRI fluctuation frequency is higher in the older adults, although the fluctuation amplitude is lower. The rs‐fMRI signal frequency is also higher in men than in women. Both age and sex effects on fMRI frequency vary with the frequency band examined but are not found in the frequency of physiological‐noise components. This higher rs‐fMRI frequency in older adults is not mediated by the electroencephalograph (EEG)‐frequency increase but a likely link between fMRI signal frequency and EEG entropy, which vary with age and sex. Additionally, in different rs‐fMRI frequency bands, the fMRI‐EEG amplitude ratio is higher in young adults. This is the first study to investigate the neuronal contribution to age and sex effects in the frequency dimension of the rs‐fMRI signal and may lead to the development of new, frequency‐based rs‐fMRI metrics. Our study demonstrates that Fourier analysis of the fMRI signal can reveal novel information about aging. Furthermore, fMRI and EEG signals reflect different aspects of age‐ and sex‐related brain differences, but the signal frequency and complexity, instead of amplitude, may hold their link. This is the first study to investigate the neuronal contribution to age and sex effects in the frequency dimension of the rs‐fMRI signal and may lead to the development of new, frequency‐based rs‐fMRI metrics. fMRI and EEG signals reflect different aspects of age‐ and sex‐related brain differences, but signal frequency and complexity, instead of amplitude, may hold their link.

Previous research has suggested that certain types of the affective temperament, including depressive, cyclothymic, hyperthymic, irritable, and anxious, are subclinical manifestations and precursors of mental disorders. However, the neural mechanisms that underlie these temperaments are not fully understood. The aim of this study was to identify the brain regions associated with different affective temperaments. We collected the resting‐state functional magnetic resonance imaging (fMRI) data from 211 healthy adults and evaluated their affective temperaments using the Temperament Evaluation of Memphis, Pisa, Paris and San Diego Autoquestionnaire. We used intersubject representational similarity analysis to identify brain regions associated with each affective temperament. Brain regions associated with each affective temperament were detected. These regions included the prefrontal cortex, anterior cingulate cortex (ACC), precuneus, amygdala, thalami, hippocampus, and visual areas. The ACC, lingual gyri, and precuneus showed similar activity across several affective temperaments. The similarity in related brain regions was high among the cyclothymic, irritable, and anxious temperaments, and low between hyperthymic and the other affective temperaments. These findings may advance our understanding of the neural mechanisms underlying affective temperaments and their potential relationship to mental disorders and may have potential implications for personalized treatment strategies for mood disorders. We employed intersubject representational similarity analysis to identify the brain regions associated with affective temperaments, which are considered subclinical manifestations and precursors of mental disorders, particularly bipolar disorder. The study revealed that depressive, cyclothymic, irritable, and anxious temperaments exhibited similar brain activity patterns, while the hyperthymic temperament displayed distinct activity patterns.

Neurofeedback is a promising approach for non-invasive modulation of human brain activity with applications for treatment of mental disorders and enhancement of brain performance. Neurofeedback techniques are commonly based on either electroencephalography (EEG) or real-time functional magnetic resonance imaging (rtfMRI). Advances in simultaneous EEG–fMRI have made it possible to combine the two approaches. Here we report the first implementation of simultaneous multimodal rtfMRI and EEG neurofeedback (rtfMRI–EEG-nf). It is based on a novel system for real-time integration of simultaneous rtfMRI and EEG data streams. We applied the rtfMRI–EEG-nf to training of emotional self-regulation in healthy subjects performing a positive emotion induction task based on retrieval of happy autobiographical memories. The participants were able to simultaneously regulate their BOLD fMRI activation in the left amygdala and frontal EEG power asymmetry in the high-beta band using the rtfMRI−EEG-nf. Our proof-of-concept results demonstrate the feasibility of simultaneous self-regulation of both hemodynamic (rtfMRI) and electrophysiological (EEG) activities of the human brain. They suggest potential applications of rtfMRI–EEG-nf in the development of novel cognitive neuroscience research paradigms and enhanced cognitive therapeutic approaches for major neuropsychiatric disorders, particularly depression. •We report the first implementation of simultaneous rtfMRI and EEG neurofeedback.•A novel integration of simultaneous rtfMRI and EEG data streams is described.•Subjects can self-regulate their left amygdala fMRI activation and frontal EEG asymmetry.•Hemodynamic and electrophysiological processes can be regulated simultaneously.•rtfMRI–EEG neurofeedback holds promise for improved treatment of mental disorders.

AFNI is an open source software package for the analysis and display of functional MRI data. It originated in 1994 to meet the specific needs of researchers at the Medical College of Wisconsin, in particular the mapping of activation maps to Talairach–Tournoux space, but has been expanded steadily since then into a wide-ranging set of tool for FMRI data analyses. AFNI was the first platform for real-time 3D functional activation and registration calculations. One of AFNI's main strengths is its flexibility and transparency. In recent years, significant efforts have been made to increase the user-friendliness of AFNI's FMRI processing stream, with the introduction of “super-scripts” to setup the entire analysis, and graphical front-ends for these managers. ► How AFNI came to be and how it is structured. ► Outline of recent usability and statistical improvements. ► Speculations about the future of AFNI and FMRI software.

Delineation of functional topography is critical to the evolving understanding of the cerebellum's role in a wide range of nervous system functions. We used data from the Human Connectome Project (n = 787) to analyze cerebellar fMRI task activation (motor, working memory, language, social and emotion processing) and resting-state functional connectivity calculated from cerebral cortical seeds corresponding to the peak Cohen's d of each task contrast. The combination of exceptional statistical power, activation from both motor and multiple non-motor tasks in the same participants, and convergent resting-state networks in the same participants revealed novel aspects of the functional topography of the human cerebellum. Consistent with prior studies there were two distinct representations of motor activation. Newly revealed were three distinct representations each for working memory, language, social, and emotional task processing that were largely separate for these four cognitive and affective domains. In most cases, the task-based activations and the corresponding resting-network correlations were congruent in identifying the two motor representations and the three non-motor representations that were unique to working memory, language, social cognition, and emotion. The definitive localization and characterization of distinct triple representations for cognition and emotion task processing in the cerebellum opens up new basic science questions as to why there are triple representations (what different functions are enabled by the different representations?) and new clinical questions (what are the differing consequences of lesions to the different representations?). •We analyzed motor and multiple nonmotor task fMRI activations in the cerebellum.•Resting-state seeds were placed at each task activation peak in the cerebral cortex.•We describe cerebellar task topography in the largest single cohort studied to date.•Nonmotor cerebellar task activation revealed a pattern of triple representation.•Resting-state analysis revealed an overlapping pattern of triple representation.

Non-invasive neuroimaging serves as a valuable tool for investigating the mechanisms within the central nervous system (CNS) related to somatosensory and motor processing, emotions, memory, cognition, and other functions. Despite the extensive use of brain imaging, spinal cord imaging has received relatively less attention, regardless of its potential to study peripheral communications with the brain and the descending corticospinal systems. To comprehensively understand the neural mechanisms underlying human sensory and motor functions, particularly in pathological conditions, simultaneous examination of neuronal activity in both the brain and spinal cord becomes imperative. Although technically demanding in terms of data acquisition and analysis, a growing but limited number of studies have successfully utilized specialized acquisition protocols for corticospinal imaging. These studies have effectively assessed sensorimotor, autonomic, and interneuronal signaling within the spinal cord, revealing interactions with cortical processes in the brain. In this mini-review, we aim to examine the expanding body of literature that employs cutting-edge corticospinal imaging to investigate the flow of sensorimotor information between the brain and spinal cord. Additionally, we will provide a concise overview of recent advancements in functional magnetic resonance imaging (fMRI) techniques. Furthermore, we will discuss potential future perspectives aimed at enhancing our comprehension of large-scale neuronal networks in the CNS and their disruptions in clinical disorders. This collective knowledge will aid in refining combined corticospinal fMRI methodologies, leading to the development of clinically relevant biomarkers for conditions affecting sensorimotor processing in the CNS.

Abstract Introduction We performed a systematic review and meta-analysis of the Alzheimer's disease (AD) literature to examine consistency of functional connectivity alterations in AD dementia and mild cognitive impairment, using resting-state functional magnetic resonance imaging. Methods Studies were screened using a standardized procedure. Multiresolution statistics were performed to assess the spatial consistency of findings across studies. Results Thirty-four studies were included (1363 participants, average 40 per study). Consistent alterations in connectivity were found in the default mode, salience, and limbic networks in patients with AD dementia, mild cognitive impairment, or in both groups. We also identified a strong tendency in the literature toward specific examination of the default mode network. Discussion Convergent evidence across the literature supports the use of resting-state connectivity as a biomarker of AD. The locations of consistent alterations suggest that highly connected hub regions in the brain might be an early target of AD.