Neuromodulatory subcortical nucleus integrity is associated with white matter microstructure, tauopathy and APOE status.

The neuromodulatory subcortical nuclei within the isodendritic core (IdC) are the earliest sites of tauopathy in Alzheimer's disease (AD). They project broadly throughout the brain's white matter. We investigated the relationship between IdC microstructure and whole-brain white matter microstructure to better understand early neuropathological changes in AD. Using multiparametric quantitative magnetic resonance imaging we observed two covariance patterns between IdC and white matter microstructure in 133 cognitively unimpaired older adults (age 67.9 ± 5.3 years) with familial risk for AD. IdC integrity related to 1) whole-brain neurite density, and 2) neurite orientation dispersion in white matter tracts known to be affected early in AD. Pattern 2 was associated with CSF concentration of phosphorylated-tau, indicating AD specificity. Apolipoprotein-E4 carriers expressed both patterns more strongly than non-carriers. IdC microstructure variation is reflected in white matter, particularly in AD-affected tracts, highlighting an early mechanism of pathological development.

MVComp toolbox: MultiVariate Comparisons of brain MRI features accounting for common information across metrics.

Multivariate approaches have recently gained in popularity to address the physiological unspecificity of neuroimaging metrics and to better characterize the complexity of biological processes underlying behavior. However, commonly used approaches are biased by the intrinsic associations between variables, or they are computationally expensive and may be more complicated to implement than standard univariate approaches. Here, we propose using the Mahalanobis distance (D2), an individual-level measure of deviation relative to a reference distribution that accounts for covariance between metrics. To facilitate its use, we introduce an open-source python-based tool for computing D2 relative to a reference group or within a single individual: the MultiVariate Comparison (MVComp) toolbox. The toolbox allows different levels of analysis (i.e., group- or subject-level), resolutions (e.g., voxel-wise, ROI-wise) and dimensions considered (e.g., combining MRI metrics or WM tracts). Several example cases are presented to showcase the wide range of possible applications of MVComp and to demonstrate the functionality of the toolbox. The D2 framework was applied to the assessment of white matter (WM) microstructure at 1) the group-level, where D2 can be computed between a subject and a reference group to yield an individualized measure of deviation. We observed that clustering applied to D2 in the corpus callosum yields parcellations that highly resemble known topography based on neuroanatomy, suggesting that D2 provides an integrative index that meaningfully reflects the underlying microstructure. 2) At the subject level, D2 was computed between voxels to obtain a measure of (dis)similarity. The loadings of each MRI metric (i.e., its relative contribution to D2) were then extracted in voxels of interest to showcase a useful option of the MVComp toolbox. These relative contributions can provide important insights into the physiological underpinnings of differences observed. Integrative multivariate models are crucial to expand our understanding of the complex brain-behavior relationships and the multiple factors underlying disease development and progression. Our toolbox facilitates the implementation of a useful multivariate method, making it more widely accessible.

Motor sequences; separating the sequence from the motor. A longitudinal rsfMRI study.

In motor learning, sequence specificity, i.e. the learning of specific sequential associations, has predominantly been studied using task-based fMRI paradigms. However, offline changes in resting state functional connectivity after sequence-specific motor learning are less well understood. Previous research has established that plastic changes following motor learning can be divided into stages including fast learning, slow learning and retention. A description of how resting state functional connectivity after sequence-specific motor sequence learning (MSL) develops across these stages is missing. This study aimed to identify plastic alterations in whole-brain functional connectivity after learning a complex motor sequence by contrasting an active group who learned a complex sequence with a control group who performed a control task matched for motor execution. Resting state fMRI and behavioural performance were collected in both groups over the course of 5 consecutive training days and at follow-up after 12 days to encompass fast learning, slow learning, overall learning and retention. Between-group interaction analyses showed sequence-specific decreases in functional connectivity during overall learning in the right supplementary motor area (SMA). We found that connectivity changes in a key region of the motor network, the superior parietal cortex (SPC) were not a result of sequence-specific learning but were instead linked to motor execution. Our study confirms the sequence-specific role of SMA that has previously been identified in online task-based learning studies, and extends it to resting state network changes after sequence-specific MSL.

White matter microstructural changes in short-term learning of a continuous visuomotor sequence.

Efficient neural transmission is crucial for optimal brain function, yet the plastic potential of white matter (WM) has long been overlooked. Growing evidence now shows that modifications to axons and myelin occur not only as a result of long-term learning, but also after short training periods. Motor sequence learning (MSL), a common paradigm used to study neuroplasticity, occurs in overlapping learning stages and different neural circuits are involved in each stage. However, most studies investigating short-term WM plasticity have used a pre-post design, in which the temporal dynamics of changes across learning stages cannot be assessed. In this study, we used multiple magnetic resonance imaging (MRI) scans at 7 T to investigate changes in WM in a group learning a complex visuomotor sequence (LRN) and in a control group (SMP) performing a simple sequence, for five consecutive days. Consistent with behavioral results, where most improvements occurred between the two first days, structural changes in WM were observed only in the early phase of learning (d1-d2), and in overall learning (d1-d5). In LRNs, WM microstructure was altered in the tracts underlying the primary motor and sensorimotor cortices. Moreover, our structural findings in WM were related to changes in functional connectivity, assessed with resting-state functional MRI data in the same cohort, through analyses in regions of interest (ROIs). Significant changes in WM microstructure were found in a ROI underlying the right supplementary motor area. Together, our findings provide evidence for highly dynamic WM plasticity in the sensorimotor network during short-term MSL.

State-Dependent Entrainment of Prefrontal Cortex Local Field Potential Activity Following Patterned Stimulation of the Cerebellar Vermis.

The cerebellum is involved in sensorimotor, cognitive, and emotional functions through cerebello-cerebral connectivity. Cerebellar neurostimulation thus likely affects cortical circuits, as has been shown in studies using cerebellar stimulation to treat neurological disorders through modulation of frontal EEG oscillations. Here we studied the effects of different frequencies of cerebellar stimulation on oscillations and coherence in the cerebellum and prefrontal cortex in the urethane-anesthetized rat. Local field potentials were recorded in the right lateral cerebellum (Crus I/II) and bilaterally in the prefrontal cortex (frontal association area, FrA) in adult male Sprague-Dawley rats. Stimulation was delivered to the cerebellar vermis (lobule VII) using single pulses (0.2 Hz for 60 s), or repeated pulses at 1 Hz (30 s), 5 Hz (10 s), 25 Hz (2 s), and 50 Hz (1 s). Effects of stimulation were influenced by the initial state of EEG activity which varies over time during urethane-anesthesia; 1 Hz stimulation was more effective when delivered during the slow-wave state (Stage 1), while stimulation with single-pulse, 25, and 50 Hz showed stronger effects during the activated state (Stage 2). Single-pulses resulted in increases in oscillatory power in the delta and theta bands for the cerebellum, and in frequencies up to 80 Hz in cortical sites. 1 Hz stimulation induced a decrease in 0-30 Hz activity and increased activity in the 30-200 Hz range, in the right FrA. 5 Hz stimulation reduced power in high frequencies in Stage 1 and induced mixed effects during Stage 2.25 Hz stimulation increased cortical power at low frequencies during Stage 2, and increased power in higher frequency bands during Stage 1. Stimulation at 50 Hz increased delta-band power in all recording sites, with the strongest and most rapid effects in the cerebellum. 25 and 50 Hz stimulation also induced state-dependent effects on cerebello-cortical and cortico-cortical coherence at high frequencies. Cerebellar stimulation can therefore entrain field potential activity in the FrA and drive synchronization of cerebello-cortical and cortico-cortical networks in a frequency-dependent manner. These effects highlight the role of the cerebellar vermis in modulating large-scale synchronization of neural networks in non-motor frontal cortex.