Resting-state networks and anosognosia in Alzheimer's disease.

Background: Recent evidence suggests that anosognosia or unawareness of cognitive impairment in Alzheimer's Disease (AD) may be explained by a disconnection between brain regions involved in accessing and monitoring information regarding self and others. It has been demonstrated that AD patients with anosognosia have reduced connectivity within the default mode network (DMN) and that anosognosia in people with prodromal AD is positively associated with bilateral anterior cingulate cortex (ACC), suggesting a possible role of this region in mechanisms of awareness in the early phase of disease. We hypothesized that anosognosia in AD is associated with an imbalance between the activity of large-scale resting-state functional magnetic resonance imaging (fMRI) networks, in particular the DMN, the salience network (SN), and the frontoparietal network (FPN). Methods: Sixty patients with MCI and AD dementia underwent fMRI and neuropsychological assessment including the Anosognosia Questionnaire Dementia (AQ-D), a measure of anosognosia based on a discrepancy score between patient's and carer's judgments. After having applied Independent Component Analysis (ICA) to resting fMRI data we performed: (i) correlations between the AQ-D score and functional connectivity in the DMN, SN, and FPN, and (ii) comparisons between aware and unaware patients of the DMN, SN, and FPN functional connectivity. Results: We found that anosognosia was associated with (i) weak functional connectivity within the DMN, in posterior and middle cingulate cortex particularly, (ii) strong functional connectivity within the SN in ACC, and between the SN and basal ganglia, and (iii) a heterogenous effect concerning the functional connectivity of the FPN, with a weak connectivity between the FPN and PCC, and a strong connectivity between the FPN and ACC. The observed effects were controlled for differences in severity of cognitive impairment and age. Conclusion: Anosognosia in the AD continuum is associated with a dysregulation of the functional connectivity of three large-scale networks, namely the DMN, SN, and FPN.

Age-specific prevalence of the different clinical presentations of AD and FTD in young-onset dementia.

BACKGROUND: Studies have shown that the prevalence of all-variants Alzheimer's disease (AD) and frontotemporal dementia (FTD) both increase with age, even before the age of 65. However, it is not known whether their different clinical presentations all increase in prevalence with age in the same way. METHODS: We studied the prevalence of the different clinical presentations of young-onset AD and FTD by 5-year age groups in a population-based study identifying all dementia patients with a diagnosis of AD and FTD and symptoms onset before age 65 in the Modena province, Italy. By using regression models of cumulative occurrences, we also estimated age-specific prevalence and compared the growth curves of the clinical presentations. RESULTS: The prevalence of all-variants AD increased with age, from 18/1,000,000 in the 40-44 age group to 1411/1,000,000 in the 60-64 age group. The prevalence of all-variants FTD also increased with age, from 18/1,000,000 to 866/1,000,000. An estimation of age-specific prevalence functions of each clinical presentation showed that atypical non-amnestic AD and aphasic FTD grew the most in early ages, followed by the behavioural variant of FTD (bvFTD). Then, around the age of 60, amnestic AD took over and its age-specific prevalence continued to increase disproportionally compared to all the other clinical variants of AD and FTD, which, instead, started to decrease in prevalence. CONCLUSIONS: Amnestic AD is the clinical presentation that increases the most with advancing age, followed by bvFTD, suggesting that there is a differential vulnerability to the effect of ageing within the same neurodegenerative disease.

Sparse Bayesian modeling of hierarchical independent component analysis: Reliable estimation of individual differences in brain networks.

Independent component analysis (ICA) is one of the leading approaches for studying brain functional networks. There is increasing interest in neuroscience studies to investigate individual differences in brain networks and their association with demographic characteristics and clinical outcomes. In this work, we develop a sparse Bayesian group hierarchical ICA model that offers significant improvements over existing ICA techniques for identifying covariate effects on the brain network. Specifically, we model the population-level ICA source signals for brain networks using a Dirichlet process mixture. To reliably capture individual differences on brain networks, we propose sparse estimation of the covariate effects in the hierarchical ICA model via a horseshoe prior. Through extensive simulation studies, we show that our approach performs considerably better in detecting covariate effects in comparison with the leading group ICA methods. We then perform an ICA decomposition of a between-subject meditation study. Our method is able to identify significant effects related to meditative practice in brain regions that are consistent with previous research into the default mode network, whereas other group ICA approaches find few to no effects.

Neural correlates of emotional valence for faces and words.

Stimuli with negative emotional valence are especially apt to influence perception and action because of their crucial role in survival, a property that may not be precisely mirrored by positive emotional stimuli of equal intensity. The aim of this study was to identify the neural circuits differentially coding for positive and negative valence in the implicit processing of facial expressions and words, which are among the main ways human beings use to express emotions. Thirty-six healthy subjects took part in an event-related fMRI experiment. We used an implicit emotional processing task with the visual presentation of negative, positive, and neutral faces and words, as primary stimuli. Dynamic Causal Modeling (DCM) of the fMRI data was used to test effective brain connectivity within two different anatomo-functional models, for the processing of words and faces, respectively. In our models, the only areas showing a significant differential response to negative and positive valence across both face and word stimuli were early visual cortices, with faces eliciting stronger activations. For faces, DCM revealed that this effect was mediated by a facilitation of activity in the amygdala by positive faces and in the fusiform face area by negative faces; for words, the effect was mainly imputable to a facilitation of activity in the primary visual cortex by positive words. These findings support a role of early sensory cortices in discriminating the emotional valence of both faces and words, where the effect may be mediated chiefly by the subcortical/limbic visual route for faces, and rely more on the direct thalamic pathway to primary visual cortex for words.

Emergence of associative learning in a neuromorphic inference network.

Objective. In the theoretical framework of predictive coding and active inference, the brain can be viewed as instantiating a rich generative model of the world that predicts incoming sensory data while continuously updating its parameters via minimization of prediction errors. While this theory has been successfully applied to cognitive processes-by modelling the activity of functional neural networks at a mesoscopic scale-the validity of the approach when modelling neurons as an ensemble of inferring agents, in a biologically plausible architecture, remained to be explored.Approach.We modelled a simplified cerebellar circuit with individual neurons acting as Bayesian agents to simulate the classical delayed eyeblink conditioning protocol. Neurons and synapses adjusted their activity to minimize their prediction error, which was used as the network cost function. This cerebellar network was then implemented in hardware by replicating digital neuronal elements via a low-power microcontroller.Main results. Persistent changes of synaptic strength-that mirrored neurophysiological observations-emerged via local (neurocentric) prediction error minimization, leading to the expression of associative learning. The same paradigm was effectively emulated in low-power hardware showing remarkably efficient performance compared to conventional neuromorphic architectures.Significance. These findings show that: (a) an ensemble of free energy minimizing neurons-organized in a biological plausible architecture-can recapitulate functional self-organization observed in nature, such as associative plasticity, and (b) a neuromorphic network of inference units can learn unsupervised tasks without embedding predefined learning rules in the circuit, thus providing a potential avenue to a novel form of brain-inspired artificial intelligence.

From Generative Models to Generative Passages: A Computational Approach to (Neuro) Phenomenology.

This paper presents a version of neurophenomenology based on generative modelling techniques developed in computational neuroscience and biology. Our approach can be described as computational phenomenology because it applies methods originally developed in computational modelling to provide a formal model of the descriptions of lived experience in the phenomenological tradition of philosophy (e.g., the work of Edmund Husserl, Maurice Merleau-Ponty, etc.). The first section presents a brief review of the overall project to naturalize phenomenology. The second section presents and evaluates philosophical objections to that project and situates our version of computational phenomenology with respect to these projects. The third section reviews the generative modelling framework. The final section presents our approach in detail. We conclude by discussing how our approach differs from previous attempts to use generative modelling to help understand consciousness. In summary, we describe a version of computational phenomenology which uses generative modelling to construct a computational model of the inferential or interpretive processes that best explain this or that kind of lived experience.

The Impact of Mindfulness Meditation on the Wandering Mind: a Systematic Review.

Through the practice of Mindfulness Meditation (MM), meditators become familiar with the observation of ongoing spontaneous thoughts, while maintaining an attitude of openness and equanimity. The aim of this systematic review is to present a synthesis of available findings of the short and long-term effects of MM on mind wandering (MW). We included studies that considered both first-person and behavioral/physiological measures of MW. The search resulted in 2035 papers, 24 of which were eligible. Reviewed studies revealed a high heterogeneity in designs, outcome measures and interventions. Most of the pre-post intervention studies showed that a protracted practice of MM (at least 2 weeks) reduced MW, limiting its negative effects on different cognitive tasks. Cross-sectional studies highlighted differences between expert meditators and naïve individuals: meditators self-reported less MW and showed decreased Default Mode Network activity, during meditation and resting-state. Further studies are needed to replicate available findings and to more deeply explore how MW is influenced by meditation, also considering its qualitative characteristics that remain largely unexplored.

Bayesian Joint Modeling of Multiple Brain Functional Networks.

Investigating the similarity and changes in brain networks under different mental conditions has become increasingly important in neuroscience research. A standard separate estimation strategy fails to pool information across networks and hence has reduced estimation accuracy and power to detect between-network differences. Motivated by a fMRI Stroop task experiment that involves multiple related tasks, we develop an integrative Bayesian approach for jointly modeling multiple brain networks that provides a systematic inferential framework for network comparisons. The proposed approach explicitly models shared and differential patterns via flexible Dirichlet process-based priors on edge probabilities. Conditional on edges, the connection strengths are modeled via Bayesian spike and slab prior on the precision matrix off-diagonals. Numerical simulations illustrate that the proposed approach has increased power to detect true differential edges while providing adequate control on false positives and achieves greater network estimation accuracy compared to existing methods. The Stroop task data analysis reveals greater connectivity differences between task and fixation that are concentrated in brain regions previously identified as differentially activated in Stroop task, and more nuanced connectivity differences between exertion and relaxed task. In contrast, penalized modeling approaches involving computationally burdensome permutation tests reveal negligible network differences between conditions that seem biologically implausible.

Modeling Early Phases of COVID-19 Pandemic in Northern Italy and Its Implication for Outbreak Diffusion.

The COVID-19 pandemic has sparked an intense debate about the hidden factors underlying the dynamics of the outbreak. Several computational models have been proposed to inform effective social and healthcare strategies. Crucially, the predictive validity of these models often depends upon incorporating behavioral and social responses to infection. Among these tools, the analytic framework known as "dynamic causal modeling" (DCM) has been applied to the COVID-19 pandemic, shedding new light on the factors underlying the dynamics of the outbreak. We have applied DCM to data from northern Italian regions, the first areas in Europe to contend with the outbreak, and analyzed the predictive validity of the model and also its suitability in highlighting the hidden factors governing the pandemic diffusion. By taking into account data from the beginning of the pandemic, the model could faithfully predict the dynamics of outbreak diffusion varying from region to region. The DCM appears to be a reliable tool to investigate the mechanisms governing the spread of the SARS-CoV-2 to identify the containment and control strategies that could efficiently be used to counteract further waves of infection.

Reward-related brain activity and behavior are associated with peripheral ghrelin levels in obesity.

BACKGROUND/OBJECTIVES: While excessive food consumption represents a key factor in the development of obesity, the underlying mechanisms are still unclear. Ghrelin, a gut-brain hormone involved in the regulation of appetite, is impaired in obesity. In addition to its role in eating behavior, this hormone was shown to affect brain regions controlling reward, including the striatum and prefrontal cortex, and there is strong evidence of impaired reward processing in obesity. The present study investigated the possibility that disrupted reward-related brain activity in obesity relates to ghrelin deficiency. SUBJECTS/METHODS: Fifteen severely obese subjects (BMI > 35 kg/m2) and fifteen healthy non-obese control subjects (BMI < 30 kg/m2) were recruited. A guessing-task paradigm, previously shown to activate the ventral striatum, was used to assess reward-related brain neural activity by functional magnetic resonance imaging (fMRI). Fasting blood samples were collected for the measurement of circulating ghrelin. RESULTS: Significant activations in the ventral striatum, ventromedial prefrontal cortex and extrastriate visual cortex were elicited by the fMRI task in both obese and control subjects. In addition, greater reward-related activations were present in the dorsolateral prefrontal cortex, and precuneus/posterior cingulate of obese subjects compared to controls. Obese subjects exhibited longer choice times after repeated reward and lower circulating ghrelin levels than lean controls. Reduced ghrelin levels significantly predicted slower post-reward choices and reward-related hyperactivity in dorsolateral prefrontal cortices in obese subjects. CONCLUSION: This study provides evidence of association between circulating ghrelin and reward-related brain activity in obesity and encourages further exploration of the role of ghrelin system in altered eating behavior in obesity.

Short-term mindfulness practice attenuates reward prediction errors signals in the brain.

Activity changes in dopaminergic neurons encode the ongoing discrepancy between expected and actual value of a stimulus, providing a teaching signal for a reward prediction process. Previous work comparing a cohort of long-term Zen meditators to controls demonstrated an attenuation of reward prediction signals to appetitive reward in the striatum. Using a cross-commodity design encompassing primary- and secondary-reward conditioning experiments, the present study asks the question of whether reward prediction signals are causally altered by mindfulness training in naïve subjects. Volunteers were randomly assigned to 8 weeks of mindfulness training (MT), active control training (CT), or a one-time mindfulness induction group (MI). We observed a decreased response to positive prediction errors in the putamen in the MT group compared to CT using both a primary and a secondary-reward experiment. Furthermore, the posterior insula showed greater activation to primary rewards, independently of their predictability, in the MT group, relative to CT and MI group. These results support the notion that increased attention to the present moment and its interoceptive features - a core component of mindfulness practice - may reduce predictability effects in reward processing, without dampening (in fact, enhancing) the response to the actual delivery of the stimulus.

The contemplative exercise through the lenses of predictive processing: A promising approach.

The theory of predictive processing in the comprehensive articulation proposed by Karl Friston is a framework that boasts an impressively wide explanatory power in neurobiology, where processes apparently as diverse as perception, action, attention, and learning unfold, and are coherently orchestrated, according to the single general mandate of free-energy minimization. In the present opinion piece, I argue that the adoption of this theoretical perspective can provide a much needed unitary framework for contemplative research as well, whose explosive growth in terms of the number of published studies and amount of collected data has not been matched yet by a similarly extensive effort to theoretically organize the findings, so that a deeper understanding of meditation-related processes can be attained. After an introduction to the basic notions of predictive processing, a tentative application of the latter to the meditative exercise is discussed, taking as a paradigmatic example the Japanese Zen meditation practice of shikantaza. Finally, I provide a short list of experimental paradigms that seem particularly useful to test the hypotheses born out of the predictive processing approach to contemplative research.

The epistemic and pragmatic value of non-action: a predictive coding perspective on meditation.

The surge of interest about mindfulness meditation is associated with a growing empirical evidence about its impact on the mind and body. Yet, despite promising phenomenological or psychological models of mindfulness, a general mechanistic understanding of meditation steeped in neuroscience is still lacking. In parallel, predictive processing approaches to the mind are rapidly developing in the cognitive sciences with an impressive explanatory power: processes apparently as diverse as perception, action, attention, and learning, can be seen as unfolding and being coherently orchestrated according to the single general mandate of free-energy minimization. Here, we briefly explore the possibility to supplement previous phenomenological models of focused attention meditation by formulating them in terms of active inference. We first argue that this perspective can account for how paying voluntary attention to the body in meditation helps settling the mind by downweighting habitual and automatic trajectories of (pre)motor and autonomic reactions, as well as the pull of distracting spontaneous thought at the same time. Secondly, we discuss a possible relationship between phenomenological notions such as opacity and de-reification, and the deployment of precision-weighting via the voluntary allocation of attention. We propose the adoption of this theoretical framework as a promising strategy for contemplative research. Explicit computational simulations and comparisons with experimental and phenomenological data will be critical to fully develop this approach.

Pain Mirrors: Neural Correlates of Observing Self or Others' Facial Expressions of Pain.

Facial expressions of pain are able to elicit empathy and adaptive behavioral responses in the observer. An influential theory posits that empathy relies on an affective mirror mechanism, according to which emotion recognition relies upon the internal simulation of motor and interoceptive states triggered by emotional stimuli. We tested this hypothesis comparing representations of self or others' expressions of pain in nineteen young healthy female volunteers by means of functional magnetic resonance imaging (fMRI). We hypothesized that one's own facial expressions are more likely to elicit the internal simulation of emotions, being more strictly related to self. Video-clips of the facial expressions of each volunteer receiving either painful or non-painful mechanical stimulations to their right hand dorsum were recorded and used as stimuli in a 2 × 2 (Self/Other; Pain/No-Pain) within-subject design. During each trial, a 2 s video clip was presented, displaying either the subject's own neutral or painful facial expressions (Self No-Pain, SNP; Self Pain, SP), or the expressions of other unfamiliar volunteers (Others' No-Pain, ONP; Others' Pain, OP), displaying a comparable emotional intensity. Participants were asked to indicate whether each video displayed a pain expression. fMRI signals were higher while viewing Pain than No-Pain stimuli in a large bilateral array of cortical areas including middle and superior temporal, supramarginal, superior mesial and inferior frontal (IFG) gyri, anterior insula (AI), anterior cingulate (ACC), and anterior mid-cingulate (aMCC) cortex, as well as right fusiform gyrus. Bilateral activations were also detected in thalamus and basal ganglia. The Self vs. Other contrast showed signal changes in ACC and aMCC, IFG, AI, and parietal cortex. A significant interaction between Self and Pain [(SP vs. SNP) >(OP vs. ONP)] was found in a pre-defined region of aMCC known to be also active during noxious stimulation. These findings demonstrate that the observation of one's own and others' facial expressions share a largely common neural network, but self-related stimuli induce generally higher activations. In line with our hypothesis, selectively greater activity for self pain-related stimuli was found in aMCC, a medial-wall region critical for pain perception and recognition.

Voluntary modulation of mental effort investment: an fMRI study.

Mental effort is a common phenomenological construct deeply linked to volition and self-control. While it is often assumed that the amount of exertion invested in a task can be voluntarily regulated, the neural bases of such faculty and its behavioural effects are yet insufficiently understood. In this study, we investigated how the instructions to execute a demanding cognitive task either "with maximum exertion" or "as relaxed as possible" affected performance and brain activity. The maximum exertion condition, compared to relaxed execution, was associated with speeded motor responses without an accuracy trade-off, and an amplification of both task-related activations in dorsal frontoparietal and cerebellar regions, and task-related deactivations in default mode network (DMN) areas. Furthermore, the visual cue to engage maximum effort triggered an anticipatory widespread increase of activity in attentional, sensory and executive regions, with its peak in the brain stem reticular activating system. Across individuals, this surge of activity in the brain stem, but also in medial wall cortical regions projecting to the adrenal medulla, positively correlated with increases in heart rate, suggesting that the intention to willfully modulate invested effort involves mechanisms related to catecholaminergic transmission and a suppression of DMN activity in favor of externally-directed attentional processes.

Effects of oxytocin and vasopressin on the neural response to unreciprocated cooperation within brain regions involved in stress and anxiety in men and women.

Anxiety disorders are characterized by hyperactivity in both the amygdala and the anterior insula. Interventions that normalize activity in these areas may therefore be effective in treating anxiety disorders. Recently, there has been significant interest in the potential use of oxytocin (OT), as well as vasopressin (AVP) antagonists, as treatments for anxiety disorders. In this double-blind, placebo-controlled, pharmaco- fMRI study, 153 men and 151 women were randomized to treatment with either 24 IU intranasal OT, 20 IU intranasal AVP, or placebo and imaged with fMRI as they played the iterated Prisoner's Dilemma game with same-sex human and computer partners. In men, OT attenuated the fMRI response to unreciprocated cooperation (CD), a negative social interaction, within the amygdala and anterior insula. This effect was specific to interactions with human partners. In contrast, among women, OT unexpectedly attenuated the amygdala and anterior insula response to unreciprocated cooperation from computer but not human partners. Among women, AVP did not significantly modulate the response to unreciprocated cooperation in either the amygdala or the anterior insula. However, among men, AVP attenuated the BOLD response to CD outcomes with human partners across a relatively large cluster including the amygdala and the anterior insula, which was contrary to expectations. Our results suggest that OT may decrease the stress of negative social interactions among men, whereas these effects were not found in women interacting with human partners. These findings support continued investigation into the possible efficacy of OT as a treatment for anxiety disorders.

Network-based characterization of brain functional connectivity in Zen practitioners.

In the last decade, a number of neuroimaging studies have investigated the neurophysiological effects associated with contemplative practices. Meditation-related changes in resting state functional connectivity (rsFC) have been previously reported, particularly in the default mode network, frontoparietal attentional circuits, saliency-related regions, and primary sensory cortices. We collected functional magnetic resonance imaging data from a sample of 12 experienced Zen meditators and 12 meditation-naïve matched controls during a basic attention-to-breathing protocol, together with behavioral performance outside the scanner on a set of computerized neuropsychological tests. We adopted a network system of 209 nodes, classified into nine functional modules, and a multi-stage approach to identify rsFC differences in meditators and controls. Between-group comparisons of modulewise FC, summarized by the first principal component of the relevant set of edges, revealed important connections of frontoparietal circuits with early visual and executive control areas. We also identified several group differences in positive and negative edgewise FC, often involving the visual, or frontoparietal regions. Multivariate pattern analysis of modulewise FC, using support vector machine (SVM), classified meditators, and controls with 79% accuracy and selected 10 modulewise connections that were jointly prominent in distinguishing meditators and controls; a similar SVM procedure based on the subjects' scores on the neuropsychological battery yielded a slightly weaker accuracy (75%). Finally, we observed a good correlation between the across-subject variation in strength of modulewise connections among frontoparietal, executive, and visual circuits, on the one hand, and in the performance on a rapid visual information processing test of sustained attention, on the other. Taken together, these findings highlight the usefulness of employing network analysis techniques in investigating the neural correlates of contemplative practices.

Oxytocin and vasopressin effects on the neural response to social cooperation are modulated by sex in humans.

Recent research has examined the effects of oxytocin (OT) and vasopressin (AVP) on human social behavior and brain function. However, most participants have been male, while previous research in our lab demonstrated sexually differentiated effects of OT and AVP on the neural response to reciprocated cooperation. Here we extend our previous work by significantly increasing the number of participants to enable the use of more stringent statistical thresholds that permit more precise localization of OT and AVP effects in the brain. In a double-blind, placebo-controlled study, 153 men and 151 women were randomized to receive 24 IU intranasal OT, 20 IU intranasal AVP or placebo. Afterwards, they were imaged with fMRI while playing an iterated Prisoner's Dilemma Game with same-sex partners. Sex differences were observed for effects of OT on the neural response to reciprocated cooperation, such that OT increased the caduate/putamen response among males, whereas it decreased this response among females. Thus, 24 IU OT may increase the reward or salience of positive social interactions among men, while decreasing their reward or salience among women. Similar sex differences were also observed for AVP effects within bilateral insula and right supramarginal gyrus when a more liberal statistical threshold was employed. While our findings support previous suggestions that exogenous nonapeptides may be effective treatments for disorders such as depression and autism spectrum disorder, they caution against uniformly extending such treatments to men and women alike.