Reorganization of thalamocortical connections in congenitally blind humans.

Cross-modal plasticity in blind individuals has been reported over the past decades showing that nonvisual information is carried and processed by "visual" brain structures. However, despite multiple efforts, the structural underpinnings of cross-modal plasticity in congenitally blind individuals remain unclear. We mapped thalamocortical connectivity and assessed the integrity of white matter of 10 congenitally blind individuals and 10 sighted controls. We hypothesized an aberrant thalamocortical pattern of connectivity taking place in the absence of visual stimuli from birth as a potential mechanism of cross-modal plasticity. In addition to the impaired microstructure of visual white matter bundles, we observed structural connectivity changes between the thalamus and occipital and temporal cortices. Specifically, the thalamic territory dedicated to connections with the occipital cortex was smaller and displayed weaker connectivity in congenitally blind individuals, whereas those connecting with the temporal cortex showed greater volume and increased connectivity. The abnormal pattern of thalamocortical connectivity included the lateral and medial geniculate nuclei and the pulvinar nucleus. For the first time in humans, a remapping of structural thalamocortical connections involving both unimodal and multimodal thalamic nuclei has been demonstrated, shedding light on the possible mechanisms of cross-modal plasticity in humans. The present findings may help understand the functional adaptations commonly observed in congenitally blind individuals.

One session of fMRI-Neurofeedback training on motor imagery modulates whole-brain effective connectivity and dynamical complexity.

In the past decade, several studies have shown that Neurofeedback (NFB) by functional magnetic resonance imaging can alter the functional coupling of targeted and non-targeted areas. However, the causal mechanisms underlying these changes remain uncertain. Here, we applied a whole-brain dynamical model to estimate Effective Connectivity (EC) profiles of resting-state data acquired before and immediately after a single-session NFB training for 17 participants who underwent motor imagery NFB training and 16 healthy controls who received sham feedback. Within-group and between-group classification analyses revealed that only for the NFB group it was possible to accurately discriminate between the 2 resting-state sessions. NFB training-related signatures were reflected in a support network of direct connections between areas involved in reward processing and implicit learning, together with regions belonging to the somatomotor, control, attention, and default mode networks, identified through a recursive-feature elimination procedure. By applying a data-driven approach to explore NFB-induced changes in spatiotemporal dynamics, we demonstrated that these regions also showed decreased switching between different brain states (i.e. metastability) only following real NFB training. Overall, our findings contribute to the understanding of NFB impact on the whole brain's structure and function by shedding light on the direct connections between brain areas affected by NFB training.

Meditation-induced effects on whole-brain structural and effective connectivity.

In the past decades, there has been a growing scientific interest in characterizing neural correlates of meditation training. Nonetheless, the mechanisms underlying meditation remain elusive. In the present work, we investigated meditation-related changes in functional dynamics and structural connectivity (SC). For this purpose, we scanned experienced meditators and control (naive) subjects using magnetic resonance imaging (MRI) to acquire structural and functional data during two conditions, resting-state and meditation (focused attention on breathing). In this way, we aimed to characterize and distinguish both short-term and long-term modifications in the brain's structure and function. First, to analyze the fMRI data, we calculated whole-brain effective connectivity (EC) estimates, relying on a dynamical network model to replicate BOLD signals' spatio-temporal structure, akin to functional connectivity (FC) with lagged correlations. We compared the estimated EC, FC, and SC links as features to train classifiers to predict behavioral conditions and group identity. Then, we performed a network-based analysis of anatomical connectivity. We demonstrated through a machine-learning approach that EC features were more informative than FC and SC solely. We showed that the most informative EC links that discriminated between meditators and controls involved several large-scale networks mainly within the left hemisphere. Moreover, we found that differences in the functional domain were reflected to a smaller extent in changes at the anatomical level as well. The network-based analysis of anatomical pathways revealed strengthened connectivity for meditators compared to controls between four areas in the left hemisphere belonging to the somatomotor, dorsal attention, subcortical and visual networks. Overall, the results of our whole-brain model-based approach revealed a mechanism underlying meditation by providing causal relationships at the structure-function level.

Using neurofeedback to induce and explore brain plasticity.

A recent study by Sampaio-Baptista and colleagues showed that bidirectionally white matter plasticity can be elicited 24 h after a short regime of neurofeedback (NF) training in healthy individuals. The findings reinforce NF as a tool to induce brain plasticity while highlighting it as a promising intervention for clinical populations.

Predictors of real-time fMRI neurofeedback performance and improvement - A machine learning mega-analysis.

Real-time fMRI neurofeedback is an increasingly popular neuroimaging technique that allows an individual to gain control over his/her own brain signals, which can lead to improvements in behavior in healthy participants as well as to improvements of clinical symptoms in patient populations. However, a considerably large ratio of participants undergoing neurofeedback training do not learn to control their own brain signals and, consequently, do not benefit from neurofeedback interventions, which limits clinical efficacy of neurofeedback interventions. As neurofeedback success varies between studies and participants, it is important to identify factors that might influence neurofeedback success. Here, for the first time, we employed a big data machine learning approach to investigate the influence of 20 different design-specific (e.g. activity vs. connectivity feedback), region of interest-specific (e.g. cortical vs. subcortical) and subject-specific factors (e.g. age) on neurofeedback performance and improvement in 608 participants from 28 independent experiments. With a classification accuracy of 60% (considerably different from chance level), we identified two factors that significantly influenced neurofeedback performance: Both the inclusion of a pre-training no-feedback run before neurofeedback training and neurofeedback training of patients as compared to healthy participants were associated with better neurofeedback performance. The positive effect of pre-training no-feedback runs on neurofeedback performance might be due to the familiarization of participants with the neurofeedback setup and the mental imagery task before neurofeedback training runs. Better performance of patients as compared to healthy participants might be driven by higher motivation of patients, higher ranges for the regulation of dysfunctional brain signals, or a more extensive piloting of clinical experimental paradigms. Due to the large heterogeneity of our dataset, these findings likely generalize across neurofeedback studies, thus providing guidance for designing more efficient neurofeedback studies specifically for improving clinical neurofeedback-based interventions. To facilitate the development of data-driven recommendations for specific design details and subpopulations the field would benefit from stronger engagement in open science research practices and data sharing.

Can we predict real-time fMRI neurofeedback learning success from pretraining brain activity?

Neurofeedback training has been shown to influence behavior in healthy participants as well as to alleviate clinical symptoms in neurological, psychosomatic, and psychiatric patient populations. However, many real-time fMRI neurofeedback studies report large inter-individual differences in learning success. The factors that cause this vast variability between participants remain unknown and their identification could enhance treatment success. Thus, here we employed a meta-analytic approach including data from 24 different neurofeedback studies with a total of 401 participants, including 140 patients, to determine whether levels of activity in target brain regions during pretraining functional localizer or no-feedback runs (i.e., self-regulation in the absence of neurofeedback) could predict neurofeedback learning success. We observed a slightly positive correlation between pretraining activity levels during a functional localizer run and neurofeedback learning success, but we were not able to identify common brain-based success predictors across our diverse cohort of studies. Therefore, advances need to be made in finding robust models and measures of general neurofeedback learning, and in increasing the current study database to allow for investigating further factors that might influence neurofeedback learning.

Prospective motion correction of fMRI: Improving the quality of resting state data affected by large head motion.

The quality of functional MRI (fMRI) data is affected by head motion. It has been shown that fMRI data quality can be improved by prospectively updating the gradients and radio-frequency pulses in response to head motion during image acquisition by using an MR-compatible optical tracking system (prospective motion correction, or PMC). Recent studies showed that PMC improves the temporal Signal to Noise Ratio (tSNR) of resting state fMRI data (rs-fMRI) acquired from subjects not moving intentionally. Besides that, the time courses of Independent Components (ICs), resulting from Independent Component Analysis (ICA), were found to present significant temporal correlation with the motion parameters recorded by the camera. However, the benefits of applying PMC for improving the quality of rs-fMRI acquired under large head movements and its effects on resting state networks (RSN) and connectivity matrices are still unknown. In this study, subjects were instructed to cross their legs at will while rs-fMRI data with and without PMC were acquired, which generated head motion velocities ranging from 4 to 30 â€‹mm/s. We also acquired fMRI data without intentional motion. Independent component analysis of rs-fMRI was performed to evaluate IC maps and time courses of RSNs. We also calculated the temporal correlation among different brain regions and generated connectivity matrices for the different motion and PMC conditions. In our results we verified that the crossing leg movements reduced the tSNR of sessions without and with PMC by 45 and 20%, respectively, when compared to sessions without intentional movements. We have verified an interaction between head motion speed and PMC status, showing stronger attenuation of tSNR for acquisitions without PMC than for those with PMC. Additionally, the spatial definition of major RSNs, such as default mode, visual, left and right central executive networks, was improved when PMC was enabled. Furthermore, motion altered IC-time courses by decreasing power at low frequencies and increasing power at higher frequencies (typically associated with artefacts). PMC partially reversed these alterations of the power spectra. Finally, we showed that PMC provides temporal correlation matrices for data acquired under motion conditions more comparable to those obtained by fMRI sessions where subjects were instructed not to move.

Cortical lateralization of cheirosensory processing in callosal dysgenesis.

The paradoxical absence of a split-brain syndrome in most cases of callosal dysgenesis has originated three main hypotheses, namely, (i) bilateral cortical representation of language, (ii) bilateral thalamocortical projections of somatosensory pathways conveyed by the spinothalamic-medial lemniscus system, and (iii) a variable combination of (i) and (ii). We used functional neuroimaging to investigate the cortical representation and lateralization of somatosensory information from the palm of each hand in six cases of callosal dysgenesis (hypothesis [ii]). Cortical regions of interest were contralateral and ipsilateral S1 (areas 3a and 3b, 1 and 2 in the central sulcus and postcentral gyrus) and S2 (parts of areas 40 and 43 in the parietal operculum). The degree of cortical asymmetry was expressed by a laterality index (LI), which may assume values from -1 (fully left-lateralized) to +1 (fully right-lateralized). In callosal dysgenesis, LI values for the right and the left hands were, respectively, -1 and + 1 for both S1 and S2, indicating absence of engagement of ipsilateral S1 and S2. In controls, LI values were - 0.70 (S1) and - 0.51 (S2) for right hand stimulation, and 0.82 (S1) and 0.36 (S2) for left hand stimulation, reflecting bilateral asymmetric activations, which were significantly higher in the hemisphere contralateral to the stimulated hand. Therefore, none of the main hypotheses so far entertained to account for the callosal dysgenesis-split-brain paradox have succeeded. We conclude that the preserved interhemispheric transfer of somatosensory tactile information in callosal dysgenesis must be mediated by a fourth alternative, such as aberrant interhemispheric bundles, reorganization of subcortical commissures, or both.

Increase in Short-Interval Intracortical Facilitation of the Motor Cortex after Low-Frequency Repetitive Magnetic Stimulation of the Unaffected Hemisphere in the Subacute Phase after Stroke.

Low-frequency repetitive transcranial magnetic stimulation of the unaffected hemisphere (UH-LF-rTMS) in patients with stroke can decrease interhemispheric inhibition from the unaffected to the affected hemisphere and improve hand dexterity and strength of the paretic hand. The objective of this proof-of-principle study was to explore, for the first time, effects of UH-LF-rTMS as add-on therapy to motor rehabilitation on short-term intracortical inhibition (SICI) and intracortical facilitation (ICF) of the motor cortex of the unaffected hemisphere (M1UH) in patients with ischemic stroke. Eighteen patients were randomized to receive, immediately before rehabilitation treatment, either active or sham UH-LF-rTMS, during two weeks. Resting motor threshold (rMT), SICI, and ICF were measured in M1UH before the first session and after the last session of treatment. There was a significant increase in ICF in the active group compared to the sham group after treatment, and there was no significant differences in changes in rMT or SICI. ICF is a measure of intracortical synaptic excitability, with a relative contribution of spinal mechanisms. ICF is typically upregulated by glutamatergic agonists and downregulated by gabaergic antagonists. The observed increase in ICF in the active group, in this hypothesis-generating study, may be related to M1UH reorganization induced by UH-LF-rTMS.

Enhancing Motor Network Activity Using Real-Time Functional MRI Neurofeedback of Left Premotor Cortex.

Neurofeedback by functional magnetic resonance imaging (fMRI) is a technique of potential therapeutic relevance that allows individuals to be aware of their own neurophysiological responses and to voluntarily modulate the activity of specific brain regions, such as the premotor cortex (PMC), important for motor recovery after brain injury. We investigated (i) whether healthy human volunteers are able to up-regulate the activity of the left PMC during a right hand finger tapping motor imagery (MI) task while receiving continuous fMRI-neurofeedback, and (ii) whether successful modulation of brain activity influenced non-targeted motor control regions. During the MI task, participants of the neurofeedback group (NFB) received ongoing visual feedback representing the level of fMRI responses within their left PMC. Control (CTL) group participants were shown similar visual stimuli, but these were non-contingent on brain activity. Both groups showed equivalent levels of behavioral ratings on arousal and MI, before and during the fMRI protocol. In the NFB, but not in CLT group, brain activation during the last run compared to the first run revealed increased activation in the left PMC. In addition, the NFB group showed increased activation in motor control regions extending beyond the left PMC target area, including the supplementary motor area, basal ganglia and cerebellum. Moreover, in the last run, the NFB group showed stronger activation in the left PMC/inferior frontal gyrus when compared to the CTL group. Our results indicate that modulation of PMC and associated motor control areas can be achieved during a single neurofeedback-fMRI session. These results contribute to a better understanding of the underlying mechanisms of MI-based neurofeedback training, with direct implications for rehabilitation strategies in severe brain disorders, such as stroke.

Structural and functional brain rewiring clarifies preserved interhemispheric transfer in humans born without the corpus callosum.

Why do humans born without the corpus callosum, the major interhemispheric commissure, lack the disconnection syndrome classically described in callosotomized patients? This paradox was discovered by Nobel laureate Roger Sperry in 1968, and has remained unsolved since then. To tackle the hypothesis that alternative neural pathways could explain this puzzle, we investigated patients with callosal dysgenesis using structural and functional neuroimaging, as well as neuropsychological assessments. We identified two anomalous white-matter tracts by deterministic and probabilistic tractography, and provide supporting resting-state functional neuroimaging and neuropsychological evidence for their functional role in preserved interhemispheric transfer of complex tactile information, such as object recognition. These compensatory pathways connect the homotopic posterior parietal cortical areas (Brodmann areas 39 and surroundings) via the posterior and anterior commissures. We propose that anomalous brain circuitry of callosal dysgenesis is determined by long-distance plasticity, a set of hardware changes occurring in the developing brain after pathological interference. So far unknown, these pathological changes somehow divert growing axons away from the dorsal midline, creating alternative tracts through the ventral forebrain and the dorsal midbrain midline, with partial compensatory effects to the interhemispheric transfer of cortical function.