Comparative Study of Brain fMRI of Olfactory Stimulation in Neuromyelitis Optica Spectrum Disease and Multiple Sclerosis

Objective: To observe the characteristics of brain fMRI during olfactory stimulation in patients with neuromyelitis optica spectrum disease (NMOSD) and multiple sclerosis (MS), compare the differences of brain functional activation areas between patients with NMOSD and MS, and explore the characteristics of olfactory-related brain networks of NMOSD and MS.Methods: Nineteen patients with NMOSD and 16 patients with MS who met the diagnostic criteria were recruited, and 19 healthy controls matched by sex and age were recruited. The olfactory function of all participants was assessed using the visual analog scale (VAS). Olfactory stimulation was alternately performed using a volatile body (lavender and rose solution) and the difference in brain activation was evaluated by task-taste fMRI scanning simultaneously.Results: Activation intensity was weaker in the NMOSD group than in the healthy controls, including the left rectus, right superior temporal gyrus, and left cuneus. The activation intensity was stronger for the NMOSD than the controls in the left insula and left middle frontal gyrus (P < 0.05). Activation intensity was weaker in the MS group than the healthy controls in the bilateral hippocampus, right parahippocampal gyrus, right insula, left rectus gyrus, and right precentral gyrus, and stronger in the left paracentral lobule among the MS than the controls (P < 0.05). Compared with the MS group, activation intensity in the NMOSD group was weaker in the right superior temporal gyrus and left paracentral lobule, while it was stronger among the NMOSD group in the bilateral insula, bilateral hippocampus, bilateral parahippocampal gyrus, left inferior orbital gyrus, left superior temporal gyrus, left putamen, and left middle frontal gyrus (P < 0.05).Conclusion: Olfactory-related brain networks are altered in both patients, and there are differences between their olfactory-related brain networks. It may provide a new reference index for the clinical differentiation and disease evaluation of NMOSD and MS. Moreover, further studies are needed.

Population receptive fields in non-human primates from whole-brain fMRI and large-scale neurophysiology in visual cortex in visual cortex

Population receptive field (pRF) modeling is a popular fMRI method to map the retinotopic organization of the human brain. While fMRI-based pRF-maps are qualitatively similar to invasively recorded single-cell receptive fields in animals, it remains unclear what neuronal signal they represent. We addressed this question in awake non-human primates comparing whole-brain fMRI and large-scale neurophysiological recordings in areas V1 and V4 of the visual cortex. We examined the fits of several pRF-models based on the fMRI BOLD-signal, multi-unit spiking activity (MUA) and local field potential (LFP) power in different frequency bands. We found that pRFs derived from BOLD-fMRI were most similar to MUA-pRFs in V1 and V4, while pRFs based on LFP gamma power also gave a good approximation. FMRI-based pRFs thus reliably reflect neuronal receptive field properties in the primate brain. In addition to our results in V1 and V4, the whole-brain fMRI measurements revealed retinotopic tuning in many other cortical and subcortical areas with a consistent increase in pRF-size with increasing eccentricity, as well as a retinotopically specific deactivation of default-mode network nodes similar to previous observations in humans.

Complex of global functional network as the core of consciousness

Finding the neural basis of consciousness is a challenging issue, and it is still inconclusive where the core of consciousness is distributed in the brain. The global neuronal workspace theory (GNWT) emphasizes the role of the frontoparietal regions, whereas the integrated information theory (IIT) argues that the posterior part of the brain is the core of consciousness. IIT has proposed “main complex” as the core of consciousness in a dynamic system, which is a set of elements that the information loss in a hierarchical partition approach is the largest among that of all its supersets and subsets. However, no experimental study has reported the core of consciousness using the main complex for actual brain activity. This study estimated the main complex of brain dynamics using a functional MRI. The whole-brain fMRI data of eight conditions (seven tasks and a rest state) were divided into multiple elements based on network atlases, and the main complex of the dynamic system was estimated for each condition. It is assumed that, if there is a set of elements in the complex that are common to all conditions, the set is likely to contain the core of consciousness. Executive control, salience, and dorsal/ventral attention networks were commonly included in the main complex across all conditions, implying that these networks are responsible for the core of consciousness. This finding is consistent with the GNWT, as these networks are across the prefrontal and parietal regions.

Characterizing pupil dynamics coupling to brain state fluctuation based on lateral hypothalamic activity

Pupil dynamics presents varied correlation features with brain activity under different vigilant levels. The modulation of brain state changes can arise from the lateral hypothalamus (LH), where diverse neuronal cell types contribute to arousal regulation in opposite directions via the anterior cingulate cortex (ACC). However, the relationship of the LH and pupil dynamics has seldom been investigated. Here, we performed local field potential (LFP) recordings at the LH and ACC, and the whole brain fMRI with simultaneous fiber photometry Ca2+ recording in the ACC, to evaluate their correlation with brain state-dependent pupil dynamics. Both LFP and functional MRI (fMRI) data showed opposite correlation features to pupil dynamics, demonstrating an LH activity-dependent manner. Our results demonstrate that the correlation of pupil dynamics with ACC LFP and whole-brain fMRI signals depends on LH activity, indicating a role of the latter in brain state regulation.

Disentangling dimensions of animacy in human brain and behaviour

The perception of animate things is of great behavioural importance to humans. Despite the prominence of the distinct brain and behavioural responses to animate and inanimate things, however, it remains unclear which of several commonly entangled properties underlie these observations. Here, we investigate the importance of five dimensions of animacy: being alive, looking like an animal, having agency, having mobility, and being unpredictable in brain (fMRI, EEG) and behaviour (property and similarity judgments) of 19 subjects using a stimulus set of 128 images that disentangles the five dimensions (optimized by a genetic algorithm). Our results reveal a differential pattern across brain and behaviour. The living/non-living distinction (being alive) was prominent in judgments, but despite its prominence in neuroscience literature, did not explain variance in brain representations. The other dimensions of animacy explained variance in both brain and behaviour. The having agency dimension explained more variance in higher-level visual areas, consistent with higher cognitive contributions. The being unpredictable dimension instead captured representations in both lower and higher-level visual cortex, possibly because unpredictable things require attention. Animacy is multidimensional and our results show that distinct dimensions are differentially represented in human brain and behaviour.

Can fMRI functional connectivity index dynamic neural communication?

In order to continuously respond to a changing environment and support self-generating cognition and behaviour, neural communication must be highly flexible and dynamic at the same time than hierarchically organized. While whole-brain fMRI measures have revealed robust yet changing patterns of statistical dependencies between regions, it is not clear whether these statistical patterns (referred to as functional connectivity) can reflect dynamic large-scale communication in a way that is relevant to cognition. For functional connectivity to reflect actual communication, we propose three necessary conditions: it must span sufficient temporal complexity to support the needs of cognition while still being highly organized so that the system behaves reliably; it must be able to adapt to the current behavioural context; and it must exhibit fluctuations at sufficiently short timescales. In this paper, we introduce principal components of connectivity analysis (PCCA), an approach based on running principal component analysis on multiple runs of a time-varying functional connectivity model to show that functional connectivity follows low- yet multi-dimensional trajectories that can be reliably measured, and that these trajectories meet the aforementioned criteria to index flexible communication between neural populations and support moment-to-moment cognition.