INTRODUCTION A brainwide, synaptic-resolution connectivity map—a connectome—is essential for understanding how the brain generates behavior. However because of technological constraints imaging entire brains with electron microscopy (EM) and reconstructing circuits from such datasets has been challenging. To date, complete connectomes have been mapped for only three organisms, each with several hundred brain neurons: the nematode C. elegans , the larva of the sea squirt Ciona intestinalis , and of the marine annelid Platynereis dumerilii . Synapse-resolution circuit diagrams of larger brains, such as insects, fish, and mammals, have been approached by considering select subregions in isolation. However, neural computations span spatially dispersed but interconnected brain regions, and understanding any one computation requires the complete brain connectome with all its inputs and outputs. RATIONALE We therefore generated a connectome of an entire brain of a small insect, the larva of the fruit fly, Drosophila melanogaster. This animal displays a rich behavioral repertoire, including learning, value computation, and action selection, and shares homologous brain structures with adult Drosophila and larger insects. Powerful genetic tools are available for selective manipulation or recording of individual neuron types. In this tractable model system, hypotheses about the functional roles of specific neurons and circuit motifs revealed by the connectome can therefore be readily tested. RESULTS The complete synaptic-resolution connectome of the Drosophila larval brain comprises 3016 neurons and 548,000 synapses. We performed a detailed analysis of the brain circuit architecture, including connection and neuron types, network hubs, and circuit motifs. Most of the brain’s in-out hubs (73%) were postsynaptic to the learning center or presynaptic to the dopaminergic neurons that drive learning. We used graph spectral embedding to hierarchically cluster neurons based on synaptic connectivity into 93 neuron types, which were internally consistent based on other features, such as morphology and function. We developed an algorithm to track brainwide signal propagation across polysynaptic pathways and analyzed feedforward (from sensory to output) and feedback pathways, multisensory integration, and cross-hemisphere interactions. We found extensive multisensory integration throughout the brain and multiple interconnected pathways of varying depths from sensory neurons to output neurons forming a distributed processing network. The brain had a highly recurrent architecture, with 41% of neurons receiving long-range recurrent input. However, recurrence was not evenly distributed and was especially high in areas implicated in learning and action selection. Dopaminergic neurons that drive learning are amongst the most recurrent neurons in the brain. Many contralateral neurons, which projected across brain hemispheres, were in-out hubs and synapsed onto each other, facilitating extensive interhemispheric communication. We also analyzed interactions between the brain and nerve cord. We found that descending neurons targeted a small fraction of premotor elements that could play important roles in switching between locomotor states. A subset of descending neurons targeted low-order post-sensory interneurons likely modulating sensory processing. CONCLUSION The complete brain connectome of the Drosophila larva will be a lasting reference study, providing a basis for a multitude of theoretical and experimental studies of brain function. The approach and computational tools generated in this study will facilitate the analysis of future connectomes. Although the details of brain organization differ across the animal kingdom, many circuit architectures are conserved. As more brain connectomes of other organisms are mapped in the future, comparisons between them will reveal both common and therefore potentially optimal circuit architectures, as well as the idiosyncratic ones that underlie behavioral differences between organisms. Some of the architectural features observed in the Drosophila larval brain, including multilayer shortcuts and prominent nested recurrent loops, are found in state-of-the-art artificial neural networks, where they can compensate for a lack of network depth and support arbitrary, task-dependent computations. Such features could therefore increase the brain’s computational capacity, overcoming physiological constraints on the number of neurons. Future analysis of similarities and differences between brains and artificial neural networks may help in understanding brain computational principles and perhaps inspire new machine learning architectures. The connectome of the Drosophila larval brain. The morphologies of all brain neurons, reconstructed from a synapse-resolution EM volume, and the synaptic connectivity matrix of an entire brain. This connectivity information was used to hierarchically cluster all brains into 93 cell types, which were internally consistent based on morphology and known function.
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Functional connectome fingerprinting: Identifying individuals and predicting cognitive functions via autoencoder
Functional network connectivity has been widely acknowledged to characterize brain functions, which can be regarded as “brain fingerprinting” to identify an individual from a pool of subjects. Both common and unique information has been shown to exist in the connectomes across individuals. However, very little is known about whether and how this information can be used to predict the individual variability of the brain. In this paper, we propose to enhance the uniqueness of individual connectome based on an autoencoder network. Specifically, we hypothesize that the common neural activities shared across individuals may reduce the individual identification. By removing contributions from shared activities, inter‐subject variability can be enhanced. Our experimental results on HCP data show that the refined connectomes obtained by utilizing autoencoder with sparse dictionary learning can distinguish an individual from the remaining participants with high accuracy (up to 99.5% for the rest–rest pair). Furthermore, high‐level cognitive behaviors (e.g., fluid intelligence, executive function, and language comprehension) can also be better predicted with the obtained refined connectomes. We also find that high‐order association cortices contribute more to both individual discrimination and behavior prediction. In summary, our proposed framework provides a promising way to leverage functional connectivity networks for cognition and behavior study, in addition to a better understanding of brain functions.
more » « less- NSF-PAR ID:
- 10387483
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Human Brain Mapping
- Volume:
- 42
- Issue:
- 9
- ISSN:
- 1065-9471
- Page Range / eLocation ID:
- p. 2691-2705
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Brain functional connectome analysis is commonly based on population‐wise inference. However, in this way precious information provided at the individual subject level may be overlooked. Recently, several studies have shown that individual differences contribute strongly to the functional connectivity patterns. In particular, functional connectomes have been proven to offer a fingerprint measure, which can reliably identify a given individual from a pool of participants. In this work, we propose to refine the standard measure of individual functional connectomes using dictionary learning. More specifically, we rely on the assumption that each functional connectivity is dominated by stable group and individual factors. By subtracting population‐wise contributions from connectivity patterns facilitated by dictionary representation, intersubject variability should be increased within the group. We validate our approach using several types of analyses. For example, we observe that refined connectivity profiles significantly increase subject‐specific identifiability across functional magnetic resonance imaging (fMRI) session combinations. Besides, refined connectomes can also improve the prediction power for cognitive behaviors. In accordance with results from the literature, we find that individual distinctiveness is closely linked with differences in neurocognitive activity within the brain. In summary, our results indicate that individual connectivity analysis benefits from the group‐wise inferences and refined connectomes are indeed desirable for brain mapping.
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The functional connectome supports information transmission through the brain at various spatial scales, from exchange between broad cortical regions to finer-scale, vertex-wise connections that underlie specific information processing mechanisms. In adults, while both the coarse- and fine-scale functional connectomes predict cognition, the fine scale can predict up to twice the variance as the coarse-scale functional connectome. Yet, past brain-wide association studies, particularly using large developmental samples, focus on the coarse connectome to understand the neural underpinnings of individual differences in cognition. Using a large cohort of children (age 9–10 years;
n = 1,115 individuals; both sexes; 50% female, including 170 monozygotic and 219 dizygotic twin pairs and 337 unrelated individuals), we examine the reliability, heritability, and behavioral relevance of resting-state functional connectivity computed at different spatial scales. We use connectivity hyperalignment to improve access to reliable fine-scale (vertex-wise) connectivity information and compare the fine-scale connectome with the traditional parcel-wise (coarse scale) functional connectomes. Though individual differences in the fine-scale connectome are more reliable than those in the coarse-scale, they are less heritable. Further, the alignment and scale of connectomes influence their ability to predict behavior, whereby some cognitive traits are equally well predicted by both connectome scales, but other, less heritable cognitive traits are better predicted by the fine-scale connectome. Together, our findings suggest there are dissociable individual differences in information processing represented at different scales of the functional connectome which, in turn, have distinct implications for heritability and cognition. -
We propose a novel matrix autoencoder to map functional connectomes from resting state fMRI (rs-fMRI) to structural connectomes from Diffusion Tensor Imaging (DTI), as guided by subject-level phenotypic measures. Our specialized autoencoder infers a low dimensional manifold embedding for the rs-fMRI correlation matrices that mimics a canonical outer-product decomposition. The embedding is simultaneously used to reconstruct DTI tractography matrices via a second manifold alignment decoder and to predict inter-subject phenotypic variability via an artificial neural network. We validate our framework on a dataset of 275 healthy individuals from the Human Connectome Project database and on a second clinical dataset consisting of 57 subjects with Autism Spectrum Disorder. We demonstrate that the model reliably recovers structural connectivity patterns across individuals, while robustly extracting predictive and interpretable brain biomarkers in a cross-validated setting. Finally, our framework outperforms several baselines at predicting behavioral phenotypes in both real-world datasets.more » « less
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INTRODUCTION The analysis of the human brain is a central goal of neuroscience, but for methodological reasons, research has focused on model organisms, the mouse in particular. Because substantial homology was found at the level of ion channels, transcriptional programs, and basic neuronal types, a strong similarity of neuronal circuits across species has also been assumed. However, a rigorous test of the configuration of local neuronal circuitry in mouse versus human—in particular, in the gray matter of the cerebral cortex—is missing. The about 1000-fold increase in number of neurons is the most obvious evolutionary change of neuronal network properties from mouse to human. Whether the structure of the local cortical circuitry has changed as well is, however, unclear. Recent data from transcriptomic analyses has indicated an increase in the proportion of inhibitory interneurons from mouse to human. But what the effect of such a change is on the circuit configurations found in the human cerebral cortex is not known. This is, however, of particular interest also to the study of neuropsychiatric disorders because in these, the alteration of inhibitory-to-excitatory synaptic balance has been identified as one possible mechanistic underpinning. RATIONALE We used recent methodological improvements in connectomics to acquire data from one macaque and two human individuals, using biopsies of the temporal, parietal, and frontal cortex. Human tissue was obtained from neurosurgical interventions related to tumor removal, in which access path tissue was harvested that was not primarily affected by the underlying disease. A key concern in the analysis of human patient tissue has been the relation to epilepsy surgery, when the underlying disease has required often year-long treatment with pharmaceuticals, plausibly altering synaptic connectivity. Therefore, the analysis of nonepileptic surgery tissue seemed of particular importance. We also included data from one macaque individual, who was not known to have any brain-related pathology. RESULTS We acquired three-dimensional electron microscopy data from temporal and frontal cortex of human and temporal and parietal cortex of macaque. From these, we obtained connectomic reconstructions and compared these with five connectomes from mouse cortex. On the basis of these data, we were able to determine the effect of the about 2.5-fold expansion of the interneuron pool in macaque and human cortex compared with that of mouse. Contrary to expectation, the inhibitory-to-excitatory synaptic balance on pyramidal neurons in macaque and human cortex was not substantially altered. Rather, the interneuron pool was selectively expanded for bipolar-type interneurons, which prefer the innervation of other interneurons, and which further increased their preference for interneuron innervation from mouse to human. These changes were each multifold, yielding in effect an about 10-fold expanded interneuron-to-interneuron network in the human cortex that is only sparsely present in mouse. The total amount of synaptic input to pyramidal neurons, however, did not change according to the threefold thickening of the cortex; rather, a modest increase from about 12,000 synaptic inputs in mouse to about 15,000 in human was found. CONCLUSION The principal cells of the cerebral cortex, pyramidal neurons, maintain almost constant inhibitory-to-excitatory input balance and total synaptic input across 100 million years of evolutionary divergence, which is particularly noteworthy with the concomitant 1000-fold expansion of the neuronal network size and the 2.5-fold increase of inhibitory interneurons from mouse to human. Rather, the key network change from mouse to human is an expansion of almost an order of magnitude of an interneuron-to-interneuron network that is virtually absent in mouse but constitutes a substantial part of the human cortical network. Whether this new network is primarily created through the expansion of existing neuronal types, or is related to the creation of new interneuron subtypes, requires further study. The discovery of this network component in human cortex encourages detailed analysis of its function in health and disease. Connectomic screening across mammalian species: Comparison of five mouse, two macaque, and two human connectomic datasets from the cerebral cortex. ( A ) Automated reconstructions of all neurons with their cell bodies in the volume shown, using random colors. The analyzed connectomes comprised a total of ~1.6 million synapses. Arrows indicate evolutionary divergence: the last common ancestor between human and mouse, approximately 100 million years ago, and the last common ancestor between human and macaque, about 20 million years ago. ( B ) Illustration of the about 10-fold expansion of the interneuron-to-interneuron network from mouse to human.more » « less
