Unmasking Latent Inhibitory Connections in Human Cortex to Reveal Dormant Cortical Memories

Balance of cortical excitation and inhibition (EI) is thought to be disrupted in several neuropsychiatric conditions, yet it is not clear how it is maintained in the healthy human brain. When EI balance is disturbed during learning and memory in animal models, it can be restabilized via formation of inhibitory replicas of newly formed excitatory connections. Here we assess evidence for such selective inhibitory rebalancing in humans. Using fMRI repetition suppression we measure newly formed cortical associations in the human brain. We show that expression of these associations reduces over time despite persistence in behavior, consistent with inhibitory rebalancing. To test this, we modulated excitation/inhibition balance with transcranial direct current stimulation (tDCS). Using ultra-high-field (7T) MRI and spectroscopy, we show that reducing GABA allows cortical associations to be re-expressed. This suggests that in humans associative memories are stored in balanced excitatory-inhibitory ensembles that lie dormant unless latent inhibitory connections are unmasked.

Video Abstract

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Associative memories can be measured in human cortex following learning

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Over time the expression of these memories becomes silenced

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By reducing cortical GABA with brain stimulation these memories are re-expressed

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Cortical memories appear to be stored in balanced excitatory-inhibitory ensembles

Shifts in reinforcement signalling while playing slot-machines as a function of prior experience and impulsivity

Electronic gaming machines (EGMs) offer significant revenue streams for mercantile gambling. However, limited clinical and experimental evidence suggests that EGMs are associated with heightened risks of clinically problematic patterns of play. Little is known about the neural structures that might mediate the transition from exploratory EGM play to the ‘addictive’ play seen in problem gamblers; neither is it known how personality traits associated with gambling activity (and gambling problems) influence reinforcement processing while playing EGMs. Using functional magnetic resonance imaging in healthy participants, we show that a single episode of slot-machine play is subsequently associated with reduced amplitudes of blood-oxygenation level-dependent signals within reinforcement-related structures, such as the ventral striatum and caudate nucleus, following winning game outcomes; but increased amplitudes of anticipatory signals within the ventral striatum and amygdala while watching the game reels spin. Trait impulsivity enhanced positive signals within the ventral striatum and amygdala following the delivery of winning outcomes but diminished positive signals following the experience of almost-winning (’near-misses’). These results indicate that a single episode of slot-machine play engages the well-characterised reinforcement-learning mechanisms mediated by ascending dopamine mesolimbic and mesostriatal pathways, to shift reward value of EGMs away from game outcomes towards anticipatory states. Impulsivity, itself linked to problem gambling and heightened vulnerability to other addictive disorders, is associated with divergent coding of winning outcomes and almost-winning experiences within the ventral striatum and amygdala, potentially enhancing the reward value of successful slot-machine game outcomes but, at the same time,modulating the aversive motivational consequences of near-miss outcomes.

Shifts in reinforcement signalling while playing slot-machines as a function of prior experience and impulsivity

Electronic gaming machines (EGMs) offer significant revenue streams for mercantile gambling. However, limited clinical and experimental evidence suggests that EGMs are associated with heightened risks of clinically problematic patterns of play. Little is known about the neural structures that might mediate the transition from exploratory EGM play to the 'addictive' play seen in problem gamblers; neither is it known how personality traits associated with gambling activity (and gambling problems) influence reinforcement processing while playing EGMs. Using functional magnetic resonance imaging in healthy participants, we show that a single episode of slot-machine play is subsequently associated with reduced amplitudes of blood-oxygenation-level-dependent signals within reinforcement-related structures, such as the ventral striatum and caudate nucleus, following winning game outcomes; but increased amplitudes of anticipatory signals within the ventral striatum and amygdala while watching the game reels spin. Trait impulsivity enhanced positive signals within the ventral striatum and amygdala following the delivery of winning outcomes but diminished positive signals following the experience of almost-winning ('near-misses'). These results indicate that a single episode of slot-machine play engages the well-characterised reinforcement-learning mechanisms mediated by ascending dopamine mesolimbic and mesostriatal pathways, to shift reward value of EGMs away from game outcomes towards anticipatory states. Impulsivity, itself linked to problem gambling and heightened vulnerability to other addictive disorders, is associated with divergent coding of winning outcomes and almost-winning experiences within the ventral striatum and amygdala, potentially enhancing the reward value of successful slot-machine game outcomes but, at the same time, modulating the aversive motivational consequences of near-miss outcomes.

The Human Connectome Project: a data acquisition perspective

The Human Connectome Project (HCP) is an ambitious 5-year effort to characterize brain connectivity and function and their variability in healthy adults. This review summarizes the data acquisition plans being implemented by a consortium of HCP investigators who will study a population of 1200 subjects (twins and their non-twin siblings) using multiple imaging modalities along with extensive behavioral and genetic data. The imaging modalities will include diffusion imaging (dMRI), resting-state fMRI (R-fMRI), task-evoked fMRI (T-fMRI), T1- and T2-weighted MRI for structural and myelin mapping, plus combined magnetoencephalography and electroencephalography (MEG/EEG). Given the importance of obtaining the best possible data quality, we discuss the efforts underway during the first two years of the grant (Phase I) to refine and optimize many aspects of HCP data acquisition, including a new 7T scanner, a customized 3T scanner, and improved MR pulse sequences.

The danger of systematic bias in group-level FMRI-lag-based causality estimation

Schippers, Renken and Keysers (NeuroImage, 2011) present a simulation of multi-subject lag-based causality estimation. We fully agree that single-subject evaluations (e.g., Smith et al., 2011) need to be revisited in the context of multi-subject studies, and Schippers' paper is a good example, including detailed multi-level simulation and cross-subject statistical modelling. The authors conclude that "the average chance to find a significant Granger causality effect when no actual influence is present in the data stays well below the p-level imposed on the second level statistics" and that "when the analyses reveal a significant directed influence, this direction was accurate in the vast majority of the cases". Unfortunately, we believe that the general meaning that may be taken from these statements is not supported by the paper's results, as there may in reality be a systematic (group-average) difference in haemodynamic delay between two brain areas. While many statements in the paper (e.g., the final two sentences) do refer to this problem, we fear that the overriding message that many readers may take from the paper could cause misunderstanding.

Network analysis detects changes in the contralesional hemisphere following stroke

Changes in brain structure occur in remote regions following focal damage such as stroke. Such changes could disrupt processing of information across widely distributed brain networks. We used diffusion MRI tractography to assess connectivity between brain regions in 9 chronic stroke patients and 18 age-matched controls. We applied complex network analysis to calculate 'communicability', a measure of the ease with which information can travel across a network. Clustering individuals based on communicability separated patient and control groups, not only in the lesioned hemisphere but also in the contralesional hemisphere, despite the absence of gross structural pathology in the latter. In our highly selected patient group, lesions were localised to the left basal ganglia/internal capsule. We found reduced communicability in patients in regions surrounding the lesions in the affected hemisphere. In addition, communicability was reduced in homologous locations in the contralesional hemisphere for a subset of these regions. We interpret this as evidence for secondary degeneration of fibre pathways which occurs in remote regions interconnected, directly or indirectly, with the area of primary damage. We also identified regions with increased communicability in patients that could represent adaptive, plastic changes post-stroke. Network analysis provides new and powerful tools for understanding subtle changes in interactions across widely distributed brain networks following stroke.

Multiple-subjects connectivity-based parcellation using hierarchical Dirichlet process mixture models

We propose a hierarchical infinite mixture model approach to address two issues in connectivity-based parcellations: (i) choosing the number of clusters, and (ii) combining data from different subjects. In a Bayesian setting, we model voxel-wise anatomical connectivity profiles as an infinite mixture of multivariate Gaussian distributions, with a Dirichlet process prior on the cluster parameters. This type of prior allows us to conveniently model the number of clusters and estimate its posterior distribution directly from the data. An important benefit of using Bayesian modelling is the extension to multiple subjects clustering via a hierarchical mixture of Dirichlet processes. Data from different subjects are used to infer on class parameters and the number of classes at individual and group level. Such a method accounts for inter-subject variability, while still benefiting from combining different subjects data to yield more robust estimates of the individual clusterings.

Anatomical connectivity of the subgenual cingulate region targeted with deep brain stimulation for treatment-resistant depression

Chronic deep brain stimulation (DBS) of subgenual cingulate white matter results in dramatic remission of symptoms in some previously treatment-resistant depression patients. The effects of stimulation may be mediated locally or via corticocortical or corticosubcortical connections. We use tractography to define the likely connectivity of cingulate regions stimulated in DBS-responsive patients using diffusion imaging data acquired in healthy control subjects. We defined 2 distinct regions within anterior cingulate cortex based on anatomical connectivity: a pregenual region strongly connected to medial prefrontal and anterior midcingulate cortex and a subgenual region with strongest connections to nucleus accumbens, amygdala, hypothalamus, and orbitofrontal cortex. The location of electrode contact points from 9 patients successfully treated with DBS lies within this subgenual region. The anatomical connectivity of the subgenual cingulate region targeted with DBS for depression supports the hypothesis that treatment efficacy is mediated via effects on a distributed network of frontal, limbic, and visceromotor brain regions. At present, targeting of DBS for depression is based on landmarks visible in conventional magnetic resonance imaging. Preoperatively acquired diffusion imaging for connectivity-based cortical mapping could improve neurosurgical targeting. We hypothesize that the subgenual region with greatest connectivity across the distributed network described here may prove most effective.

A Bayesian framework for global tractography

We readdress the diffusion tractography problem in a global and probabilistic manner. Instead of tracking through local orientations, we parameterise the connexions between brain regions at a global level, and then infer on global and local parameters simultaneously in a Bayesian framework. This approach offers a number of important benefits. The global nature of the tractography reduces sensitivity to local noise and modelling errors. By constraining tractography to ensure a connexion is found, and then inferring on the exact location of the connexion, we increase the robustness of connectivity-based parcellations, allowing parcellations of connexions that were previously invisible to tractography. The Bayesian framework allows a direct comparison of the evidence for connecting and non-connecting models, to test whether the connexion is supported by the data. Crucially, by explicit parameterisation of the connexion between brain regions, we infer on a parameter that is shared with models of functional connectivity. This model is a first step toward the joint inference on functional and anatomical connectivity.

Contrasting roles for cingulate and orbitofrontal cortex in decisions and social behaviour

There is general acknowledgement that both the anterior cingulate and orbitofrontal cortex are implicated in reinforcement-guided decision making, and emotion and social behaviour. Despite the interest that these areas generate in both the cognitive neuroscience laboratory and the psychiatric clinic, ideas about the distinctive contributions made by each have only recently begun to emerge. This reflects an increasing understanding of the component processes that underlie reinforcement-guided decision making, such as the representation of reinforcement expectations, the exploration, updating and representation of action values, and the appreciation that choices are guided not just by the prospect of reward but also by the costs that action entails. Evidence is emerging to suggest that the anterior cingulate and orbitofrontal cortex make distinct contributions to each of these aspects of decision making.

Probabilistic diffusion tractography with multiple fibre orientations: What can we gain?

We present a direct extension of probabilistic diffusion tractography to the case of multiple fibre orientations. Using automatic relevance determination, we are able to perform online selection of the number of fibre orientations supported by the data at each voxel, simplifying the problem of tracking in a multi-orientation field. We then apply the identical probabilistic algorithm to tractography in the multi- and single-fibre cases in a number of example systems which have previously been tracked successfully or unsuccessfully with single-fibre tractography. We show that multi-fibre tractography offers significant advantages in sensitivity when tracking non-dominant fibre populations, but does not dramatically change tractography results for the dominant pathways.

Between session reproducibility and between subject variability of diffusion MR and tractography measures

As diffusion tractography is increasingly used to generate quantitative measures to address clinical questions, it is important to characterise the inter-session reproducibility and inter-subject variability of these measures. Here, we assess the reproducibility and variability of diffusion tractography measures using diffusion data from 8 subjects scanned 3 times. We used probabilistic tractography to define the cingulum bundle, pyramidal tracts, optic radiations and genu of the corpus callosum in each individual data set using three different methods of seed definition. Measures of mean fractional anisotropy (FA) and mean diffusivity (MD) along the tracts were more reproducible than measures of tract volume. Further, tracts defined using a two region of interest (ROI) approach were more reproducible than those defined using manually placed seed masks alone. For mean FA taken from tracts defined using the two ROI approach, inter-session coefficients of variation (CV) were all below 5% and inter-subject CVs were below 10%; for mean MD inter-session, CVs were all below 3% and inter-subject CVs were below 8%. We use the variability measures found here to calculate the sample sizes required to detect changes in FA, MD or tract volume of a given size, either between groups of subjects or within subjects over time. Finally, we compare tractography results using 60 diffusion encoding directions to those found using a subset of 12 directions; the number of diffusion directions did not have a significant effect on reproducibility, but tracts derived using fewer directions were consistently smaller than those derived using 60 direction data. We suggest that 12 direction data are sufficient for reproducibly defining the core of large bundles but may be less sensitive to smaller pathways.

Reliable identification of the auditory thalamus using multi-modal structural analyses

The medial geniculate body (MGB) of the thalamus is a key component of the auditory system. It is involved in relaying and transforming auditory information to the cortex and in top-down modulation of processing in the midbrain, brainstem, and ear. Functional imaging investigations of this region in humans, however, have been limited by the difficulty of distinguishing MGB from other thalamic nuclei. Here, we introduce two methods for reliably delineating MGB anatomically in individuals based on conventional and diffusion MRI data. The first uses high-resolution proton density weighted scanning optimized for subcortical grey-white contrast. The second uses diffusion-weighted imaging and probabilistic tractography to automatically segment the medial and lateral geniculate nuclei from surrounding structures based on their distinctive patterns of connectivity to the rest of the brain. Both methods produce highly replicable results that are consistent with published atlases. Importantly, both methods rely on commonly available imaging sequences and standard hardware, a significant advantage over previously described approaches. In addition to providing useful approaches for identifying the MGB and LGN in vivo, our study offers further validation of diffusion tractography for the parcellation of grey matter regions on the basis of their connectivity patterns.

Connection patterns distinguish 3 regions of human parietal cortex

Three regions of the macaque inferior parietal lobule and adjacent lateral intraparietal sulcus (IPS) are distinguished by the relative strengths of their connections with the superior colliculus, parahippocampal gyrus, and ventral premotor cortex. It was hypothesized that connectivity information could therefore be used to identify similar areas in the human parietal cortex using diffusion-weighted imaging and probabilistic tractography. Unusually, the subcortical routes of the 3 projections have been reported in the macaque, so it was possible to compare not only the terminations of connections but also their course. The medial IPS had the highest probability of connection with the superior colliculus. The projection pathway resembled that connecting parietal cortex and superior colliculus in the macaque. The posterior angular gyrus and the adjacent superior occipital gyrus had a high probability of connection with the parahippocampal gyrus. The projection pathway resembled the macaque inferior longitudinal fascicle, which connects these areas. The ventral premotor cortex had a high probability of connection with the supramarginal gyrus and anterior IPS. The connection was mediated by the third branch of the superior longitudinal fascicle, which interconnects similar regions in the macaque. Human parietal areas have anatomical connections resembling those of functionally related macaque parietal areas.

Distinct right frontal lobe activation in language processing following left hemisphere injury

Right hemisphere activation during functional imaging studies of language has frequently been reported following left hemisphere injury. Few studies have anatomically characterized the specific right hemisphere structures engaged. We used functional MRI (fMRI) with verbal fluency tasks in 12 right-handed patients with left temporal lobe epilepsy (LTLE) and 12 right-handed healthy controls to localize language-related activity in the right inferior frontal gyrus (RIFG). During the phonemic task, LTLE patients activated a significantly more posterior region of the right anterior insula/frontal operculum than healthy controls (P = 0.02). Activation of the left inferior frontal gyrus (LIFG) did not differ significantly between the two groups. This suggests that, following left hemisphere injury, language-related processing in the right hemisphere differs from that with a functionally normal left hemisphere. The localization of activation in the left and right inferior frontal gyri was determined with respect to the anatomical sub-regions pars opercularis (Pop), pars triangularis (Ptr) and pars orbitalis (Por). In the LIFG, both healthy controls (8 out of 12) and LTLE patients (9 out of 12) engaged primarily Pop during phonemic fluency. Activations in the RIFG, however, were located mostly in the anterior insula/frontal operculum in both healthy controls (8 out of 12) and LTLE patients (8 out of 12), albeit in distinct regions. Mapping the locations of peak voxels in relation to previously obtained cytoarchitectonic maps of Broca's area confirmed lack of homology between activation regions in the left and right IFG. Verbal fluency-related activation in the RIFG was not anatomically homologous to LIFG activation in either patients or controls. To test more directly whether RIFG activation shifts in a potentially adaptive manner after left hemisphere injury, fMRI studies were performed in a patient prior to and following anatomical left hemispherectomy for the treatment of Rasmussen's encephalitis. An increase in activation magnitude and posterior shift in location were found in the RIFG after hemispherectomy for both phonemic and semantic tasks. Together, these results suggest that left temporal lobe injury is associated with potentially adaptive changes in right inferior frontal lobe functions in processing related to expressive language.

A consistent relationship between local white matter architecture and functional specialisation in medial frontal cortex

Functionally significant landmarks in the brain do not necessarily align with local sulcal and gyral architecture in a manner that is consistent across individuals. However, the functional specialisation underlying these landmarks is strongly constrained by the connectional architecture of the region. Here, we explore this relationship in the supplementary motor area (SMA) and pre-SMA in the medial frontal cortex of the human brain. Using diffusion tensor, conventional and functional MR imaging, we find that the location of the functional boundary between SMA and preSMA is more consistent with respect to specific features of the local white matter as it approaches neocortex than with respect to the local gyral and sulcal anatomy in the region.

Relating connectional architecture to grey matter function using diffusion imaging

Understanding brain function in terms of connectional architecture is a major goal of neuroimaging. However, direct investigation of the influence of brain circuitry on function has been hindered by the lack of a technique for exploring anatomical connectivity in the in vivo brain. Recent advances in magnetic resonance diffusion imaging have given scientists access to data relating to local white matter architecture and, for the first time, have raised the possibility of in vivo investigations into brain circuitry. This review investigates whether diffusion imaging may be used to identify regions of grey matter that are distinct in their connectional architecture, and whether these connectional differences are reflected either in local cytoarchitecture or in local grey matter function. Establishing a direct relationship between regional boundaries based on diffusion imaging and borders between regions that perform different functions would not only be of great significance when interpreting functional results, but would also provide a first step towards the validation of diffusion-based anatomical connectivity studies.

Changes in connectivity profiles define functionally distinct regions in human medial frontal cortex

A fundamental issue in neuroscience is the relation between structure and function. However, gross landmarks do not correspond well to microstructural borders and cytoarchitecture cannot be visualized in a living brain used for functional studies. Here, we used diffusion-weighted and functional MRI to test structure-function relations directly. Distinct neocortical regions were defined as volumes having similar connectivity profiles and borders identified where connectivity changed. Without using prior information, we found an abrupt profile change where the border between supplementary motor area (SMA) and pre-SMA is expected. Consistent with this anatomical assignment, putative SMA and pre-SMA connected to motor and prefrontal regions, respectively. Excellent spatial correlations were found between volumes defined by using connectivity alone and volumes activated during tasks designed to involve SMA or pre-SMA selectively. This finding demonstrates a strong relationship between structure and function in medial frontal cortex and offers a strategy for testing such correspondences elsewhere in the brain.

Characterization and propagation of uncertainty in diffusion-weighted MR imaging

A fully probabilistic framework is presented for estimating local probability density functions on parameters of interest in a model of diffusion. This technique is applied to the estimation of parameters in the diffusion tensor model, and also to a simple partial volume model of diffusion. In both cases the parameters of interest include parameters defining local fiber direction. A technique is then presented for using these density functions to estimate global connectivity (i.e., the probability of the existence of a connection through the data field, between any two distant points), allowing for the quantification of belief in tractography results. This technique is then applied to the estimation of the cortical connectivity of the human thalamus. The resulting connectivity distributions correspond well with predictions from invasive tracer methods in nonhuman primate.

Non-invasive mapping of connections between human thalamus and cortex using diffusion imaging

Evidence concerning anatomical connectivities in the human brain is sparse and based largely on limited post-mortem observations. Diffusion tensor imaging has previously been used to define large white-matter tracts in the living human brain, but this technique has had limited success in tracing pathways into gray matter. Here we identified specific connections between human thalamus and cortex using a novel probabilistic tractography algorithm with diffusion imaging data. Classification of thalamic gray matter based on cortical connectivity patterns revealed distinct subregions whose locations correspond to nuclei described previously in histological studies. The connections that we found between thalamus and cortex were similar to those reported for non-human primates and were reproducible between individuals. Our results provide the first quantitative demonstration of reliable inference of anatomical connectivity between human gray matter structures using diffusion data and the first connectivity-based segmentation of gray matter.