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Abstract Background Surgery may render temporal lobe epilepsy (TLE) patients seizure-free. However, TLE is a heterogenous entity and surgical prognosis varies between patients. Network-based biomarkers have been shown to be altered in TLE patients and hold promise for classifying TLE subtypes and improving pre-surgical prognosis. The aim of the present study is to investigate a network-based biomarker, the weighted degree of connectivity (wDC), on an individual level, and its relation to TLE subtypes and surgical prognosis. Methods Thirty unilateral TLE patients undergoing the same surgical procedure (anterior temporal resection) and 18 healthy controls were included. All patients were followed-up in the same center for a mean time of 6.85 years and classified as seizure-free (SF) and non seizure-free (non-SF). Using pre-surgical resting state functional MRI, whole brain wDC values for patients and controls were calculated. Then, we divided both temporal lobes in three Regions-of-interest (ROIs) -mesial, pole and lateral- as these areas are known to behave differently in seizure onset and propagation, delimiting different TLE profiles. The wDC values for the defined ROIs of each individual patient were compared with the healthy group. Results After surgery, 14 TLE patients remained SF. As a group, patients had higher wDC than controls in both the temporal pole (p

IntroductionLearning new verbal information can be impaired in 20–40% of patients after mesial temporal lobe resection. In recent years, understanding epilepsy as a brain network disease, and investigating the relationship between large-scale resting networks and cognition has led to several advances. Aligned studies suggest that it is the integrity of the hippocampal connectivity with these large-scale networks what is relevant for cognition, with evidence showing a functional and structural heterogeneity along the long axis hippocampus bilaterally.ObjectiveOur aim is to examine whether pre-operative resting-state connectivity along the long hippocampal axis is associated with verbal learning decline after anterior temporal lobe resection.MethodsThirty-one patients with epilepsy who underwent an anterior temporal lobe resection were pre-surgically scanned at 3-tesla, and pre/post-surgery evaluated for learning deficits using the Rey Auditory Verbal Learning Task (RAVLT). Eighteen controls matched by age, gender and handedness were also scanned and evaluated with the RAVLT. We studied the functional connectivity along the (anterior/posterior) long axis hippocampal subregions and resting-state functionally-defined brain networks involved in learning [executive (EXE), dorsal attention (DAN) and default-mode (DMN) networks]. Functional connectivity differences between the two groups of patients (learning intact or with learning decline) and controls were investigated with MANOVA and discriminant analysis.ResultsThere were significant differences in the pattern of hippocampal connectivity among the groups. Regarding the anterior connectivity hippocampal pattern, our data showed an increase of connectivity in the pathological side with the DAN (p = 0.011) and the EXE (p = 0.008) when comparing learning-decline vs. learning-intact patients. Moreover, the non-pathological side showed an increase in the anterior connectivity pattern with the DAN (p = 0.027) between learning-decline vs. learning-intact patients. In contrast, the posterior hippocampus showed a reduction of connectivity in the learning-decline patients with the DMN, both in the pathological (p = 0.004) and the non-pathological sides (p = 0.036). Finally, the discriminant analysis based on the pre-operative connectivity pattern significantly differentiated the learning-decline patients from the other groups (p = 0.019).ConclusionOur findings reveal bilateral connectivity disruptions along the longitudinal axis of the hippocampi with resting-state networks, which could be key to identify those patients at risk of verbal learning decline after epilepsy surgery.

Over the past 2,500 years, contemplative traditions have explored the nature of the mind using meditation. More recently, neuroimaging research on meditation has revealed differences in brain function and structure in meditators. Nevertheless, the underlying neural mechanisms are still unclear. In order to understand how meditation shapes global activity through the brain, we investigated the spatiotemporal dynamics across the whole-brain functional network using the Intrinsic Ignition Framework. Recent neuroimaging studies have demonstrated that different states of consciousness differ in their underlying dynamical complexity, i.e., how the broadness of communication is elicited and distributed through the brain over time and space. In this work, controls and experienced meditators were scanned using functional magnetic resonance imaging (fMRI) during resting-state and meditation (focused attention on breathing). Our results evidenced that the dynamical complexity underlying meditation shows less complexity than during resting-state in the meditator group but not in the control group. Furthermore, we report that during resting-state, the brain activity of experienced meditators showed higher metastability (i.e., a wider dynamical regime over time) than the one observed in the control group. Overall, these results indicate that the meditation state operates in a different dynamical regime compared to the resting-state.

Abstract Background Despite its impact on daily life, impulsivity in Huntington's disease (HD) is understudied as a neuropsychiatric symptom. Our aim is to characterize temporal impulsivity in HD and to disentangle the white matter correlate associated with impulsivity. Methods Forty‐seven HD individuals and 36 healthy controls were scanned and evaluated for temporal impulsivity using a delay‐discounting (DD) task and complementary Sensitivity to Punishment and Sensitivity to Reward Questionnaire. Diffusion tensor imaging was employed to characterize the structural connectivity of three limbic tracts: the uncinate fasciculus (UF), the accumbofrontal tract (NAcc‐OFC), and the dorsolateral prefrontal cortex connectig the caudate nucleus (DLPFC‐cn). Multiple linear regression analyses were applied to analyze the relationship between impulsive behavior and white matter microstructural integrity. Results Our results revealed altered structural connectivity in the DLPC‐cn, the NAcc‐OFC and the UF in HD individuals. At the same time, the variability in structural connectivity of these tracts was associated with the individual differences in temporal impulsivity. Specifically, increased structural connectivity in the right NAcc‐OFC and reduced connectivity in the left UF were associated with higher temporal impulsivity scores. Conclusions The present findings highlight the importance of investigating the spectrum of temporal impulsivity in HD. As, while less prevalent than other psychiatric features, this symptom is still reported to significantly impact the quality of life of patients and caregivers. This study provides evidence that individual differences observed in temporal impulsivity may be explained by variability in limbic frontostriatal tracts, while shedding light on the role of sensitivity to reward in modulating impulsive behavior through the selection of immediate rewards.

A unifying turbulent dynamics framework using both model-free and modelbased measures of whole-brain information provides insights into brain states.

Magnetic resonance imaging (MRI) biomarkers require complex processing routines that are time-consuming and labor-intensive for clinical users. The Single Subject Brain Analysis Toolbox (SeSBAT) is a fully automated MATLAB toolbox with a graphical user interface (GUI) that offers standardized and optimized protocols for the pre-processing and analysis of anatomical MRI data at the single-subject level. In this study, the two-fold strategy provided by SeSBAT is illustrated through its application on a cohort of 42 patients with Huntington’s disease (HD), in pre-manifest and early manifest stages, as a suitable model of neurodegenerative processes. On the one hand, hypothesis-driven analysis can be used to extract biomarkers of neurodegeneration in specific brain regions of interest (ROI-based analysis). On the other hand, an exploratory voxel-based morphometry (VBM) approach can detect volume changes due to neurodegeneration throughout the whole brain (whole-brain analysis). That illustration reveals the potential of SeSBAT in providing potential prognostic biomarkers in neurodegenerative processes in clinics, which could be critical to overcoming the limitations of current qualitative evaluation strategies, and thus improve the diagnosis and monitoring of neurodegenerative disorders. Furthermore, the importance of the availability of tools for characterization at the single-subject level has been emphasized, as there is high interindividual variability in the pattern of neurodegeneration. Thus, tools like SeSBAT could pave the way towards more effective and personalized medicine.

Huntington's disease (HD) is a genetic neurodegenerative disease which involves a triad of motor, cognitive and psychiatric disturbances. However, there is great variability in the prominence of each type of symptom across individuals. The neurobiological basis of such variability remains poorly understood but would be crucial for better tailored treatments. Multivariate multimodal neuroimaging approaches have been successful in disentangling these profiles in other disorders. Thus we applied for the first time such approach to HD. We studied the relationship between HD symptom domains and multimodal measures sensitive to grey and white matter structural alterations. Forty-three HD gene carriers (23 manifest and 20 premanifest individuals) were scanned and underwent behavioural assessments evaluating motor, cognitive and psychiatric domains. We conducted a multimodal analysis integrating different structural neuroimaging modalities measuring grey matter volume, cortical thickness and white matter diffusion indices – fractional anisotropy and radial diffusivity. All neuroimaging measures were entered into a linked independent component analysis in order to obtain multimodal components reflecting common inter-subject variation across imaging modalities. The relationship between multimodal neuroimaging independent components and behavioural measures was analysed using multiple linear regression. We found that cognitive and motor symptoms shared a common neurobiological basis, whereas the psychiatric domain presented a differentiated neural signature. Behavioural measures of different symptom domains correlated with different neuroimaging components, both the brain regions involved and the neuroimaging modalities most prominently associated with each type of symptom showing differences. More severe cognitive and motor signs together were associated with a multimodal component consisting in a pattern of reduced grey matter, cortical thickness and white matter integrity in cognitive and motor related networks. In contrast, depressive symptoms were associated with a component mainly characterised by reduced cortical thickness pattern in limbic and paralimbic regions. In conclusion, using a multivariate multimodal approach we were able to disentangle the neurobiological substrates of two distinct symptom profiles in HD: one characterised by cognitive and motor features dissociated from a psychiatric profile. These results open a new view on a disease classically considered as a uniform entity and initiates a new avenue for further research considering these qualitative individual differences. Keywords: Linked ICA, Data fusion, Huntington's disease, Neurodegeneration, Clinical profiles, Structural MRI

Background: Apathy is the neuropsychiatric syndrome that correlates most highly with Huntington's disease progression, and, like early patterns of neurodegeneration, is associated with lesions to cortico-striatal connections. However, due to its multidimensional nature and elusive etiology, treatment options are limited. Objectives: To disentangle underlying white matter microstructural correlates across the apathy spectrum in Huntington's disease. Methods: Forty-six Huntington's disease individuals (premanifest (N = 22) and manifest (N = 24)) and 35 healthy controls were scanned at 3-tesla and underwent apathy evaluation using the short-Problem Behavior Assessment and short-Lille Apathy Rating Scale, with the latter being characterized into three apathy domains, namely emotional, cognitive, and auto-activation deficit. Diffusion tensor imaging was used to study whether individual differences in specific cortico-striatal tracts predicted global apathy and its subdomains. Results: We elucidate that apathy profiles may develop along differential timelines, with the auto-activation deficit domain manifesting prior to motor onset. Furthermore, diffusion tensor imaging revealed that inter-individual variability in the disruption of discrete cortico-striatal tracts might explain the heterogeneous severity of apathy profiles. Specifically, higher levels of auto-activation deficit symptoms significantly correlated with increased mean diffusivity in the right uncinate fasciculus. Conversely, those with severe cognitive apathy demonstrated increased mean diffusivity in the right frontostriatal tract and left dorsolateral prefrontal cortex to caudate nucleus tract. Conclusions: The current study provides evidence that white matter correlates associated with emotional, cognitive, and auto-activation subtypes may elucidate the heterogeneous nature of apathy in Huntington's disease, as such opening a door for individualized pharmacological management of apathy as a multidimensional syndrome in other neurodegenerative disorders. Keywords: Apathy, Diffusion MRI, Huntington's disease, Individual differences, Neurodegeneration, White matter microstructure

An assortment of human behaviors is thought to be driven by rewards including reinforcement learning, novelty processing, learning, decision making, economic choice, incentive motivation, and addiction. In each case the ventral tegmental area / ventral striatum (Nucleus accumbens) system (VTA-VS) has been implicated as a key structure by functional imaging studies, mostly on the basis of standard, univariate analyses. Here we propose that standard fMRI analysis needs to be complemented by methods that take into account the differential connectivity of the VTA-VS system in the different behavioral contexts in order to describe reward based processes more appropriately. We first consider the wider network for reward processing as it emerged from animal experimentation. Subsequently, an example for a method to assess functional connectivity is given. Finally, we illustrate the usefulness of such analyses by examples regarding reward valuation, reward expectation and the role of reward in addiction.

Controversial results have been reported concerning the neural mechanisms involved in the processing of rewards and punishments. On the one hand, there is evidence suggesting that monetary gains and losses activate a similar fronto-subcortical network. On the other hand, results of recent studies imply that reward and punishment may engage distinct neural mechanisms. Using functional magnetic resonance imaging we investigated both regional and interregional functional connectivity patterns while participants performed a gambling task featuring unexpectedly high monetary gains and losses. Classical univariate statistical analysis showed that monetary gains and losses activated a similar fronto-striatal-limbic network, in which main activation peaks were observed bilaterally in the ventral striatum. Functional connectivity analysis showed similar responses for gain and loss conditions in the insular cortex, the amygdala, and the hippocampus that correlated with the activity observed in the seed region ventral striatum, with the connectivity to the amygdale appearing more pronounced after losses. Larger functional connectivity was found to the medial OFC for negative outcomes. The fact that different functional patterns were obtained with both analyses suggests that the brain activations observed in the classical univariate approach identifies the involvement of different functional networks in a current task. These results stress the importance of studying functional connectivity in addition to standard fMRI analysis in reward-related studies.