Inducing a meditative state by artificial perturbations: A mechanistic understanding of brain dynamics underlying meditation.

Contemplative neuroscience has increasingly explored meditation using neuroimaging. However, the brain mechanisms underlying meditation remain elusive. Here, we implemented a mechanistic framework to explore the spatiotemporal dynamics of expert meditators during meditation and rest, and controls during rest. We first applied a model-free approach by defining a probabilistic metastable substate (PMS) space for each condition, consisting of different probabilities of occurrence from a repertoire of dynamic patterns. Moreover, we implemented a model-based approach by adjusting the PMS of each condition to a whole-brain model, which enabled us to explore in silico perturbations to transition from resting-state to meditation and vice versa. Consequently, we assessed the sensitivity of different brain areas regarding their perturbability and their mechanistic local-global effects. Overall, our work reveals distinct whole-brain dynamics in meditation compared to rest, and how transitions can be induced with localized artificial perturbations. It motivates future work regarding meditation as a practice in health and as a potential therapy for brain disorders.

Our work explores brain dynamics in a group of expert meditators and controls. First, we characterized meditation and rest with a repertoire of brain patterns, each with its distinct probability of occurrence. Then, we generated whole-brain models of each condition, which enabled us to artificially perturb the systems to induce transitions between rest and meditation. Our results open new avenues in meditation research as a practice in health and disease.

De novo depression following temporal lobe epilepsy surgery.

Surgical removal of the mesial temporal lobe can effectively treat drug-resistant epilepsy but may lead to mood disorders. This fact is of particular interest in patients without a prior psychiatric history. The study investigates the relationship between Temporal Lobe Epilepsy (TLE), mood disorders, and the functional connectivity of the Hippocampus (Hipp) and Nucleus Accumbens (NAcc). In this case control study, twenty-seven TLE patients and 18 control subjects participated, undergoing structural and functional magnetic resonance imaging (MRI) scans before and after surgery. Post-surgery, patients were categorized into those developing de novo depression (DnD) within the first year and those without depression (nD). Functional connectivity maps between NAcc and the whole brain were generated, and connectivity strength between the to-be-resected Hipp area and NAcc was compared. Within the first year post-surgery, 7 out of 27 patients developed DnD. Most patients (88.8 %) exhibited a significant reduction in NAcc-Hipp connectivity compared to controls. The DnD group showed notably lower connectivity values than the nD group, with statistically significant disparities. Receiver Operating Characteristic (ROC) curve analysis identified a potential biomarker threshold (Crawford-T value of -2.08) with a sensitivity of 0.83 and specificity of 0.76. The results suggest that functional connectivity patterns within the reward network could serve as a potential biomarker for predicting de novo mood disorders in TLE patients undergoing surgery. This insight may assist in identifying individuals at a higher risk of developing DnD after surgery, enhancing therapeutic guidance and clinical decision-making.

Disentangling the neurobiological bases of temporal impulsivity in Huntington's disease.

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.

Delineating apathy profiles in Huntington's disease with the short-Lille Apathy Rating Scale.

INTRODUCTION: Apathy, a prevalent feature in neurological disorders including Huntington's disease (HD), is characterized by a reduction in goal-directed behavior across cognitive, auto-activation (i.e., self-activating thoughts/behavior), and emotional domains. Nonetheless, current diagnostic criteria are incapable of distinguishing multidimensional apathy profiles. Meanwhile, the short-Lille Apathy Rating Scale (LARS-s) bears potential as an operative diagnostic tool to disentangle apathy dimensions in clinical practice. The present study thereby examines the psychometric properties and factor structure of the LARS-s to tap into apathy profiles and their underlying neural correlates in HD. METHODS: Forty HD individuals were scanned and evaluated for apathy using the LARS-s, assessed for reliability and validity in HD, and the short-Problem Behavior Assessment (PBA-s). To study the dimensional structure of apathy, principal component analysis (PCA) of the LARS-s was implemented. Resulting factors were associated with gray matter volume through whole-brain voxel-based morphometry. RESULTS: The LARS-s demonstrated satisfactory psychometric properties, sharing convergent validity with PBA-s apathy and discriminant validity against depression. PCA resulted in three factors representative of apathy profiles across cognitive, auto-activation, and emotional domains. Anatomically, global apathy was significantly related with large-scale motor, cognitive, and limbic networks. Exploratory analyses of apathy profiles revealed correspondence between each factor and distinct cortical and subcortical nodes. CONCLUSION: The LARS-s is capable of capturing the multidimensional spectrum of apathy. At the same time, apathy profiles in HD are underpinned by functionally diverse neural networks. Such findings promote the continued study of apathy domains to pinpoint personalized therapeutic targets in neurologic disorders in addition to HD.

Verbal Learning and Longitudinal Hippocampal Network Connectivity in Temporal Lobe Epilepsy Surgery.

Introduction: Learning 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. Objective: Our 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. Methods: Thirty-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. Results: There 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). Conclusion: Our 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.

Unifying turbulent dynamics framework distinguishes different brain states.

Significant advances have been made by identifying the levels of synchrony of the underlying dynamics of a given brain state. This research has demonstrated that non-conscious dynamics tend to be more synchronous than in conscious states, which are more asynchronous. Here we go beyond this dichotomy to demonstrate that different brain states are underpinned by dissociable spatiotemporal dynamics. We investigated human neuroimaging data from different brain states (resting state, meditation, deep sleep and disorders of consciousness after coma). The model-free approach was based on Kuramoto's turbulence framework using coupled oscillators. This was extended by a measure of the information cascade across spatial scales. Complementarily, the model-based approach used exhaustive in silico perturbations of whole-brain models fitted to these measures. This allowed studying of the information encoding capabilities in given brain states. Overall, this framework demonstrates that elements from turbulence theory provide excellent tools for describing and differentiating between brain states.

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.

Mapping connectivity fingerprints for presurgical evaluation of temporal lobe epilepsy.

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 < 0.05) as well as in the mesial regions (p < 0.002) of the to-be-resected temporal lobe. When comparing between SF and non-SF patients, a step-wise binary logistic regression model including all the ROIs, showed that having an increased wDC of the temporal pole (p < 0.05) and the mesial area (p < 0.05) of the to-be-resected temporal lobe was associated with seizure freedom long-term after surgery. CONCLUSIONS: This study provides a network-based presurgical biomarker that could pave the way towards personalized prediction. In patients with TLE undergoing anterior temporal resections, having an increased wDC at rest could be a signature of the epileptogenic area, and could help identifying those patients who would benefit most from surgery.

Gray Matter Vulnerabilities Predict Longitudinal Development of Apathy in Huntington's Disease.

BACKGROUND: Apathy, a common neuropsychiatric disturbance in Huntington's disease (HD), is subserved by a complex neurobiological network. However, no study has yet employed a whole-brain approach to examine underlying regional vulnerabilities that may precipitate apathy changes over time. OBJECTIVES: To identify whole-brain gray matter volume (GMV) vulnerabilities that may predict longitudinal apathy development in HD. METHODS: Forty-five HD individuals (31 female) were scanned and evaluated for apathy and other neuropsychiatric features using the short-Problem Behavior Assessment for a maximum total of six longitudinal visits (including baseline). In order to identify regions where changes in GMV may describe changes in apathy, we performed longitudinal voxel-based morphometry (VBM) on those 33 participants with a magnetic resonance imaging (MRI) scan on their second visit at 18 ± 6 months follow-up (78 MRI datasets). We next employed a generalized linear mixed-effects model (N = 45) to elucidate whether initial and specific GMV may predict apathy development over time. RESULTS: Utilizing longitudinal VBM, we revealed a relationship between increases in apathy and specific GMV atrophy in the right middle cingulate cortex (MCC). Furthermore, vulnerability in the right MCC volume at baseline successfully predicted the severity and progression of apathy over time. CONCLUSIONS: This study highlights that individual differences in apathy in HD may be explained by variability in atrophy and initial vulnerabilities in the right MCC, a region implicated in action-initiation. These findings thus serve to facilitate the prediction of an apathetic profile, permitting targeted, time-sensitive interventions in neurodegenerative disease with potential implications in otherwise healthy populations. © 2021 International Parkinson and Movement Disorder Society.

SeSBAT: Single Subject Brain Analysis Toolbox. Application to Huntington's Disease as a Preliminary Study.

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.

Neurocognitive signatures of phonemic sequencing in expert backward speakers.

Despite its prolific growth, neurolinguistic research on phonemic sequencing has largely neglected the study of individuals with highly developed skills in this domain. To bridge this gap, we report multidimensional signatures of two experts in backward speech, that is, the capacity to produce utterances by reversing the order of phonemes while retaining their identity. Our approach included behavioral assessments of backward and forward speech alongside neuroimaging measures of voxel-based morphometry, diffusion tensor imaging, and resting-state functional connectivity. Relative to controls, both backward speakers exhibited behavioral advantages for reversing words and sentences of varying complexity, irrespective of working memory skills. These patterns were accompanied by increased grey matter volume, higher mean diffusivity, and enhanced functional connectivity along dorsal and ventral stream regions mediating phonological and other linguistic operations, with complementary support of areas subserving associative-visual and domain-general processes. Still, the specific loci of these neural patterns differed between both subjects, suggesting individual variability in the correlates of expert backward speech. Taken together, our results offer new vistas on the domain of phonemic sequencing, while illuminating neuroplastic patterns underlying extraordinary language abilities.

Hippocampal microstructural architecture and surgical outcome: Hippocampal diffusivity could predict seizure relapse.

PURPOSE: Our aim was to study the microstructural architecture of the contralateral hippocampus to the affected side in patients with temporal lobe epilepsy with hippocampal sclerosis (TLE-HS) and its relation with surgical outcome. METHOD: We included 33 consecutive patients evaluated in our epilepsy surgery program during a five-year period. They underwent a presurgical MRI with volumetric T1 and diffusion weighted sequences. 22 patients with TLE-HS (13 women, 12 right TLE-HS) were finally selected. Median follow-up after surgery was 6.25 years (4.5-8.83 years). We segmented the hippocampal subfields of the contralateral hippocampus using FreeSurfer and calculated the fractional anisotropy (FA) and the mean diffusivity (MD) of each subfield. We also scanned 18 healthy age-matched controls. RESULTS: After surgery, 50 % of the patients (n = 11) remained seizure-free (SF) following surgery. Comparing non-SF to SF patients, the MD showed increased values of the CA1 (p = 0.035), the molecular layer (p = 0.010) and the dentate gyrus (p = 0.041) in the healthy hippocampus. Using a cut-off point for a survival analysis, we found that patients with lower values of MD of the molecular layer and the CA1 remained SF during long-term post-operative follow-up (p < 0.0001). CONCLUSIONS: The contralateral hippocampal internal microstructure may have be implicated in post-surgery seizure freedom in patients with TLE-HS.

Cognitive and brain structural changes in long-term oligodendroglial tumor survivors.

BACKGROUND: We identify cognitive impairment and MRI structural brain changes in long-term oligodendroglial tumor survivors treated with radiation therapy (RT) alone (21%) or with chemotherapy (CT) (79%). METHODS: Oligodendroglial tumor patients (based on the World Health Organization [WHO] 2007 classification) who completed RT ± CT at least 2 years before the study initiation, were classified into 3 groups according to the time treatment was completed: Group 1 = 2-5 years (n = 22), Group 2 = 6-10 years (n = 13), and Group 3 >10 years (n = 13). All patients had a cross-sectional neuropsychological evaluation (n = 48) and a longitudinal volumetric analysis (gray matter [GM; n = 34]) between postsurgical and last follow-up MRI. White matter (WM) changes on MRI were assessed using a qualitative scale. RESULTS: There were no differences regarding tumor or treatment-related characteristics between groups. Six of 22 patients (27.3%) in Group 1; 5/13 (38.5%) in Group 2; and 9/13 (69.2%) in Group 3 had cognitive impairment that was considered severe in 3/22 patients (13.6%) in Group 1; 4/13 (30.8%) in Group 2; and 6/13 (46.2%) in Group 3. Patients in Groups 2 and 3 showed significant GM atrophy and more leukoencephalopathy than Group 1. Cognitive deficits were associated with brain atrophy and WM changes. CONCLUSIONS: Long-term oligodendroglial tumor survivors who underwent standard RT ± CT treatment, mainly >5 years of its completion, present cognitive impairment, especially on memory and executive functions, associated with late GM and WM damage, thus highlighting the need of developing future strategies in patients with oligodendroglial tumor and long expected survival.

White matter cortico-striatal tracts predict apathy subtypes in Huntington's disease.

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.

Characterizing the Dynamical Complexity Underlying Meditation.

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.

Specific patterns of brain alterations underlie distinct clinical profiles in Huntington's disease.

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.

Tau Protein is Associated with Longitudinal Memory Decline in Cognitively Healthy Subjects with Normal Alzheimer's Disease Cerebrospinal Fluid Biomarker Levels.

BACKGROUND: We investigated a sample of cognitively healthy subjects with normal Alzheimer's disease (AD) cerebrospinal fluid (CSF) biomarker levels to identify the earliest variables related to longitudinal memory changes. OBJECTIVE: Employing a new highly demanding learning and memory test (the Ancient Farming Equipment Test; AFE-T), we aimed to investigate whether a biomarker related to neurodegeneration (i.e., CSF tau) was associated with longitudinal memory decline. METHODS: Thirty-two cognitively and biologically normal (CBN) subjects underwent MRI, neuropsychological assessment, and the AFE-T at baseline and 18 months later. To explore the relationship between cognitive performance and relevant factors, a linear model was set up. For a secondary analysis that further explore the effect of tau, the subjects were divided into CBN-Tau↓ (tau < 228.64 pg/ml; n = 16) and CBN-Tau↑ (tau > 228.64 pg/ml; n = 16). We also performed voxel-based morphometry (VBM) to identify regions of grey matter volume that would predict both baseline and longitudinal cognitive performance. RESULTS: Our main finding was an association between CSF tau and longitudinal memory decline measured with AFE-T (B = -0.17, p < 0.05; r = -0.414; p < 0.01), and further analyses showed different evolvement between subgroups, with an accelerated decline in individuals with higher tau (F(1,31) = 8.37; p < 0.01). VBM results suggested that AFE-T performance is related to grey matter volume in a medial temporal, middle frontal, and posterior cerebellar network at baseline, and that there are strategic brain areas driving the longitudinal cognitive changes. CONCLUSIONS: The present findings provide evidence for structural and biological markers linked to cognitive aging by highlighting the role of tau, a marker of neurodegeneration, which can be related with the earliest memory changes in healthy subjects.

Implicit but not explicit extinction to threat-conditioned stimulus prevents spontaneous recovery of threat-potentiated startle responses in humans.

INTRODUCTION: It has long been posited that threat learning operates and forms under an affective and a cognitive learning system that is supported by different brain circuits. A primary drawback in exposure-based therapies is the high rate of relapse that occurs when higher order areas fail to inhibit responses driven by the defensive circuit. It has been shown that implicit exposure of fearful stimuli leads to a long-lasting reduction in avoidance behavior in patients with phobia. Despite the potential benefits of this approach in the treatment of phobias and posttraumatic stress disorder, implicit extinction is still underinvestigated. METHODS: Two groups of healthy participants were threat conditioned. The following day, extinction training was conducted using a stereoscope. One group of participants was explicitly exposed with the threat-conditioned image, while the other group was implicitly exposed using a continuous flash suppression (CFS) technique. On the third day, we tested the spontaneous recovery of defensive responses using explicit presentations of the images. RESULTS: On the third day, we found that only the implicit extinction group showed reduced spontaneous recovery of defensive responses to the threat-conditioned stimulus, measured by threat-potentiated startle responses but not by the electrodermal activity. CONCLUSION: Our results suggest that implicit extinction using CFS might facilitate the modulation of the affective component of fearful memories, attenuating its expression after 24 hr. The limitations of the CFS technique using threatful stimuli urge the development of new strategies to improve implicit presentations and circumvent such limitations. Our study encourages further investigations of implicit extinction as a potential therapeutic target to further advance exposure-based psychotherapies.

An active cognitive lifestyle as a potential neuroprotective factor in Huntington's disease.

A cognitive stimulating lifestyle has been observed to confer cognitive benefits in multiple neurodegenerative diseases. However, the underlying neurobiological basis of this phenomenon remains unclear. Huntington's disease can provide a suitable model to study the effects and neural mechanisms of cognitive engagement in neurodegeneration. In this study, we investigate the effect of lifestyle factors such as education, occupation and engagement in cognitive activities in Huntington's disease gene carriers on cognitive performance and age of onset as well as the underlying neural changes sustaining these effects, measured by magnetic resonance imaging. Specifically, we analyzed both gray matter volume and the strength of connectivity of the executive control resting-state network. High levels of cognitive engagement were significantly associated with more preserved executive functions, a delay in the appearance of symptoms, reduced volume loss of the left precuneus and the bilateral caudate and a modulation of connectivity strength of anterior cingulate cortex and left angular gyrus with the executive control network. These findings suggest that a cognitively stimulating lifestyle may promote brain maintenance by modulating the executive control resting-state network and conferring protection against neurodegeneration, which results in a delayed onset of symptoms and improved performance in executive functions.