To be and not to be: wide-field Ca2+ imaging reveals neocortical functional segmentation combines stability and flexibility

The stability and flexibility of the functional parcellation of the cerebral cortex is fundamental to how familiar and novel information is both represented and stored. We leveraged new advances in Ca2+ sensors and microscopy to understand the dynamics of functional segmentation in the dorsal cerebral cortex. We performed wide-field Ca2+ imaging in head-fixed mice and used spatial independent component analysis (ICA) to identify independent spatial sources of Ca2+ fluorescence. The imaging data were evaluated over multiple timescales and discrete behaviors including resting, walking, and grooming. When evaluated over the entire dataset, a set of template independent components (ICs) were identified that were common across behaviors. Template ICs were present across a range of timescales, from days to 30 seconds, although with lower occurrence probability at shorter timescales, highlighting the stability of the functional segmentation. Importantly, unique ICs emerged at the shorter duration timescales that could act to transiently refine the cortical network. When data were evaluated by behavior, both common and behavior-specific ICs emerged. Each behavior is composed of unique combinations of common and behavior-specific ICs. These observations suggest that cerebral cortical functional segmentation exhibits considerable spatial stability over time and behaviors while retaining the flexibility for task-dependent reorganization.

Brain Activity Reflects Subjective Response to Delayed Input When Using an Electromyography-Controlled Robot

In various experimental settings, electromyography (EMG) signals have been used to control robots. EMG-based robot control requires intrinsic parameters for control, which makes it difficult for users to understand the input protocol. When a proper input is not provided, the response time of the system varies; as such, the user’s subjective delay should be investigated regardless of the actual delay. In this study, we investigated the influence of the subjective perception of delay on brain activation. Brain recordings were taken while subjects used EMG signals to control a robot hand, which requires a basic processing delay. We used muscle synergy for the grip command of the robot hand. After controlling the robot by grasping their hand, one of four additional delay durations (0 ms, 50 ms, 125 ms, and 250 ms) was applied in every trial, and subjects were instructed to answer whether the delay was natural, additional, or whether they were not sure. We compared brain activity based on responses (“sure” and “not sure”). Our results revealed a significant power difference in the theta band of the parietal lobe, and this time range included the interval in which the subjects could not feel the delay. Our study provides important insights that should be considered when constructing an adaptive system and evaluating its usability.

Independent Component Extraction from the Incomplete Coordinate Time Series of Regional GNSS Networks

Independent component analysis (ICA) is one of the most effective approaches in extracting independent signals from a global navigation satellite system (GNSS) regional station network. However, ICA requires the involved time series to be complete, thereby the missing data of incomplete time series should be interpolated beforehand. In this contribution, a modified ICA is proposed, by which the missing data are first recovered based on the reversible property between the original time series and decomposed principal components, then the complete time series are further processed with FastICA. To evaluate the performance of the modified ICA for extracting independent components, 24 regional GNSS network stations located in North China from 2011 to 2019 were selected. After the trend, annual and semiannual terms were removed from the GNSS time series, the first two independent components captured 17.42, 18.44 and 17.38% of the total energy for the North, East and Up coordinate components, more than those derived by the iterative ICA that accounted for 16.21%, 17.72% and 16.93%, respectively. Therefore, modified ICA can extract more independent signals than iterative ICA. Subsequently, selecting the 7 stations with less missing data from the network, we repeatedly process the time series after randomly deleting parts of the data and compute the root mean square error (RMSE) from the differences of reconstructed signals before and after deleting data. All RMSEs of modified ICA are smaller than those of iterative ICA, indicating that modified ICA can extract more exact signals than iterative ICA.

Abnormal default-mode network homogeneity in first-episode, drug-naive schizophrenia at rest

BACKGROUND

Dysconnectivity hypothesis posits that schizophrenia relates to abnormal resting-state connectivity within the default-mode network (DMN) and this aberrant connectivity is considered as contribution of difficulties in self-referential and introspective processing. However, little is known about the alterations of the network homogeneity (NH) of the DMN in schizophrenia. In the present study, we used an automatic NH method to investigate the NH of the DMN in schizophrenia patients at rest.

METHODS

Forty-nine first-episode, drug-naive schizophrenia patients and 50 age-, gender-, and education-matched healthy controls underwent a resting-state functional magnetic resonance imaging (fMRI). An automated NH approach was used to analyze the data.

RESULTS

Patients exhibited lower NH than controls in the left medial prefrontal cortex (MPFC) and the right middle temporal gyrus (MTG). Significantly higher NH values in the left posterior cingulate cortex (PCC) and the right cerebellum Crus I were found in the patient group than in the control group. No significant correlation was found between abnormal NH values and Positive and Negative Symptom Scale (PANSS) scores, duration of untreated psychosis (DUP), age or years of education in the patient group.

CONCLUSIONS

Our findings suggest that abnormal NH of the DMN exists in first-episode, drug-naive schizophrenia and further highlight the importance of the DMN in the pathophysiology of schizophrenia.

Using joint ICA to link function and structure using MEG and DTI in schizophrenia

In this study we employed joint independent component analysis (jICA) to perform a novel multivariate integration of magnetoencephalography (MEG) and diffusion tensor imaging (DTI) data to investigate the link between function and structure. This model-free approach allows one to identify covariation across modalities with different temporal and spatial scales [temporal variation in MEG and spatial variation in fractional anisotropy (FA) maps]. Healthy controls (HC) and patients with schizophrenia (SP) participated in an auditory/visual multisensory integration paradigm to probe cortical connectivity in schizophrenia. To allow direct comparisons across participants and groups, the MEG data were registered to an average head position and regional waveforms were obtained by calculating the local field power of the planar gradiometers. Diffusion tensor images obtained in the same individuals were preprocessed to provide FA maps for each participant. The MEG/FA data were then integrated using the jICA software (http://mialab.mrn.org/software/fit). We identified MEG/FA components that demonstrated significantly different (p<0.05) covariation in MEG/FA data between diagnostic groups (SP vs. HC) and three components that captured the predominant sensory responses in the MEG data. Lower FA values in bilateral posterior parietal regions, which include anterior/posterior association tracts, were associated with reduced MEG amplitude (120-170 ms) of the visual response in occipital sensors in SP relative to HC. Additionally, increased FA in a right medial frontal region was linked with larger amplitude late MEG activity (300-400 ms) in bilateral central channels for SP relative to HC. Step-wise linear regression provided evidence that right temporal, occipital and late central components were significant predictors of reaction time and cognitive performance based on the Measurement and Treatment Research to Improve Cognition in Schizophrenia (MATRICS) cognitive assessment battery. These results point to dysfunction in a posterior visual processing network in schizophrenia, with reduced MEG amplitude, reduced FA and poorer overall performance on the MATRICS. Interestingly, the spatial location of the MEG activity and the associated FA regions are spatially consistent with white matter regions that subserve these brain areas. This novel approach provides evidence for significant pairing between function (neurophysiology) and structure (white matter integrity) and demonstrates that this multivariate, multimodal integration technique is sensitive to group differences in function and structure.