Resting State Functional Connectivity of the Supplementary Motor Area and the Caudate Nucleus in Prodromal Huntington\u27s Disease

Huntington’s disease (HD) is a fatal neurodegenerative genetic disease that causes motor difficulties, mood impairment, and cognitive dysfunction. Prodromal Huntington’s disease (PrHD) refers to people who carry the mutated huntingtin (htt) gene, but do not yet fit the criteria needed for a full diagnosis. Changes in mood typically begin in the prodromal phase, and apathy is a particularly devastating change that progresses in severity throughout the course of the disease. We investigated neural connectivity changes that could be associated with apathy severity within this population. We performed a seed-based connectivity analysis on resting state scans of 89 (PrHD) patients, with the supplementary motor area, bilateral caudate and caudate head as our regions of interest. We found that apathy severity was significantly correlated with increased connectivity between the caudate head and the supplementary motor area (p = 0.03). Further analyses are needed to establish the extent of the effect of caudate atrophy on this relationship, which we would predict would be highly related due to the degenerative nature of the disease

Resting-state Connectivity Dynamics in the Human Brain using High-speed fMRI

Resting-state fMRI using seed-based connectivity analysis (SCA) typically involves regression of the confounding signals resulting from movement and physiological noise sources. This not only adds additional complexity to the analysis but may also introduce possible regression bias. We recently introduced a computationally efficient real-time SCA approach without confound regression, which employs sliding-window correlation analysis with running mean and standard deviation (meta-statistics). The present study characterizes the confound tolerance of this windowed seed-based connectivity analysis (wSCA), which combines efficient decorrelation of confounding signal events with high-pass filter characteristics that reduce sensitivity to drifts. The confound suppression and the strength of resting-state network (RSN) connectivity were characterized for a range of confounding signal profiles as a function of sliding-window width and scan duration, using simulation and in vivo data. The connectivity strength in six resting-state networks (RSNs) and artifactual connectivity in white matter were compared between wSCA and conventional regression-based SCA (cSCA). The wSCA approach demonstrated scalable confound suppression that increased with decreasing sliding-window width and increasing scan duration in both simulations and in vivo. The confound suppression for sliding-window widths ≤ 15 s was comparable to that of cSCA. Twenty-eight RSNs that were previously reported in a group-ICA study were detected in real-time at scan durations as short as 30 s and with sliding-window widths as short as 4 s. The inter- and intra- network connectivity dynamics of the 28 resting-state networks were studied in real-time and self-repeating connectivity patterns were identified. The wSCA is further investigated offline to study the strength and temporal fluctuations in connectivity using 28 single-region seeds and 28 multi-region seed clusters to measure inter-regional connectivity (IRC) in 140 functional brain regions and inter-network connectivity (INC) among the hubs of 28 RSNs. Multi-region seed IRC maps displayed smaller temporal fluctuations and stronger resting-state connectivity compared with single-region seed IRC maps. Dual thresholding of the meta-statistics maps demonstrated higher spatio-temporal IRC stability in auditory, sensorimotor, and visual cortices compared to other brain regions. The group averaged INC matrices for single-region seeds were consistent with the functional network connectivity matrices (FNCMs) presented in the aforementioned group-ICA study. Furthermore, we extended the mapping of functional connectivity to the whole-brain connectivity fingerprints. In combination with novel brain parcellation methods and advanced machine learning algorithms, wSCA can aid in studying the spatial and temporal connectivity dynamics of the resting-state connectivity. The robust confound tolerance, high temporal resolution, and compatibility with real-time high-speed fMRI, make this approach suitable for monitoring data quality, neurofeedback, and clinical research studies involving disease related changes in functional connectomics

Thalamocortical relationship in epileptic patients with generalized spike and wave discharges — A multimodal neuroimaging study

AbstractUnlike focal or partial epilepsy, which has a confined range of influence, idiopathic generalized epilepsy (IGE) often affects the whole or a larger portion of the brain without obvious, known cause. It is important to understand the underlying network which generates epileptic activity and through which epileptic activity propagates. The aim of the present study was to investigate the thalamocortical relationship using non-invasive imaging modalities in a group of IGE patients. We specifically investigated the roles of the mediodorsal nuclei in the thalami and the medial frontal cortex in generating and spreading IGE activities. We hypothesized that the connectivity between these two structures is key in understanding the generation and propagation of epileptic activity in brains affected by IGE. Using three imaging techniques of EEG, fMRI and EEG-informed fMRI, we identified important players in generation and propagation of generalized spike-and-wave discharges (GSWDs). EEG-informed fMRI suggested multiple regions including the medial frontal area near to the anterior cingulate cortex, mediodorsal nuclei of the thalamus, caudate nucleus among others that related to the GSWDs. The subsequent seed-based fMRI analysis revealed a reciprocal cortical and bi-thalamic functional connection. Through EEG-based Granger Causality analysis using (DTF) and adaptive DTF, within the reciprocal thalamocortical circuitry, thalamus seems to serve as a stronger source in driving cortical activity from initiation to the propagation of a GSWD. Such connectivity change starts before the GSWDs and continues till the end of the slow wave discharge. Thalamus, especially the mediodorsal nuclei, may serve as potential targets for deep brain stimulation to provide more effective treatment options for patients with drug-resistant generalized epilepsy

Consciousness-specific dynamic interactions of brain integration and functional diversity

Abstract: Prominent theories of consciousness emphasise different aspects of neurobiology, such as the integration and diversity of information processing within the brain. Here, we combine graph theory and dynamic functional connectivity to compare resting-state functional MRI data from awake volunteers, propofol-anaesthetised volunteers, and patients with disorders of consciousness, in order to identify consciousness-specific patterns of brain function. We demonstrate that cortical networks are especially affected by loss of consciousness during temporal states of high integration, exhibiting reduced functional diversity and compromised informational capacity, whereas thalamo-cortical functional disconnections emerge during states of higher segregation. Spatially, posterior regions of the brain’s default mode network exhibit reductions in both functional diversity and integration with the rest of the brain during unconsciousness. These results show that human consciousness relies on spatio-temporal interactions between brain integration and functional diversity, whose breakdown may represent a generalisable biomarker of loss of consciousness, with potential relevance for clinical practice

Correlating Resting-State Functional Connectivity with Mental Imagery Vividness in a Healthy Population

Mental imagery is the act of using the “mind’s eyes and ears” to generate and experience sensory information that is absent in the external environment. The vividness of mental imagery varies across individuals, but not much is known about what contributes to these differences. This exploratory study investigates the possible relationship between resting-state functional connectivity and the vividness of mental imagery. We performed a seed-based connectivity analysis on resting-state scans of two groups of healthy control subjects with Brodmann area 19, the precuneus, the superior temporal gyrus, the hippocampus, and the posterior cingulate cortex as regions of interest. Although the underlying functional network connectivity was the same across groups, there was no groupwise replication of pairwise connectivities associated with either visual or auditory mental imagery vividness. The lack of replication may be due to a number of factors, but we highlight the impact of asking one group about the vividness of their imagery after each task-based trial and not the other. This may have primed the individuals in the former group to be in a self-referential state of mind during the resting-state scan, affecting the pairwise connectivity relationships to either imagery modality

Unhealthy yet Avoidable-How Cognitive Bias Modification Alters Behavioral and Brain Responses to Food Cues in Individuals with Obesity

Obesity is associated with automatically approaching problematic stimuli, such as unhealthy food. Cognitive bias modification (CBM) could beneficially impact problematic approach behavior. However, it is unclear which mechanisms are targeted by CBM in obesity. Candidate mechanisms include: (1) altering reward value of food stimuli; and (2) strengthening inhibitory abilities. Thirty-three obese adults completed either CBM or sham training during functional magnetic resonance imaging (fMRI) scanning. CBM consisted of implicit training to approach healthy and avoid unhealthy foods. At baseline, approach tendencies towards food were present in all participants. Avoiding vs. approaching food was associated with higher activity in the right angular gyrus (rAG). CBM resulted in a diminished approach bias towards unhealthy food, decreased activation in the rAG, and increased activation in the anterior cingulate cortex. Relatedly, functional connectivity between the rAG and right superior frontal gyrus increased. Analysis of brain connectivity during rest revealed training-related connectivity changes of the inferior frontal gyrus and bilateral middle frontal gyri. Taken together, CBM strengthens avoidance tendencies when faced with unhealthy foods and alters activity in brain regions underpinning behavioral inhibition.Peer reviewe

Network based statistical analysis detects changes induced by continuous theta-burst stimulation on brain activity at rest

We combined continuous theta-burst stimulation (cTBS) and resting state (RS)-fMRI approaches to investigate changes in functional connectivity (FC) induced by right dorsolateral prefrontal cortex (DLPFC)-cTBS at rest in a group of healthy subjects. Seed-based fMRI analysis revealed a specific pattern of correlation between the right prefrontal cortex and several brain regions: based on these results, we defined a 29-node network to assess changes in each network connection before and after, respectively, DLPFC-cTBS and sham sessions. A decrease of correlation between the right prefrontal cortex and right parietal cortex (Brodmann areas 46 and 40, respectively) was detected after cTBS, while no significant result was found when analyzing sham-session data. To our knowledge, this is the first study that demonstrates within-subject changes in FC induced by cTBS applied on prefrontal area. The possibility to induce selective changes in a specific region without interfering with functionally correlated area could have several implications for the study of functional properties of the brain, and for the emerging therapeutic strategies based on transcranial stimulation

Improving Reliability of Subject-Level Resting-State fMRI Parcellation with Shrinkage Estimators

A recent interest in resting state functional magnetic resonance imaging (rsfMRI) lies in subdividing the human brain into anatomically and functionally distinct regions of interest. For example, brain parcellation is often used for defining the network nodes in connectivity studies. While inference has traditionally been performed on group-level data, there is a growing interest in parcellating single subject data. However, this is difficult due to the low signal-to-noise ratio of rsfMRI data, combined with typically short scan lengths. A large number of brain parcellation approaches employ clustering, which begins with a measure of similarity or distance between voxels. The goal of this work is to improve the reproducibility of single-subject parcellation using shrinkage estimators of such measures, allowing the noisy subject-specific estimator to "borrow strength" in a principled manner from a larger population of subjects. We present several empirical Bayes shrinkage estimators and outline methods for shrinkage when multiple scans are not available for each subject. We perform shrinkage on raw intervoxel correlation estimates and use both raw and shrinkage estimates to produce parcellations by performing clustering on the voxels. Our proposed method is agnostic to the choice of clustering method and can be used as a pre-processing step for any clustering algorithm. Using two datasets---a simulated dataset where the true parcellation is known and is subject-specific and a test-retest dataset consisting of two 7-minute rsfMRI scans from 20 subjects---we show that parcellations produced from shrinkage correlation estimates have higher reliability and validity than those produced from raw estimates. Application to test-retest data shows that using shrinkage estimators increases the reproducibility of subject-specific parcellations of the motor cortex by up to 30%.Comment: body 21 pages, 11 figure

Intrinsic functional network contributions to the relationship between trait empathy and subjective happiness

幸福感と共感性を関連付ける安静時脳機能ネットワークの解明 --前頭前皮質の機能的結合性の役割--. 京都大学プレスリリース. 2021-01-08.Subjective happiness (well-being) is a multi-dimensional construct indexing one's evaluations of everyday emotional experiences and life satisfaction, and has been associated with different aspects of trait empathy. Despite previous research identifying the neural substrates of subjective happiness and empathy, the mechanisms mediating the relationship between the two constructs remain largely unclear. Here, we performed a data-driven, multi-voxel pattern analysis of whole-brain intrinsic functional connectivity to reveal the neural mechanisms of subjective happiness and trait empathy in a sample of young females. Behaviorally, we found that subjective happiness was negatively associated with personal distress (i.e., self-referential experience of others’ feelings). Consistent with this inverse relationship, subjective happiness was associated with the dorsolateral prefrontal cortex exhibiting decreased functional connectivity with regions important for the representation of unimodal sensorimotor information (e.g., primary sensory cortices) or multi-modal summaries of brain states (e.g., default mode network) and increased functional connectivity with regions important for the attentional modulation of these representations (e.g., frontoparietal, attention networks). Personal distress was associated with the medial prefrontal cortex exhibiting functional connectivity differences with similar networks––but in the opposite direction. Finally, intrinsic functional connectivity within and between these networks fully mediated the relationship between the two behavioral measures. These results identify an important contribution of the macroscale functional organization of the brain to human well-being, by demonstrating that lower levels of personal distress lead to higher subjective happiness through variation in intrinsic functional connectivity along a neural representation vs. modulation gradient

Reduction of somatosensory functional connectivity by transcranial alternating current stimulation at endogenous mu-frequency

Alpha, the most prominent human brain rhythm, might reflect a mechanism of functional inhibition for gating neural processing. This concept has been derived predominantly from local measures of inhibition, while large-scale network mechanisms to guide information flow are largely unknown. Here, we investigated functional connectivity changes on a whole-brain level by concurrent transcranial alternating current stimulation (tACS) and resting-state functional MRI in humans. We specifically focused on somatosensory alpha-band oscillations by adjusting the tACS frequency to each individual´s somatosensory (mu-) alpha peak frequency (mu-tACS). Potential differences of Eigenvector Centrality of primary somatosensory cortex (S1) as well as on a whole brain level between mu-tACS and sham were analyzed. Our results demonstrate that mu-tACS induces a locally-specific decrease in whole-brain functional connectivity of left S1. An additional exploratory analysis revealed that this effect primarily depends on a decrease in functional connectivity between S1 and a network of regions that are crucially involved in somatosensory processing. Furthermore, the decrease in functional centrality was specific to mu-tACS and was not observed when tACS was applied in the gamma-range in an independent study. Our findings provide evidence that modulated somatosensory (mu-) alpha-activity may affect whole-brain network level activity by decoupling primary sensory areas from other hubs involved in sensory processing