Cognitive and White-Matter Compartment Models Reveal Selective Relations between Corticospinal Tract Microstructure and Simple Reaction Time.

The speed of motor reaction to an external stimulus varies substantially between individuals and is slowed in aging. However, the neuroanatomical origins of interindividual variability in reaction time (RT) remain unclear. Here, we combined a cognitive model of RT and a biophysical compartment model of diffusion-weighted MRI (DWI) to characterize the relationship between RT and microstructure of the corticospinal tract (CST) and the optic radiation (OR), the primary motor output and visual input pathways associated with visual-motor responses. We fitted an accumulator model of RT to 46 female human participants' behavioral performance in a simple reaction time task. The non-decision time parameter (Ter) derived from the model was used to account for the latencies of stimulus encoding and action initiation. From multi-shell DWI data, we quantified tissue microstructure of the CST and OR with the neurite orientation dispersion and density imaging (NODDI) model as well as the conventional diffusion tensor imaging model. Using novel skeletonization and segmentation approaches, we showed that DWI-based microstructure metrics varied substantially along CST and OR. The Ter of individual participants was negatively correlated with the NODDI measure of the neurite density in the bilateral superior CST. Further, we found no significant correlation between the microstructural measures and mean RT. Thus, our findings suggest a link between interindividual differences in sensorimotor speed and selective microstructural properties in white-matter tracts.SIGNIFICANCE STATEMENT How does our brain structure contribute to our speed to react? Here, we provided anatomically specific evidence that interindividual differences in response speed is associated with white-matter microstructure. Using a cognitive model of reaction time (RT), we estimated the non-decision time, as an index of the latencies of stimulus encoding and action initiation, during a simple reaction time task. Using an advanced microstructural model for diffusion MRI, we estimated the tissue properties and their variations along the corticospinal tract and optic radiation. We found significant location-specific correlations between the microstructural measures and the model-derived parameter of non-decision time but not mean RT. These results highlight the neuroanatomical signature of interindividual variability in response speed along the sensorimotor pathways.

Critical Comments on EEG Sensor Space Dynamical Connectivity Analysis.

Many different analysis techniques have been developed and applied to EEG recordings that allow one to investigate how different brain areas interact. One particular class of methods, based on the linear parametric representation of multiple interacting time series, is widely used to study causal connectivity in the brain. However, the results obtained by these methods should be interpreted with great care. The goal of this paper is to show, both theoretically and using simulations, that results obtained by applying causal connectivity measures on the sensor (scalp) time series do not allow interpretation in terms of interacting brain sources. This is because (1) the channel locations cannot be seen as an approximation of a source's anatomical location and (2) spurious connectivity can occur between sensors. Although many measures of causal connectivity derived from EEG sensor time series are affected by the latter, here we will focus on the well-known time domain index of Granger causality (GC) and on the frequency domain directed transfer function (DTF). Using the state-space framework and designing two simulation studies we show that mixing effects caused by volume conduction can lead to spurious connections, detected either by time domain GC or by DTF. Therefore, GC/DTF causal connectivity measures should be computed at the source level, or derived within analysis frameworks that model the effects of volume conduction. Since mixing effects can also occur in the source space, it is advised to combine source space analysis with connectivity measures that are robust to mixing.

The interindividual variability of multimodal brain connectivity maintains spatial heterogeneity and relates to tissue microstructure.

Humans differ from each other in a wide range of biometrics, but to what extent brain connectivity varies between individuals remains largely unknown. By combining diffusion-weighted imaging (DWI) and magnetoencephalography (MEG), this study characterizes the inter-subject variability (ISV) of multimodal brain connectivity. Structural connectivity is characterized by higher ISV in association cortices including the core multiple-demand network and lower ISV in the sensorimotor cortex. MEG ISV exhibits frequency-dependent signatures, and the extent of MEG ISV is consistent with that of structural connectivity ISV in selective macroscopic cortical clusters. Across the cortex, the ISVs of structural connectivity and beta-band MEG functional connectivity are negatively associated with cortical myelin content indexed by the quantitative T1 relaxation rate measured by high-resolution 7 T MRI. Furthermore, MEG ISV from alpha to gamma bands relates to the hindrance and restriction of the white-matter tissue estimated by DWI microstructural models. Our findings depict the inter-relationship between the ISV of brain connectivity from multiple modalities, and highlight the role of tissue microstructure underpinning the ISV.

Gait Influence Diagrams in Parkinson's Disease.

Previous studies have shown that gait patterns differ between Parkinson's disease (PD) patients and controls. However, almost all these studies focused only on univariate time series of a single variable. This approach cannot reveal detailed information of foot loading dynamics and the cooperative relationships of different anatomical plantar foot areas when the subjects walk. By contrast, we propose a novel multivariate method for analyzing gait patterns of the PD patients: Gait Influence Diagrams (GIDs). These are constructed by analyzing the Wiener-Akaike-Granger- Schweder influences between vertical ground reaction force signals at different plantar areas of both feet. In this paper, we use the particular case of WAGS influence measures known as "extended Granger causality analysis". GIDs are directed graphs, with arrows indicating those influences that are significantly different between PD patients and healthy subjects. We confirm prior clinical observations that Parkinsonian gait differs significantly from the healthy one in the anterior-posterior movement direction. A new finding is that there are also pathological changes in the lateral-medial direction. Importantly, gait asymmetry for the PD patients is clearly evident in GIDs, even in earlier stages of the disease. These results suggest that GID might be of use in future PD gait pattern studies.

Achromatic temporal-frequency responses of human lateral geniculate nucleus and primary visual cortex.

The sensitivity of the sensory systems to temporal changes of the environment constitutes one of the critical issues in perception. In the present study, we investigated the human early visual system's dependency on the temporal frequency of visual input using fMRI. Blood oxygen level-dependent (BOLD) responses of the lateral geniculate nucleus (LGN) and primary visual cortex (V1) were investigated in a wide frequency range (6-46Hz) with fine frequency sampling (13 frequencies). Subject-specific functional-anatomic ROIs were derived from the combination of the anatomic template masks and the functional maps derived from multi-session fMRI analyses across all 13 stimulation conditions. Using functional-anatomic ROIs, average responses of LGN and V1 were calculated for each frequency. The V1 surface area was further parsed into 7 eccentricity sectors to detail central and peripheral responses. LGN's response revealed fluctuations on a background of non-significant decrease of the BOLD response with increasing stimulation frequency, while V1 response displayed similar fluctuations with a global maximum in the range of 8-12Hz, but a rapid and significant decrease with increasing stimulation frequency especially above 14Hz. This behavior of V1 response valid for both central and peripheral vision emphasizes that the profound low-pass effect of the visual system to visual input emerges in V1, presumably generated by the intra-cortical circuitry of V1 or projections from extra-striate areas. Besides, the high correlation between LGN and V1 BOLD responses across all visual stimulation frequencies supports the oscillatory tuning in thalamo-cortical interactions as previously claimed in electrophysiological studies.

Simultaneous EEG/fMRI analysis of the resonance phenomena in steady-state visual evoked responses.

The stability of the steady-state visual evoked potentials (SSVEPs) across trials and subjects makes them a suitable tool for the investigation of the visual system. The reproducible pattern of the frequency characteristics of SSVEPs shows a global amplitude maximum around 10 Hz and additional local maxima around 20 and 40 Hz, which have been argued to represent resonant behavior of damped neuronal oscillators. Simultaneous electroencephalogram/functional magnetic resonance imaging (EEG/fMRI) measurement allows testing of the resonance hypothesis about the frequency-selective increases in SSVEP amplitudes in human subjects, because the total synaptic activity that is represented in the fMRI-Blood Oxygen Level Dependent (fMRI-BOLD) response would not increase but get synchronized at the resonance frequency. For this purpose, 40 healthy volunteers were visually stimulated with flickering light at systematically varying frequencies between 6 and 46 Hz, and the correlations between SSVEP amplitudes and the BOLD responses were computed. The SSVEP frequency characteristics of all subjects showed 3 frequency ranges with an amplitude maximum in each of them, which roughly correspond to alpha, beta and gamma bands of the EEG. The correlation maps between BOLD responses and SSVEP amplitude changes across the different stimulation frequencies within each frequency band showed no significant correlation in the alpha range, while significant correlations were obtained in the primary visual area for the beta and gamma bands. This non-linear relationship between the surface recorded SSVEP amplitudes and the BOLD responses of the visual cortex at stimulation frequencies around the alpha band supports the view that a resonance at the tuning frequency of the thalamo-cortical alpha oscillator in the visual system is responsible for the global amplitude maximum of the SSVEP around 10 Hz. Information gained from the SSVEP/fMRI analyses in the present study might be extrapolated to the EEG/fMRI analysis of the transient event-related potentials (ERPs) in terms of expecting more reliable and consistent correlations between EEG and fMRI responses, when the analyses are carried out on evoked or induced oscillations (spectral perturbations) in separate frequency bands instead of the time-domain ERP peaks.