Measurement of BOLD changes due to cued eye-closure and stopping during a continuous visuomotor task via model-based and model-free approaches

As a precursor for investigation of changes in neural activity underlying lapses of responsiveness, we set up a system to simultaneously record functional magnetic resonance imaging (fMRI), eye-video, EOG, and continuous visuomotor response inside an MRI scanner. The BOLD fMRI signal was acquired during a novel 2-D tracking task in which participants (10 males, 10 females) were cued to either briefly stop tracking and close their eyes (Stop Close) or to briefly stop tracking (Stop) only. The onset and duration of eye-closure and stopping were identified post hoc from eye-video, EOG, and visuomotor response. fMRI data were analyzed using a general linear model (GLM) and tensorial independent component analysis (TICA). The GLM-based analysis identified predominantly increased blood oxygenation level dependent (BOLD) activity during eye-closure and stopping in multisensory areas, sensory-motor integration areas, and default-mode regions. Stopping during tracking elicited increased activity in visual processing areas, sensory-motor integration areas, and premotor areas. TICA separated the spatio-temporal pattern of activity into multiple task-related networks including the 1) occipito-medial frontal eye-movement network, 2) sensory areas, 3) left-lateralized visuomotor network, and 4) fronto-parietal visuomotor network, which were modulated differently by Stop Close and Stop. The results demonstrate the merits of using simultaneous fMRI, behavioral, and physiological recordings to investigate the mechanisms underlying complex human behaviors in the human brain. Furthermore, knowledge of widespread modulations in brain activity due to voluntary eye-closure or stopping during a continuous visuomotor task is important for studies of the brain mechanisms underlying involuntary behaviors, such as microsleeps and attention lapses, which are often accompanied by brief eye-closure and/or response failures

Early soft and flexible fusion of electroencephalography and functional magnetic resonance imaging via double coupled matrix tensor factorization for multisubject group analysis

Data fusion refers to the joint analysis of multiple datasets that provide different (e.g., complementary) views of the same task. In general, it can extract more information than separate analyses can. Jointly analyzing electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) measurements has been proved to be highly beneficial to the study of the brain function, mainly because these neuroimaging modalities have complementary spatiotemporal resolution: EEG offers good temporal resolution while fMRI is better in its spatial resolution. The EEG–fMRI fusion methods that have been reported so far ignore the underlying multiway nature of the data in at least one of the modalities and/or rely on very strong assumptions concerning the relation of the respective datasets. For example, in multisubject analysis, it is commonly assumed that the hemodynamic response function is a priori known for all subjects and/or the coupling across corresponding modes is assumed to be exact (hard). In this article, these two limitations are overcome by adopting tensor models for both modalities and by following soft and flexible coupling approaches to implement the multimodal fusion. The obtained results are compared against those of parallel independent component analysis and hard coupling alternatives, with both synthetic and real data (epilepsy and visual oddball paradigm). Our results demonstrate the clear advantage of using soft and flexible coupled tensor decompositions in scenarios that do not conform with the hard coupling assumption

Dissecting cognitive stages with time-resolved fMRI data: a comparison of fuzzy clustering and independent component analysis

in combination with cognitive tasks entailing sequences of sensory and cognitive processes, event-related acquisition schemes allow using functional MRI to examine not only the topography but also the temporal sequence of cortical activation across brain regions (time-resolved fMRI). In this study, we compared two data-driven methods - fuzzy clustering method (FCM) and independent component analysis (ICA) - in the context of time-resolved fMRI data collected during the performance of a newly devised visual imagery task. We analyzed a multisubject fMRI data set using both methods and compared their results in terms of within and between-subject consistency and spatial and temporal correspondence of obtained maps and time courses. Both FCM and spatial ICA allowed discriminating the contribution of distinct networks of brain regions to the main cognitive stages of the task (auditory perception, mental imagery and behavioural response), with good agreement across methods. Whereas ICA worked optimally on the original time series, averaging with respect to the task onset (and thus introducing some a priori information on the stimulation protocol) was found to be indispensable in the case of FCM. On averaged time series, FCM led to a richer decomposition of the spatio-temporal patterns of activation and allowed a finer separation of the neurocognitive processes subserving the mental imagery task. This study confirms the efficacy of the two examined methods in the data-driven estimation of hemodynamic responses in time-resolved fMRI studies and provides empirical guidelines to their use

Disruption to control network function correlates with altered dynamic connectivity in the wider autism spectrum.

Autism is a common developmental condition with a wide, variable range of co-occurring neuropsychiatric symptoms. Contrasting with most extant studies, we explored whole-brain functional organization at multiple levels simultaneously in a large subject group reflecting autism's clinical diversity, and present the first network-based analysis of transient brain states, or dynamic connectivity, in autism. Disruption to inter-network and inter-system connectivity, rather than within individual networks, predominated. We identified coupling disruption in the anterior-posterior default mode axis, and among specific control networks specialized for task start cues and the maintenance of domain-independent task positive status, specifically between the right fronto-parietal and cingulo-opercular networks and default mode network subsystems. These appear to propagate downstream in autism, with significantly dampened subject oscillations between brain states, and dynamic connectivity configuration differences. Our account proposes specific motifs that may provide candidates for neuroimaging biomarkers within heterogeneous clinical populations in this diverse condition

Pitfalls in fMRI

Several different techniques allow a functional assessment of neuronal activations by magnetic resonance imaging (fMRI). The by far most influential fMRI technique is based on a local T2*-sensitive hemodynamic response to neuronal activation, also known as the blood oxygenation level dependent or BOLD effect. Consequently, the term ‘fMRI' is often used synonymously with BOLD imaging. Because interpretations of fMRI brain activation maps often appear intuitive and compelling, the reader might be tempted not to critically question the fundamental processes and assumptions. We review some essential processes and assumptions of BOLD fMRI and discuss related confounds and pitfalls in fMRI - from the underlying physiological effect, to data acquisition, data analysis and the interpretation of the results including clinical fMRI. A background framework is provided for the systematic and critical interpretation of fMRI result