Ilan Dinstein

The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism

Autism spectrum disorders (ASDs) represent a formidable challenge for psychiatry and neuroscience because of their high prevalence, lifelong nature, complexity and substantial heterogeneity. Facing these obstacles requires large-scale multidisciplinary efforts. Although the field of genetics has pioneered data sharing for these reasons, neuroimaging had not kept pace. In response, we introduce the Autism Brain Imaging Data Exchange (ABIDE)—a grassroots consortium aggregating and openly sharing 1112 existing resting-state functional magnetic resonance imaging (R-fMRI) data sets with corresponding structural MRI and phenotypic information from 539 individuals with ASDs and 573 age-matched typical controls (TCs; 7–64 years) (http://fcon_1000.projects.nitrc.org/indi/abide/). Here, we present this resource and demonstrate its suitability for advancing knowledge of ASD neurobiology based on analyses of 360 male subjects with ASDs and 403 male age-matched TCs. We focused on whole-brain intrinsic functional connectivity and also survey a range of voxel-wise measures of intrinsic functional brain architecture. Whole-brain analyses reconciled seemingly disparate themes of both hypo- and hyperconnectivity in the ASD literature; both were detected, although hypoconnectivity dominated, particularly for corticocortical and interhemispheric functional connectivity. Exploratory analyses using an array of regional metrics of intrinsic brain function converged on common loci of dysfunction in ASDs (mid- and posterior insula and posterior cingulate cortex), and highlighted less commonly explored regions such as the thalamus. The survey of the ABIDE R-fMRI data sets provides unprecedented demonstrations of both replication and novel discovery. By pooling multiple international data sets, ABIDE is expected to accelerate the pace of discovery setting the stage for the next generation of ASD studies.

Interhemispheric correlations of slow spontaneous neuronal fluctuations revealed in human sensory cortex

Animal studies have shown robust electrophysiological activity in the sensory cortex in the absence of stimuli or tasks. Similarly, recent human functional magnetic resonance imaging (fMRI) revealed widespread, spontaneously emerging cortical fluctuations. However, it is unknown what neuronal dynamics underlie this spontaneous activity in the human brain. Here we studied this issue by combining bilateral single-unit, local field potentials (LFPs) and intracranial electrocorticography (ECoG) recordings in individuals undergoing clinical monitoring. We found slow (<0.1 Hz, following 1/f-like profiles) spontaneous fluctuations of neuronal activity with significant interhemispheric correlations. These fluctuations were evident mainly in neuronal firing rates and in gamma (40–100 Hz) LFP power modulations. Notably, the interhemispheric correlations were enhanced during rapid eye movement and stage 2 sleep. Multiple intracranial ECoG recordings revealed clear selectivity for functional networks in the spontaneous gamma LFP power modulations. Our results point to slow spontaneous modulations in firing rate and gamma LFP as the likely correlates of spontaneous fMRI fluctuations in the human sensory cortex.

Executed and Observed Movements Have Different Distributed Representations in Human aIPS

How similar are the representations of executed and observed hand movements in the human brain? We used functional magnetic resonance imaging (fMRI) and multivariate pattern classification analysis to compare spatial distributions of cortical activity in response to several observed and executed movements. Subjects played the rock–paper–scissors game against a videotaped opponent, freely choosing their movement on each trial and observing the opponents hand movement after a short delay. The identities of executed movements were correctly classified from fMRI responses in several areas of motor cortex, observed movements were classified from responses in visual cortex, and both observed and executed movements were classified from responses in either left or right anterior intraparietal sulcus (aIPS). We interpret above chance classification as evidence for reproducible, distributed patterns of cortical activity that were unique for execution and/or observation of each movement. Responses in aIPS enabled accurate classification of movement identity within each modality (visual or motor), but did not enable accurate classification across modalities (i.e., decoding observed movements from a classifier trained on executed movements and vice versa). These results support theories regarding the central role of aIPS in the perception and execution of movements. However, the spatial pattern of activity for a particular observed movement was distinctly different from that for the same movement when executed, suggesting that observed and executed movements are mostly represented by distinctly different subpopulations of neurons in aIPS.

Normal Movement Selectivity in Autism

It has been proposed that individuals with autism have difficulties understanding the goals and intentions of others because of a fundamental dysfunction in the mirror neuron system. Here, however, we show that individuals with autism exhibited not only normal fMRI responses in mirror system areas during observation and execution of hand movements but also exhibited typical movement-selective adaptation (repetition suppression) when observing or executing the same movement repeatedly. Movement selectivity is a defining characteristic of neurons involved in movement perception, including mirror neurons, and, as such, these findings argue against a mirror system dysfunction in autism.

Influence of meditation on anti-correlated networks in the brain

Human experiences can be broadly divided into those that are external and related to interaction with the environment, and experiences that are internal and self-related. The cerebral cortex appears to be divided into two corresponding systems: an “extrinsic” system composed of brain areas that respond more to external stimuli and tasks and an “intrinsic” system composed of brain areas that respond less to external stimuli and tasks. These two broad brain systems seem to compete with each other, such that their activity levels over time is usually anti-correlated, even when subjects are “at rest” and not performing any task. This study used meditation as an experimental manipulation to test whether this competition (anti-correlation) can be modulated by cognitive strategy. Participants either fixated without meditation (fixation), or engaged in non-dual awareness (NDA) or focused attention (FA) meditations. We computed inter-area correlations (“functional connectivity”) between pairs of brain regions within each system, and between the entire extrinsic and intrinsic systems. Anti-correlation between extrinsic vs. intrinsic systems was stronger during FA meditation and weaker during NDA meditation in comparison to fixation (without mediation). However, correlation between areas within each system did not change across conditions. These results suggest that the anti-correlation found between extrinsic and intrinsic systems is not an immutable property of brain organization and that practicing different forms of meditation can modulate this gross functional organization in profoundly different ways.

BOLD and spiking activity

Viswanathan and Freeman1 claim that oxygen concentration and, by inference, blood oxygen level–dependent (BOLD) functional magnetic resonance imaging (fMRI) reflect synaptic activity more than spiking activity. As this is a fundamental and controversial issue in fMRI research, this claim, if incorrect, may erroneously bias the interpretation of a large body of data. The authors simultaneously recorded multi-unit activity (MUA), local field potentials (LFP) and tissue oxygen concentration in primary visual cortex of anesthetized cats stimulated with moving gratings. During high temporal-frequency stimulation, when thalamic inputs were active, but few cortical neurons responded, oxygen signals were observed without MUA. Therefore, the authors concluded that oxygen responses reflect synaptic inputs more than spiking. However, careful inspection of their results leads to the opposite conclusion and supports a tight coupling between oxygen signals and local cortical spiking. Tissue oxygen responses show an initial decrease that is attributed to local oxygen consumption (negative peak) and a delayed increase that is attributed to more global changes in blood flow (positive peak). The authors showed that the initial negative peak was greater than zero during high-frequency stimulation (when spiking activity was absent), but it was in fact 80–90% smaller to high-frequency stimulation than to low-frequency stimulation (calculated from ref. 1, see red arrow in Fig. 1). Given that roughly the same thalamic input is expected on stimulation of either temporal frequency2, we conclude that the initial negative oxygen response depended only slightly (10–20%) on thalamocortical synaptic activity and mostly (80–90%) on cortical spiking. Figure 1 MUA and oxygen responses for low (top) and high (bottom) temporal frequencies (reprinted from Viswanathan and Freeman1). What about the more widespread delayed positive oxygen response? This component was evident during stimulation at high frequencies, but only in one of their two experiments that used large stimuli. A previous study demonstrated that delayed positive oxygen responses were associated with spiking outside the field of view of the electrode when using such large stimuli3. There is, therefore, a mismatch between the spatial extents of MUA (which involves the neurons closest to the electrode tip) and positive oxygen responses (which reflect a much larger neuronal population), making the comparison between the two measurements difficult to interpret. MUA measurements may also suffer from a sampling bias by failing to record spiking activity in particular types of neurons (small neurons or specific cortical layers). For example, neurons in layer 4 and adjacent area 18 that respond to higher frequencies4,5 may have contributed to the residual LFP and delayed oxygen responses while being invisible to the MUA electrode. The problem of deducing the population’s state from a few recording sites is a general methodological concern in any attempt to compare spiking activity with LFP and vascular responses6. Spiking not detected by the electrode may be reflected in LFP and vascular responses, which sum activity over a larger population. An ostensible mismatch between the measured spiking activity and LFP or vascular responses may be a result of these biases even when the spiking, LFP and BOLD are well correlated7. We do not mean to suggest that vascular responses are driven directly by spiking, as if blood vessels are voltage sensitive. Indeed vascular responses are likely to be of synaptic origin8. In contrast to subcortical structures, however, cortical circuits are dominated by massive local connectivity in which most synaptic inputs originate from nearby neurons9 and only a small minority of inputs originate from distant sites such as the thalamus. Thus, synaptic ‘inputs’ in cerebral cortex are mostly produced by local spiking of neighboring neurons, leading invariably to a tight coupling between synaptic and spiking activity, as well as oxygen responses10. The difficulty of Viswanathan and Freeman1 in decoupling synaptic from spiking activity in the cortex is not surprising. The authors implicitly assumed a feedforward model of cortical processing, which is inaccurate. Whatever the mechanisms of neurovascular coupling are, the extent of decoupling between synaptic and spiking activity ultimately depends on the nature of cortical processing; that is, whether the cortical dynamics can be switched from a local recurrent mode to a strictly feedforward mode in which synaptic inputs to a cortical area and the targets of its spiking outputs are segregated. The Viswanathan and Freeman1 study was designed to reveal such decoupling, but the results of their experiments argue against such segregation by showing that 80-90% of the local vascular response is coupled to local spiking activity.

Neural variability: friend or foe?

Although we may not realize it, our brain function varies markedly from moment to moment such that our brain responses exhibit substantial variability across trials even in response to a simple repeating stimulus. Should we care about such within-subject variability? Are there developmental, cognitive, and clinical consequences to having a brain that is more or less variable/noisy? Although neural variability seems to be beneficial for learning, excessive levels of neural variability are apparent in individuals with different clinical disorders. We propose that measuring distinct types of neural variability in autism and other disorders is likely to reveal crucial insights regarding their neuropathology. We further discuss the importance of studying neural variability more generally across development and aging in humans.

Human Cortex: Reflections of Mirror Neurons

Claims to have identified mirror neurons in human cortex have been controversial. A recent study has applied an fMRI adaptation protocol to the problem and come up with novel evidence for the existence of movement-selective mirror neurons in human cortex.

Reduction in inter-hemispheric connectivity in disorders of consciousness.

Clinical diagnosis of disorders of consciousness (DOC) caused by brain injury poses great challenges since patients are often behaviorally unresponsive. A promising new approach towards objective DOC diagnosis may be offered by the analysis of ultra-slow (<0.1 Hz) spontaneous brain activity fluctuations measured with functional magnetic resonance imaging (fMRI) during the resting-state. Previous work has shown reduced functional connectivity within the “default network”, a subset of regions known to be deactivated during engaging tasks, which correlated with the degree of consciousness impairment. However, it remains unclear whether the breakdown of connectivity is restricted to the “default network”, and to what degree changes in functional connectivity can be observed at the single subject level. Here, we analyzed resting-state inter-hemispheric connectivity in three homotopic regions of interest, which could reliably be identified based on distinct anatomical landmarks, and were part of the “Extrinsic” (externally oriented, task positive) network (pre- and postcentral gyrus, and intraparietal sulcus). Resting-state fMRI data were acquired for a group of 11 healthy subjects and 8 DOC patients. At the group level, our results indicate decreased inter-hemispheric functional connectivity in subjects with impaired awareness as compared to subjects with intact awareness. Individual connectivity scores significantly correlated with the degree of consciousness. Furthermore, a single-case statistic indicated a significant deviation from the healthy sample in 5/8 patients. Importantly, of the three patients whose connectivity indices were comparable to the healthy sample, one was diagnosed as locked-in. Taken together, our results further highlight the clinical potential of resting-state connectivity analysis and might guide the way towards a connectivity measure complementing existing DOC diagnosis.

Cortical Variability in the Sensory-Evoked Response in Autism

Previous findings have shown that individuals with autism spectrum disorder (ASD) evince greater intra-individual variability (IIV) in their sensory-evoked fMRI responses compared to typical control participants. We explore the robustness of this finding with a new sample of high-functioning adults with autism. Participants were presented with visual, somatosensory and auditory stimuli in the scanner whilst they completed a one-back task. While ASD and control participants were statistically indistinguishable with respect to behavioral responses, the new ASD group exhibited greater IIV relative to controls. We also show that the IIV was equivalent across hemispheres and remained stable over the duration of the experiment. This suggests that greater cortical IIV may be a replicable characteristic of sensory systems in autism.