Janine D. Bijsterbosch

Resting-state fMRI in the Human Connectome Project

Resting-state functional magnetic resonance imaging (rfMRI) allows one to study functional connectivity in the brain by acquiring fMRI data while subjects lie inactive in the MRI scanner, and taking advantage of the fact that functionally related brain regions spontaneously co-activate. rfMRI is one of the two primary data modalities being acquired for the Human Connectome Project (the other being diffusion MRI). A key objective is to generate a detailed in vivo mapping of functional connectivity in a large cohort of healthy adults (over 1000 subjects), and to make these datasets freely available for use by the neuroimaging community. In each subject we acquire a total of 1h of whole-brain rfMRI data at 3 T, with a spatial resolution of 2×2×2 mm and a temporal resolution of 0.7s, capitalizing on recent developments in slice-accelerated echo-planar imaging. We will also scan a subset of the cohort at higher field strength and resolution. In this paper we outline the work behind, and rationale for, decisions taken regarding the rfMRI data acquisition protocol and pre-processing pipelines, and present some initial results showing data quality and example functional connectivity analyses.

Where does transcranial magnetic stimulation (TMS) stimulate? Modelling of induced field maps for some common cortical and cerebellar targets

Computational models have been be used to estimate the electric and magnetic fields induced by transcranial magnetic stimulation (TMS) and can provide valuable insights into the location and spatial distribution of TMS stimulation. However, there has been little translation of these findings into practical TMS research. This study uses the International 10-20 EEG electrode placement system to position a standard figure-of-eight TMS coil over 13 commonly adopted targets. Using a finite element method and an anatomically detailed and realistic head model, this study provides the first pictorial and numerical atlas of TMS-induced electric fields for a range of coil positions. The results highlight the importance of subject-specific gyral folding patterns and of local thickness of subarachnoid cerebrospinal fluid (CSF). Our modelling shows that high electric fields occur primarily on the peaks of those gyri which have only a thin layer of CSF above them. These findings have important implications for inter-individual generalizability of the TMS-induced electric field. We propose that, in order to determine with accuracy the site of stimulation for an individual subject, it is necessary to solve the electric field distribution using subject-specific anatomy obtained from a high-resolution imaging modality such as MRI.

The role of the cerebellum in sub-and supraliminal error correction during sensorimotor synchronization: Evidence from fmri and tms

Our ability to interact physically with objects in the external world critically depends on temporal coupling between perception and movement (sensorimotor timing) and swift behavioral adjustment to changes in the environment (error correction). In this study, we investigated the neural correlates of the correction of subliminal and supraliminal phase shifts during a sensorimotor synchronization task. In particular, we focused on the role of the cerebellum because this structure has been shown to play a role in both motor timing and error correction. Experiment 1 used fMRI to show that the right cerebellar dentate nucleus and primary motor and sensory cortices were activated during regular timing and during the correction of subliminal errors. The correction of supraliminal phase shifts led to additional activations in the left cerebellum and right inferior parietal and frontal areas. Furthermore, a psychophysiological interaction analysis revealed that supraliminal error correction was associated with enhanced connectivity of the left cerebellum with frontal, auditory, and sensory cortices and with the right cerebellum. Experiment 2 showed that suppression of the left but not the right cerebellum with theta burst TMS significantly affected supraliminal error correction. These findings provide evidence that the left lateral cerebellum is essential for supraliminal error correction during sensorimotor synchronization.

Hand classification of fMRI ICA noise components.

&NA; We present a practical “how‐to” guide to help determine whether single‐subject fMRI independent components (ICs) characterise structured noise or not. Manual identification of signal and noise after ICA decomposition is required for efficient data denoising: to train supervised algorithms, to check the results of unsupervised ones or to manually clean the data. In this paper we describe the main spatial and temporal features of ICs and provide general guidelines on how to evaluate these. Examples of signal and noise components are provided from a wide range of datasets (3T data, including examples from the UK Biobank and the Human Connectome Project, and 7T data), together with practical guidelines for their identification. Finally, we discuss how the data quality, data type and preprocessing can influence the characteristics of the ICs and present examples of particularly challenging datasets.

Continuous theta burst stimulation over the left pre-motor cortex affects sensorimotor timing accuracy and supraliminal error correction

Adjustments to movement in response to changes in our surroundings are common in everyday behavior. Previous research has suggested that the left pre-motor cortex (PMC) is specialized for the temporal control of movement and may play a role in temporal error correction. The aim of this study was to determine the role of the left PMC in sensorimotor timing and error correction using theta burst transcranial magnetic stimulation (TBS). In Experiment 1, subjects performed a sensorimotor synchronization task (SMS) with the left and the right hand before and after either continuous or intermittent TBS (cTBS or iTBS). Timing accuracy was assessed during synchronized finger tapping with a regular auditory pacing stimulus. Responses following perceivable local timing shifts in the pacing stimulus (phase shifts) were used to measure error correction. Suppression of the left PMC using cTBS decreased timing accuracy because subjects tapped further away from the pacing tones and tapping variability increased. In addition, error correction responses returned to baseline tap-tone asynchrony levels faster following negative shifts and no overcorrection occurred following positive shifts after cTBS. However, facilitation of the left PMC using iTBS did not affect timing accuracy or error correction performance. Experiment 2 revealed that error correction performance may change with practice, independent of TBS. These findings provide evidence for a role of the left PMC in both sensorimotor timing and error correction in both hands. We propose that the left PMC may be involved in voluntarily controlled phase correction responses to perceivable timing shifts.

Resting state correlates of subdimensions of anxious affect

Resting state fMRI may help identify markers of risk for affective disorder. Given the comorbidity of anxiety and depressive disorders and the heterogeneity of these disorders as defined by DSM, an important challenge is to identify alterations in resting state brain connectivity uniquely associated with distinct profiles of negative affect. The current study aimed to address this by identifying differences in brain connectivity specifically linked to cognitive and physiological profiles of anxiety, controlling for depressed affect. We adopted a two-stage multivariate approach. Hierarchical clustering was used to independently identify dimensions of negative affective style and resting state brain networks. Combining the clustering results, we examined individual differences in resting state connectivity uniquely associated with subdimensions of anxious affect, controlling for depressed affect. Physiological and cognitive subdimensions of anxious affect were identified. Physiological anxiety was associated with widespread alterations in insula connectivity, including decreased connectivity between insula subregions and between the insula and other medial frontal and subcortical networks. This is consistent with the insula facilitating communication between medial frontal and subcortical regions to enable control of physiological affective states. Meanwhile, increased connectivity within a frontoparietal–posterior cingulate cortex–precunous network was specifically associated with cognitive anxiety, potentially reflecting increased spontaneous negative cognition (e.g., worry). These findings suggest that physiological and cognitive anxiety comprise subdimensions of anxiety-related affect and reveal associated alterations in brain connectivity.

Functional connectivity under anticipation of shock: Correlates of trait anxious affect versus induced anxiety

Sustained anxiety about potential future negative events is an important feature of anxiety disorders. In this study, we used a novel anticipation of shock paradigm to investigate individual differences in functional connectivity during prolonged threat of shock. We examined the correlates of between-participant differences in trait anxious affect and induced anxiety, where the latter reflects changes in self-reported anxiety resulting from the shock manipulation. Dissociable effects of trait anxious affect and induced anxiety were observed. Participants with high scores on a latent dimension of anxious affect showed less increase in ventromedial pFC–amygdala connectivity between periods of safety and shock anticipation. Meanwhile, lower levels of induced anxiety were linked to greater augmentation of dorsolateral pFC–anterior insula connectivity during shock anticipation. These findings suggest that ventromedial pFC–amygdala and dorsolateral pFC–insula networks might both contribute to regulation of sustained fear responses, with their recruitment varying independently across participants. The former might reflect an evolutionarily old mechanism for reducing fear or anxiety, whereas the latter might reflect a complementary mechanism by which cognitive control can be implemented to diminish fear responses generated due to anticipation of aversive stimuli or events. These two circuits might provide complementary, alternate targets for exploration in future pharmacological and cognitive intervention studies.

The effect of head orientation on subarachnoid cerebrospinal fluid distribution and its implications for neurophysiological modulation and recording techniques

Gravitational forces may lead to local changes in subarachnoid cerebrospinal fluid (CSF) layer thickness, which has important implications for neurophysiological modulation and recording techniques. This study examines the effect of gravitational pull associated with different head positions on the distribution of subarachnoid CSF using structural magnetic resonance imaging. Images of seven subjects in three different positions (supine, left lateral and prone) were statistically compared. Results suggest that subarachnoid CSF volume decreases on the side of the head closest to the ground, due to downward brain movement with gravity. These findings warrant future research into currently unexplored gravitation-induced changes in regional subarachnoid CSF thickness.

Using temporal ICA to selectively remove global noise while preserving global signal in functional MRI data.

ABSTRACT Temporal fluctuations in functional Magnetic Resonance Imaging (fMRI) have been profitably used to study brain activity and connectivity for over two decades. Unfortunately, fMRI data also contain structured temporal “noise” from a variety of sources, including subject motion, subject physiology, and the MRI equipment. Recently, methods have been developed to automatically and selectively remove spatially specific structured noise from fMRI data using spatial Independent Components Analysis (ICA) and machine learning classifiers. Spatial ICA is particularly effective at removing spatially specific structured noise from high temporal and spatial resolution fMRI data of the type acquired by the Human Connectome Project and similar studies. However, spatial ICA is mathematically, by design, unable to separate spatially widespread “global” structured noise from fMRI data (e.g., blood flow modulations from subject respiration). No methods currently exist to selectively and completely remove global structured noise while retaining the global signal from neural activity. This has left the field in a quandary—to do or not to do global signal regression—given that both choices have substantial downsides. Here we show that temporal ICA can selectively segregate and remove global structured noise while retaining global neural signal in both task‐based and resting state fMRI data. We compare the results before and after temporal ICA cleanup to those from global signal regression and show that temporal ICA cleanup removes the global positive biases caused by global physiological noise without inducing the network‐specific negative biases of global signal regression. We believe that temporal ICA cleanup provides a “best of both worlds” solution to the global signal and global noise dilemma and that temporal ICA itself unlocks interesting neurobiological insights from fMRI data. HIGHLIGHTSTemporal ICA separates global noise and global signal allowing each to be measured.Temporal ICA enables new neurobiological insights into functional networks.Temporal ICA enables generation of unbiased functional connectivity.Temporal ICA solves the GSR/no GSR cleanup dilemma.

Investigations into within- and between-subject resting-state amplitude variations

Abstract The amplitudes of spontaneous fluctuations in brain activity may be a significant source of within‐subject and between‐subject variability, and this variability is likely to be carried through into functional connectivity (FC) estimates (whether directly or indirectly). Therefore, improving our understanding of amplitude fluctuations over the course of a resting state scan and variation in amplitude across individuals is of great relevance to the interpretation of FC findings. We investigate resting state amplitudes in two large‐scale studies (HCP and UK Biobank), with the aim of determining between‐subject and within‐subject variability. Between‐subject clustering distinguished between two groups of brain networks whose amplitude variation across subjects were highly correlated with each other, revealing a clear distinction between primary sensory and motor regions (‘primary sensory/motor cluster’) and cognitive networks. Within subjects, all networks in the primary sensory/motor cluster showed a consistent increase in amplitudes from the start to the end of the scan. In addition to the strong increases in primary sensory/motor amplitude, a large number of changes in FC were found when comparing the two scans acquired on the same day (HCP data). Additive signal change analysis confirmed that all of the observed FC changes could be fully explained by changes in amplitude. Between‐subject correlations in UK Biobank data showed a negative correlation between primary sensory/motor amplitude and average sleep duration, suggesting a role of arousal. Our findings additionally reveal complex relationships between amplitude and head motion. These results suggest that network amplitude is a source of significant variability both across subjects, and within subjects on a within‐session timescale. Future rfMRI studies may benefit from obtaining arousal‐related (self report) measures, and may wish to consider the influence of amplitude changes on measures of (dynamic) functional connectivity. HighlightsWe investigate variability in resting state amplitude between‐ and within‐subjects.Clustering results reveal a distinction between cognitive and primary sensory/motor networks.Amplitudes in primary sensory/motor networks increase within subject towards the end of a scan.These increases in amplitude drive changes in apparent functional connectivity.Findings reveal complex relationships between amplitude and head motion.