Motor versus Psychomotor? Deciphering the Neural Source of Psychomotor Retardation in Depression.

Major depressive disorder (MDD) is characterized by psychomotor retardation whose underlying neural source remains unclear. Psychomotor retardation may either be related to a motor source like the motor cortex or, alternatively, to a psychomotor source with neural changes outside motor regions, like input regions such as visual cortex. These two alternative hypotheses in main (n = 41) and replication (n = 18) MDD samples using 7 Tesla MRI are investigated. Analyzing both global and local connectivity in primary motor cortex (BA4), motor network and middle temporal visual cortex complex (MT+), the main findings in MDD are: 1) Reduced local and global synchronization and increased local-to-global output in motor regions, which do not correlate with psychomotor retardation, though. 2) Reduced local-to-local BA4 - MT+ functional connectivity (FC) which correlates with psychomotor retardation. 3) Reduced global synchronization and increased local-to-global output in MT+ which relate to psychomotor retardation. 4) Reduced variability in the psychophysical measures of MT+ based motion perception which relates to psychomotor retardation. Together, it is shown that visual cortex MT+ and its relation to motor cortex play a key role in mediating psychomotor retardation. This supports psychomotor over motor hypothesis about the neural source of psychomotor retardation in MDD.

A new model for dynamic mapping of effective connectivity in task fMRI.

Whole-brain dynamic functional connectivity is a growing area in neuroimaging research, encompassing data-driven methods for investigating how large-scale brain networks dynamically reorganize during resting states. However, this approach has been rarely applied to functional magnetic resonance imaging (fMRI) data acquired during task performance. In this study, we first combined the psychophysiological interactions (PPI) and sliding-window methods to analyze dynamic effective connectivity of fMRI data obtained from subjects performing the N-back task within the Human Connectome Project dataset. We then proposed a hypothetical model called Condition Activated Specific Trajectory (CAST) to represent a series of spatiotemporal synchronous changes in significantly activated connections across time windows, which we refer to as a trajectory. Our finding demonstrate that the CAST model outperforms other models in terms of intra-group consistency of individual spatial pattern of PPI connectivity, overall representational ability of temporal variability and hierarchy for individual task performance and cognitive traits. This dynamic view afforded by the CAST model reflects the intrinsic nature of coherent brain activities.

Recognizing mild cognitive impairment based on network connectivity analysis of resting EEG with zero reference.

The diagnosis of mild cognitive impairment (MCI) is very helpful for early therapeutic interventions of Alzheimer's disease (AD). MCI has been proven to be correlated with disorders in multiple brain areas. In this paper, we used information from resting brain networks at different EEG frequency bands to reliably recognize MCI. Because EEG network analysis is influenced by the reference that is used, we also evaluate the effect of the reference choices on the resting scalp EEG network-based MCI differentiation. The conducted study reveals two aspects: (1) the network-based MCI differentiation is superior to the previously reported classification that uses coherence in the EEG; and (2) the used EEG reference influences the differentiation performance, and the zero approximation technique (reference electrode standardization technique, REST) can construct a more accurate scalp EEG network, which results in a higher differentiation accuracy for MCI. This study indicates that the resting scalp EEG-based network analysis could be valuable for MCI recognition in the future.

An ensemble with the Chinese pentatonic scale using electroencephalogram from both hemispheres.

To listen to brain activity as a piece of music, we previously proposed scale-free brainwave music (SFBM) technology, which translated the scalp electroencephalogram (EEG) into musical notes according to the power law of both the EEG and music. In this study, the methodology was further extended to ensemble music on two channels from the two hemispheres. EEG data from two channels symmetrically located on the left and right hemispheres were translated into MIDI sequences by SFBM, and the EEG parameters modulated the pitch, duration and volume of each note. Then, the two sequences were filtered into an ensemble with two voices: the pentatonic scale (traditional Chinese music) or the heptatonic scale (standard Western music). We demonstrated differences in harmony between the two scales generated at different sleep stages, with the pentatonic scale being more harmonious. The harmony intervals of this brain ensemble at various sleep stages followed the power law. Compared with the heptatonic scale, it was easier to distinguish the different stages using the pentatonic scale. These results suggested that the hemispheric ensemble can represent brain activity by variations in pitch, tempo and harmony. The ensemble with the pentatonic scale sounds more consonant, and partially reflects the relations of the two hemispheres. This can be used to distinguish the different states of brain activity and provide a new perspective on EEG analysis.

Musical training induces functional plasticity in perceptual and motor networks: insights from resting-state FMRI.

A number of previous studies have examined music-related plasticity in terms of multi-sensory and motor integration but little is known about the functional and effective connectivity patterns of spontaneous intrinsic activity in these systems during the resting state in musicians. Using functional connectivity and Granger causal analysis, functional and effective connectivity among the motor and multi-sensory (visual, auditory and somatosensory) cortices were evaluated using resting-state functional magnetic resonance imaging (fMRI) in musicians and non-musicians. The results revealed that functional connectivity was significantly increased in the motor and multi-sensory cortices of musicians. Moreover, the Granger causality results demonstrated a significant increase outflow-inflow degree in the auditory cortex with the strongest causal outflow pattern of effective connectivity being found in musicians. These resting state fMRI findings indicate enhanced functional integration among the lower-level perceptual and motor networks in musicians, and may reflect functional consolidation (plasticity) resulting from long-term musical training, involving both multi-sensory and motor functional integration.

The heterogeneity of aging brain: altered functional connectivity in default mode network in older adults during verbal fluency tests.

BACKGROUND: Successful aging (SA) and mild cognitive impairment (MCI) are heterogeneous groups of aging. To explore the heterogeneity, the functional connectivity was studied in these populations. METHODS: The present study utilized functional connectivity magnetic resonance imaging (fcMRI) to investigate default mode network (DMN) in 8 healthy subjects of SA, 8 subjects of usual aging (UA), and 8 MCI patients during verbal fluency tests (VFTs). Functional connectivity (based seeds) of different groups was analyzed by using statistical test. RESULTS: Compared with SA and UA groups, MCI subjects exhibited decreased functional connectivity in the DMN regions, including the inferior parietal lobule and left angular gyrus (t = 3.53, P < 0.001). Compared with UA and MCI groups, the SA elderly exhibited increased functional connectivity in the precuneus (t = 3.53, P < 0.001). CONCLUSIONS: These findings suggested that abnormalities of functional connectivity in DMN might be related with semantic memory impairment in aging. Left angular gyrus and precuneus might be the potential imaging-based biomarker for distinguishing heterogeneous process of elderly.

Direct MRI detection of the neuronal magnetic field: the effect of the dendrite branch.

In recent years, neuronal current MRI (nc-MRI) was proposed as a new imaging method to directly map the magnetic field change caused by neuronal activity. Nc-MRI could offer improved spatial and temporal resolution compared to blood hemodynamics-based functional magnetic resonance imaging (fMRI). In this paper, with a finite current dipole as the model of dendrite or dendrite branch, we investigated the spatial distribution of the magnetic field generated by synchronously activated neurons to evaluate the possibility of nc-MRI. Our simulations imply that the existence of a dendrite branch may not only increase the strength of the neuronal magnetic field (NMF), but also raise the non-uniform and unsymmetry of the NMF; therefore, it can enhance the detectability of the neuronal current magnetic field by MRI directly. The results show that the signal phase shift is enlarged, but it is unstable and is still very small, <<1 radian, while the magnitude signal may be strong enough for a typical MRI voxel to be detected. We suggest making further efforts to measure the magnitude signal which may induce a large effect in an nc-MRI experiment.

Scale-free music of the brain.

BACKGROUND: There is growing interest in the relation between the brain and music. The appealing similarity between brainwaves and the rhythms of music has motivated many scientists to seek a connection between them. A variety of transferring rules has been utilized to convert the brainwaves into music; and most of them are mainly based on spectra feature of EEG. METHODOLOGY/PRINCIPAL FINDINGS: In this study, audibly recognizable scale-free music was deduced from individual Electroencephalogram (EEG) waveforms. The translation rules include the direct mapping from the period of an EEG waveform to the duration of a note, the logarithmic mapping of the change of average power of EEG to music intensity according to the Fechner's law, and a scale-free based mapping from the amplitude of EEG to music pitch according to the power law. To show the actual effect, we applied the deduced sonification rules to EEG segments recorded during rapid-eye movement sleep (REM) and slow-wave sleep (SWS). The resulting music is vivid and different between the two mental states; the melody during REM sleep sounds fast and lively, whereas that in SWS sleep is slow and tranquil. 60 volunteers evaluated 25 music pieces, 10 from REM, 10 from SWS and 5 from white noise (WN), 74.3% experienced a happy emotion from REM and felt boring and drowsy when listening to SWS, and the average accuracy for all the music pieces identification is 86.8%(kappa = 0.800, P<0.001). We also applied the method to the EEG data from eyes closed, eyes open and epileptic EEG, and the results showed these mental states can be identified by listeners. CONCLUSIONS/SIGNIFICANCE: The sonification rules may identify the mental states of the brain, which provide a real-time strategy for monitoring brain activities and are potentially useful to neurofeedback therapy.

Upregulation of Mark3 and Rpgrip1 mRNA expression by jujuboside A in mouse hippocampus.

AIM: To investigate the effect of jujuboside A (JuA) on modulating gene expression in the hippocampus. METHODS: The spontaneous activity of mice was monitored, and the differential display polymerase chain reaction was adapted to screen differentially-expressed genes modulated by JuA in the mouse hippocampus. RESULTS: JuA significantly decreased the total activity intensity (P<0.01 vs control) at a dosage of 80 mg/kg, and the genes MAP/microtubule affinity-regulating kinase3 (Mark3) and retinitis pigmentosa GTPase regulator interacting protein1 (Rpgrip1) were upregulated by JuA in the mouse hippocampus. CONCLUSION: JuA had an inhibitory effect on the spontaneous activity of the mice, and JuA regulated the transcription of Mark3 and Rpgrip1 in the mouse hippocampus.