Imaging evolution of the primate brain: the next frontier?

Evolution, as we currently understand it, strikes a delicate balance between animals' ancestral history and adaptations to their current niche. Similarities between species are generally considered inherited from a common ancestor whereas observed differences are considered as more recent evolution. Hence comparing species can provide insights into the evolutionary history. Comparative neuroimaging has recently emerged as a novel subdiscipline, which uses magnetic resonance imaging (MRI) to identify similarities and differences in brain structure and function across species. Whereas invasive histological and molecular techniques are superior in spatial resolution, they are laborious, post-mortem, and oftentimes limited to specific species. Neuroimaging, by comparison, has the advantages of being applicable across species and allows for fast, whole-brain, repeatable, and multi-modal measurements of the structure and function in living brains and post-mortem tissue. In this review, we summarise the current state of the art in comparative anatomy and function of the brain and gather together the main scientific questions to be explored in the future of the fascinating new field of brain evolution derived from comparative neuroimaging.

Are adolescents more optimal decision-makers in novel environments? Examining the benefits of heightened exploration in a patch foraging paradigm.

Adolescence is a period of heightened exploration relative to adulthood and childhood. This predisposition has been linked with negative behaviours related to risk-taking, including dangerous driving, substance misuse and risky sexual practices. However, recent models have argued that adolescents' heightened exploration serves a functional purpose within the lifespan, allowing adolescents to develop experiential knowledge of their surroundings. Yet, there is limited evidence that heightened exploration in adolescence is associated with positive outcomes. To address this, the present pre-registered study utilised a foraging paradigm with a sample of adolescents aged 16-17 (N = 68) and of adults aged 21 and above (N = 69). Participants completed a patch foraging task, which required them to choose between exploiting a known resource which gradually yields fewer rewards, and exploring a novel, unknown resource with a fresh distribution of rewards. Findings demonstrated that adolescents explored more than adults, which - in the context of the current task-represented more optimal patch foraging behaviour. These findings indicate that adolescents' heightened exploration can be beneficial, as they were able to effectively navigate unknown environments and accrue rewards more successfully than adults. This provides evidence that heightened exploration in adolescence, relative to adulthood, can lead to positive outcomes and contributes to our understanding of the role increased novelty-seeking plays at this point in the lifespan.

Connectivity-based parcellation reveals distinct cortico-striatal connectivity fingerprints in Autism Spectrum Disorder.

Autism Spectrum Disorder (ASD) has been associated with abnormal synaptic development causing a breakdown in functional connectivity. However, when measured at the macro scale using resting state fMRI, these alterations are subtle and often difficult to detect due to the large heterogeneity of the pathology. Recently, we outlined a novel approach for generating robust biomarkers of resting state functional magnetic resonance imaging (RS-fMRI) using connectivity based parcellation of gross morphological structures to improve single-subject reproducibility and generate more robust connectivity fingerprints. Here we apply this novel approach to investigating the organization and connectivity strength of the cortico-striatal system in a large sample of ASD individuals and typically developed (TD) controls (N=130 per group). Our results showed differences in the parcellation of the striatum in ASD. Specifically, the putamen was found to be one single structure in ASD, whereas this was split into anterior and posterior segments in an age, IQ, and head movement matched TD group. An analysis of the connectivity fingerprints revealed that the group differences in clustering were driven by differential connectivity between striatum and the supplementary motor area, posterior cingulate cortex, and posterior insula. Our approach for analysing RS-fMRI in clinical populations has provided clear evidence that cortico-striatal circuits are organized differently in ASD. Based on previous task-based segmentations of the striatum, we believe that the anterior putamen cluster present in TD, but not in ASD, likely contributes to social and language processes.

Disrupted prediction errors index social deficits in autism spectrum disorder.

Social deficits are a core symptom of autism spectrum disorder; however, the perturbed neural mechanisms underpinning these deficits remain unclear. It has been suggested that social prediction errors-coding discrepancies between the predicted and actual outcome of another's decisions-might play a crucial role in processing social information. While the gyral surface of the anterior cingulate cortex signalled social prediction errors in typically developing individuals, this crucial social signal was altered in individuals with autism spectrum disorder. Importantly, the degree to which social prediction error signalling was aberrant correlated with diagnostic measures of social deficits. Effective connectivity analyses further revealed that, in typically developing individuals but not in autism spectrum disorder, the magnitude of social prediction errors was driven by input from the ventromedial prefrontal cortex. These data provide a novel insight into the neural substrates underlying autism spectrum disorder social symptom severity, and further research into the gyral surface of the anterior cingulate cortex and ventromedial prefrontal cortex could provide more targeted therapies to help ameliorate social deficits in autism spectrum disorder.

Corticostriatal connectivity fingerprints: Probability maps based on resting-state functional connectivity.

Over the last decade, structure-function relationships have begun to encompass networks of brain areas rather than individual structures. For example, corticostriatal circuits have been associated with sensorimotor, limbic, and cognitive information processing, and damage to these circuits has been shown to produce unique behavioral outcomes in Autism, Parkinson's Disease, Schizophrenia and healthy ageing. However, it remains an open question how abnormal or absent connectivity can be detected at the individual level. Here, we provide a method for clustering gross morphological structures into subregions with unique functional connectivity fingerprints, and generate network probability maps usable as a baseline to compare individual cases against. We used connectivity metrics derived from resting-state fMRI (N = 100), in conjunction with hierarchical clustering methods, to parcellate the striatum into functionally distinct clusters. We identified three highly reproducible striatal subregions, across both hemispheres and in an independent replication dataset (N = 100) (dice-similarity values 0.40-1.00). Each striatal seed region resulted in a highly reproducible distinct connectivity fingerprint: the putamen showed predominant connectivity with cortical and cerebellar sensorimotor and language processing areas; the ventromedial striatum cluster had a distinct limbic connectivity pattern; the caudate showed predominant connectivity with the thalamus, frontal and occipital areas, and the cerebellum. Our corticostriatal probability maps agree with existing connectivity data in humans and non-human primates, and showed a high degree of replication. We believe that these maps offer an efficient tool to further advance hypothesis driven research and provide important guidance when investigating deviant connectivity in neurological patient populations suffering from e.g., stroke or cerebral palsy. Hum Brain Mapp 38:1478-1491, 2017. © 2016 Wiley Periodicals, Inc.

Cerebellar atrophy in Parkinson's disease and its implication for network connectivity.

Pathophysiological and atrophic changes in the cerebellum are documented in Parkinson's disease. Without compensatory activity, such abnormalities could potentially have more widespread effects on both motor and non-motor symptoms. We examined how atrophic change in the cerebellum impacts functional connectivity patterns within the cerebellum and between cerebellar-cortical networks in 42 patients with Parkinson's disease and 29 control subjects. Voxel-based morphometry confirmed grey matter loss across the motor and cognitive cerebellar territories in the patient cohort. The extent of cerebellar atrophy correlated with decreased resting-state connectivity between the cerebellum and large-scale cortical networks, including the sensorimotor, dorsal attention and default networks, but with increased connectivity between the cerebellum and frontoparietal networks. The severity of patients' motor impairment was predicted by a combination of cerebellar atrophy and decreased cerebellar-sensorimotor connectivity. These findings demonstrate that cerebellar atrophy is related to both increases and decreases in cerebellar-cortical connectivity in Parkinson's disease, identifying potential cerebellar driven functional changes associated with sensorimotor deficits. A post hoc analysis exploring the effect of atrophy in the subthalamic nucleus, a cerebellar input source, confirmed that a significant negative relationship between grey matter volume and intrinsic cerebellar connectivity seen in controls was absent in the patients. This suggests that the modulatory relationship of the subthalamic nucleus on intracerebellar connectivity is lost in Parkinson's disease, which may contribute to pathological activation within the cerebellum. The results confirm significant changes in cerebellar network activity in Parkinson's disease and reveal that such changes occur in association with atrophy of the cerebellum.

Bridging the gap between functional and anatomical features of cortico-cerebellar circuits using meta-analytic connectivity modeling.

Theories positing that the cerebellum contributes to cognitive as well as motor control are driven by two sources of information: (1) studies highlighting connections between the cerebellum and both prefrontal and motor territories, (2) functional neuroimaging studies demonstrating cerebellar activations evoked during the performance of both cognitive and motor tasks. However, almost no studies to date have combined these two sources of information and investigated cortico-cerebellar connectivity during task performance. Through the use of a novel neuroimaging tool (Meta-Analytic Connectivity Modelling) we demonstrate for the first time that cortico-cerebellar connectivity patterns seen in anatomical studies and resting fMRI are also present during task performance. Consistent with human and nonhuman primate anatomical studies cerebellar lobules Crus I and II were significantly coactivated with prefrontal and parietal cortices during task performance, whilst lobules HV, HVI, HVIIb, and HVIII were significantly coactivated with the pre- and postcentral gyrus. An analysis of the behavioral domains showed that these circuits were driven by distinct tasks. Prefrontal-parietal-cerebellar circuits were more active during cognitive and emotion tasks whilst motor-cerebellar circuits were more active during action execution tasks. These results highlight the separation of prefrontal and motor cortico-cerebellar loops during task performance, and further demonstrate that activity within these circuits relates to distinct functions.

Pupil diameter covaries with BOLD activity in human locus coeruleus.

The locus coeruleus-noradrenergic (LC-NA) neuromodulatory system has been implicated in a broad array of cognitive processes, yet scope for investigating this system's function in humans is currently limited by an absence of reliable non-invasive measures of LC activity. Although pupil diameter has been employed as a proxy measure of LC activity in numerous studies, empirical evidence for a relationship between the two is lacking. In the present study, we sought to rigorously probe the relationship between pupil diameter and BOLD activity localized to the human LC. Simultaneous pupillometry and fMRI revealed a relationship between continuous pupil diameter and BOLD activity in a dorsal pontine cluster overlapping with the LC, as localized via neuromelanin-sensitive structural imaging and an LC atlas. This relationship was present both at rest and during performance of a two-stimulus oddball task, with and without spatial smoothing of the fMRI data, and survived retrospective image correction for physiological noise. Furthermore, the spatial extent of this pupil/LC relationship guided a volume-of-interest analysis in which we provide the first demonstration in humans of a fundamental characteristic of animal LC activity: phasic modulation by oddball stimulus relevance. Taken together, these findings highlight the potential for utilizing pupil diameter to achieve a more comprehensive understanding of the role of the LC-NA system in human cognition.

The effects of immunologic brainstem encephalopathy on cognitive function following awakening from a progressive autoimmune coma.

We describe a unique patient who experienced a progressive autoimmune coma from age 14 to 17. The patient awoke after treatment with immunosuppressant medication. Although alertness, verbalization, and mobilization markedly improved, the patient reported persistent cognitive difficulties. Neuropsychological assessment from age 21 showed impairments in selective attention, distractibility, and memory. Conversely, higher-order executive functions were preserved. Electrophysiological analysis also identified abnormal neural signatures of selective attention. Eighteen months after the neuropsychological assessment, voxel-based morphometry revealed reduced white matter in the medulla compared to controls. The findings are discussed in terms of the impact of brainstem encephalopathy on cognitive mechanisms.

Cerebellum and cognition: evidence for the encoding of higher order rules.

Converging anatomical and functional evidence suggests that the cerebellum processes both motor and nonmotor information originating from the primary motor cortex and prefrontal cortex, respectively. However, it has not been established whether the cerebellum only processes prefrontal information where rules specify actions or whether the cerebellum processes any form of prefrontal information no matter how abstract. Using functional magnetic resonance imaging, we distinguish between two competing hypotheses: (1) activity within prefrontal-projecting cerebellar lobules (Crus I and II) will only be evoked by rules that specify action (i.e. first-order rules; arbitrary S-R mappings) and (2) activity will be evoked in these lobules by both first-order rules and second-order rules that govern the application of lower order rules. The results showed that prefrontal-projecting cerebellar lobules Crus I and II were commonly activated by processing both first- and second-order rules. We demonstrate for the first time that cerebellar circuits engage both first- and second-order rules and in doing so show that the cerebellum can contribute to cognitive control independent of motor control.

Cerebellar plasticity and the automation of first-order rules.

Theories of corticocerebellar function propose roles for the cerebellum in automating motor control, a process thought to depend on plasticity in cerebellar circuits that exchange information with the motor cortex. Little is known, however, about automating behaviors beyond the motor domain. The present study tested the hypothesis that cerebellar plasticity also subserves the development of automaticity in behavior based on low-order rules. Human subjects were required to learn two sets of first-order rules in which visual stimuli of different shapes each arbitrarily instructed a particular finger movement. We used event-related functional magnetic resonance imaging to scan subjects while these response rules became increasingly automatic with practice, as assessed with a dual-task procedure. We found that the amplitude of the blood oxygenation level-dependent signal gradually decreased as a function of practice, as responses became increasingly automatic, and that this effect was greater for a set of rules that became automatic rapidly compared with a second set, which became automatic more slowly. These trial-by-trial activity changes occurred in Crus I of cerebellar cortical lobule HVIIA, in which neurons exchange information with the prefrontal cortex rather than the motor cortex. Activity in Crus I was time locked specifically to the processing of these rules, rather than to subsequent actions. The results support the hypothesis that decreases in cerebellar cortical activity underlie the automation of behavior, whether related to motor control and motor cortex or to response rules and prefrontal cortex.

Contagion of Temporal Discounting Value Preferences in Neurotypical and Autistic Adults.

Neuroeconomics paradigms have demonstrated that learning about another's beliefs can make you more like them (i.e., contagion). Due to social deficits in autism, it is possible that autistic individuals will be immune to contagion. We fit Bayesian computational models to a temporal discounting task, where participants made decisions for themselves before and after learning the distinct preferences of two others. Two independent neurotypical samples (N = 48; N = 98) both showed a significant contagion effect; however the strength of contagion was unrelated to autistic traits. Equivalence tests showed autistic (N = 12) and matched neurotypical N = 12) samples had similar levels of contagion and accuracy when learning about others. Despite social impairments being at the core of autistic symptomatology, contagion of value preferences appears to be intact.

A probabilistic MR atlas of the human cerebellum.

The functional organization of the cerebellum is reflected in large part by the unique afferent and efferent connectivity of the individual cerebellar lobules. This functional diversity on a relatively small spatial scale makes accurate localization methods for human functional imaging and anatomical patient-based research indispensable. Here we present a probabilistic atlas of the cerebellar lobules in the anatomical space defined by the MNI152 template. We separately masked the lobules on T1-weighted MRI scans (1 mm isotropic resolution) of 20 healthy young participants (10 male, 10 female, average age 23.7 yrs). These cerebella were then aligned to the standard or non-linear version of the whole-brain MNI152 template using a number of commonly used normalization algorithms, or to a previously published cerebellum-only template (Diedrichsen, J., 2006. A spatially unbiased atlas template of the human cerebellum. NeuroImage 33, 127-138.). The resulting average overlap was higher for the cerebellum-only template than for any of the whole-brain normalization methods. The probabilistic maps allow for the valid assignment of functional activations to specific cerebellar lobules, while providing a quantitative measure of the uncertainty of such assignments. Furthermore, maximum probability maps derived from these atlases can be used to define regions of interest (ROIs) in functional neuroimaging and neuroanatomical research. The atlas, made freely available online, is compatible with a number of widely used analysis packages.

Coupling of respiration and attention via the locus coeruleus: Effects of meditation and pranayama.

The locus coeruleus (LC) has established functions in both attention and respiration. Good attentional performance requires optimal levels of tonic LC activity, and must be matched to task consistently. LC neurons are chemosensitive, causing respiratory phrenic nerve firing to increase frequency with higher CO2 levels, and as CO2 level varies with the phase of respiration, tonic LC activity should exhibit fluctuations at respiratory frequency. Top-down modulation of tonic LC activity from brain areas involved in attentional regulation, intended to optimize LC firing to suit task requirements, may have respiratory consequences as well, as increases in LC activity influence phrenic nerve firing. We hypothesize that, due to the physiological and functional overlaps of attentional and respiratory functions of the LC, this small neuromodulatory nucleus is ideally situated to act as a mechanism of synchronization between respiratory and attentional systems, giving rise to a low-amplitude oscillation that enables attentional flexibility, but may also contribute to unintended destabilization of attention. Meditative and pranayama practices result in attentional, emotional, and physiological enhancements that may be partially due to the LC's pivotal role as the nexus in this coupled system. We present original findings of synchronization between respiration and LC activity (via fMRI and pupil dilation) and provide evidence of a relationship between respiratory phase modulation and attentional performance. We also present a mathematical dynamical systems model of respiratory-LC-attentional coupling, review candidate neurophysiological mechanisms of changes in coupling dynamics, and discuss implications for attentional theory, meditation, and pranayama, and possible therapeutic applications.

Food-Predicting Stimuli Differentially Influence Eye Movements and Goal-Directed Behavior in Normal-Weight, Overweight, and Obese Individuals.

Obese individuals have been shown to exhibit abnormal sensitivity to rewards and reward-predicting cues as for example food-associated cues frequently used in advertisements. It has also been shown that food-associated cues can increase goal-directed behavior but it is currently unknown, whether this effect differs between normal-weight, overweight, and obese individuals. Here, we investigate this question by using a Pavlovian-to-instrumental transfer (PIT) task in normal-weight (N = 20), overweight (N = 17), and obese (N = 17) individuals. Furthermore, we applied eye tracking during Pavlovian conditioning to measure the participants' conditioned response as a proxy of the incentive salience of the predicted reward. Our results show that the goal-directed behavior of overweight individuals was more strongly influenced by food-predicting cues (i.e., stronger PIT effect) than that of normal-weight and obese individuals (p < 0.001). The weight groups were matched for age, gender, education, and parental education. Eye movements during Pavlovian conditioning also differed between weight categories (p < 0.05) and were used to categorize individuals based on their fixation style into "high eye index" versus "low eye index" as well. Our main finding was that the fixation style exhibited a complex interaction with the weight category. Furthermore, we found that normal-weight individuals of the group "high eye index" had higher body mass index within the healthy range than individuals of the group "low eye index" (p < 0.001), but this relationship was not found within in the overweight or obese groups (p > 0.646). Our findings are largely consistent with the incentive sensitization theory predicting that overweight individuals are more susceptible to food-related cues than normal-weight controls. However, this hypersensitivity might be reduced in obese individuals, possibly due to habitual/compulsive overeating or differences in reward valuation.

Enhancing studies of the connectome in autism using the autism brain imaging data exchange II.

The second iteration of the Autism Brain Imaging Data Exchange (ABIDE II) aims to enhance the scope of brain connectomics research in Autism Spectrum Disorder (ASD). Consistent with the initial ABIDE effort (ABIDE I), that released 1112 datasets in 2012, this new multisite open-data resource is an aggregate of resting state functional magnetic resonance imaging (MRI) and corresponding structural MRI and phenotypic datasets. ABIDE II includes datasets from an additional 487 individuals with ASD and 557 controls previously collected across 16 international institutions. The combination of ABIDE I and ABIDE II provides investigators with 2156 unique cross-sectional datasets allowing selection of samples for discovery and/or replication. This sample size can also facilitate the identification of neurobiological subgroups, as well as preliminary examinations of sex differences in ASD. Additionally, ABIDE II includes a range of psychiatric variables to inform our understanding of the neural correlates of co-occurring psychopathology; 284 diffusion imaging datasets are also included. It is anticipated that these enhancements will contribute to unraveling key sources of ASD heterogeneity.

Monetary, Food, and Social Rewards Induce Similar Pavlovian-to-Instrumental Transfer Effects.

Multiple types of reward, such as money, food or social approval, are capable of driving behavior. However, most previous investigations have only focused on one of these reward classes in isolation, as such it is not clear whether different reward classes have a unique influence on instrumental responding or whether the subjective value of the reward, rather than the reward type per se, is most important in driving behavior. Here, we investigate behavior using a well-established reward paradigm, Pavlovian-to-instrumental transfer (PIT), and three different reward types: monetary, food and social rewards. The subjective value of each reward type was matched using a modified Becker-DeGroot-Marschak (BDM) auction where subjective reward value was expressed through physical effort using a bimanual grip force task. We measured the influence of reward-associated stimuli on how participants distributed forces between hands when reaching a target effort range on the screen bimanually and on how much time participants spent in this target range. Participants spent significantly more time in the target range (15% ± 2% maximal voluntary contraction) when a stimulus was presented that was associated with a reward used during instrumental conditioning or Pavlovian conditioning compared to a stimulus associated with a neutral outcome (i.e., general PIT). The strength of the PIT effect was modulated by subjective value (i.e., individuals who showed a stronger PIT effect rated the value of rewards more highly), but not by reward type, demonstrating that stimuli of all reward types were able to act as appetitive reinforcers and influenced instrumental responding, when matched to the same subjective reward value. This is the first demonstration that individually matched monetary, food and social rewards are equally effective as appetitive reinforcers in PIT. These findings strengthen the hypotheses that the subjective value is crucial for how much reward-associated stimuli influence behavior.

Promises, Pitfalls, and Basic Guidelines for Applying Machine Learning Classifiers to Psychiatric Imaging Data, with Autism as an Example.

Most psychiatric disorders are associated with subtle alterations in brain function and are subject to large interindividual differences. Typically, the diagnosis of these disorders requires time-consuming behavioral assessments administered by a multidisciplinary team with extensive experience. While the application of Machine Learning classification methods (ML classifiers) to neuroimaging data has the potential to speed and simplify diagnosis of psychiatric disorders, the methods, assumptions, and analytical steps are currently opaque and not accessible to researchers and clinicians outside the field. In this paper, we describe potential classification pipelines for autism spectrum disorder, as an example of a psychiatric disorder. The analyses are based on resting-state fMRI data derived from a multisite data repository (ABIDE). We compare several popular ML classifiers such as support vector machines, neural networks, and regression approaches, among others. In a tutorial style, written to be equally accessible for researchers and clinicians, we explain the rationale of each classification approach, clarify the underlying assumptions, and discuss possible pitfalls and challenges. We also provide the data as well as the MATLAB code we used to achieve our results. We show that out-of-the-box ML classifiers can yield classification accuracies of about 60-70%. Finally, we discuss how classification accuracy can be further improved, and we mention methodological developments that are needed to pave the way for the use of ML classifiers in clinical practice.

Connectivity-based parcellation increases network detection sensitivity in resting state fMRI: An investigation into the cingulate cortex in autism.

Although resting state fMRI (RS-fMRI) is increasingly used to generate biomarkers of psychiatric illnesses, analytical choices such as seed size and placement can lead to variable findings. Seed placement especially impacts on RS-fMRI studies of Autism Spectrum Disorder (ASD), because individuals with ASD are known to possess more variable network topographies. Here, we present a novel pipeline for analysing RS-fMRI in ASD using the cingulate cortex as an exemplar anatomical region of interest. Rather than using seeds based on previous literature, or gross morphology, we used a combination of structural information, task-independent (RS-fMRI) and task-dependent functional connectivity (Meta-Analytic Connectivity Modeling) to partition the cingulate cortex into six subregions with unique connectivity fingerprints and diverse behavioural profiles. This parcellation was consistent between groups and highly replicable across individuals (up to 93% detection) suggesting that the organisation of cortico-cingulo connections is highly similar between groups. However, our results showed an age-related increase in connectivity between the anterior middle cingulate cortex and right lateral prefrontal cortex in ASD, whilst this connectivity decreased in controls. There was also a Group × Grey Matter (GM) interaction, showing increased connectivity between the anterior cingulate cortex and the rectal gyrus in concert with increasing rectal gyrus GM in controls. By comparing our approach to previously established methods we revealed that our approach improves network detection in both groups, and that the ability to detect group differences using 4 mm radius spheres varies greatly with seed placement. Using our multi-modal approach we find disrupted cortico-cingulo circuits that, based on task-dependent information, may contribute to ASD deficits in attention and social interaction. Moreover, we highlight how more sensitive approaches to RS-fMRI are crucial for establishing robust and reproducible connectivity-based biomarkers in psychiatric disorders.

The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance.

Higher brain function relies upon the ability to flexibly integrate information across specialized communities of brain regions; however, it is unclear how this mechanism manifests over time. In this study, we used time-resolved network analysis of fMRI data to demonstrate that the human brain traverses between functional states that maximize either segregation into tight-knit communities or integration across otherwise disparate neural regions. Integrated states enable faster and more accurate performance on a cognitive task, and are associated with dilations in pupil diameter, suggesting that ascending neuromodulatory systems may govern the transition between these alternative modes of brain function. Together, our results confirm a direct link between cognitive performance and the dynamic reorganization of the network structure of the brain.