Comparison of male and female fly brains is unlocking the secret workings of the mind by sex

Amazing stuff! This is huge! Mind boggling!

"An international team ... has mapped every neuron in the male Drosophila fly’s brain and nerve cord, to produce a wiring map of the 166,700 neurons and the millions of connections between them. ...

Since the female Drosophila fly brain has previously been mapped, involving the same group, this new work allowed the researchers to directly compare male and female connectomes and pinpoint differences between them.

This revealed that neurons can reroute sensory information into sex-specific behavioural circuits to drive different male and female responses to the same stimuli.

For example, male flies detect the smell of another male fly and respond with aggressive behaviour, whereas female flies respond to the same smell with courtship behaviour. ...

This is the first full-scale connectome of an adult animal’s brain and nerve cord ever produced. The entire dataset now exists as a public resource, openly available as a reference and tool for other researchers. ..."

From the highlights and abstract:

"Highlights

• Whole male Drosophila CNS connectome enables sensory-to-motor circuit analysis

• First synaptic-resolution comparison of male and female brains

• Sex-specific and dimorphic neurons concentrate in higher-order, integrative centers

• Analysis of visual, auditory, olfactory, and taste pathways reveals shared principles

Summary

Sex differences in behavior exist across all animals, typically under strong genetic regulation. In Drosophila, fruitless/doublesex transcription factors identify dimorphic neurons, but their organization into functional circuits remains unclear. We present the connectome of the entire Drosophila male central nervous system. This contains 166,700 neurons spanning the brain and nerve cord, fully proofread and annotated, including fruitless/doublesex expression and 11,710 neuron types.

We provide the first comprehensive comparison between male and female brain connectomes to synaptic resolution, finding 8,069 isomorphic, 138 dimorphic, 289 male-specific, and 71 female-specific types.

This resource enables analysis of full sensory-to-motor circuits underlying complex behaviors and the impact of dimorphic elements.

Sex-specific/dimorphic neurons are concentrated in higher brain centers, while the sensory and motor periphery is largely isomorphic.

Within higher centers, male-specific connections are organized into hotspots defined by male-specific neurons or arbors. Dimorphic neurons reroute information across sexes."

Comparison of male and female fly brains is unlocking the secret workings of the mind | University of Cambridge "Scientists have produced the first full scale wiring map of a male adult animal’s brain and nerve cord, enabling unprecedented insights into how the brain works and controls behaviour."

5 amazing visuals show how the male fruit fly’s brain map is advancing neuroscience "A years-long project by HHMI Janelia, Google Research, and collaborators has built the first complete brain map for a male fruit fly, a key model organism in science."

Sexual dimorphism in the complete Drosophila male central nervous system connectome (open access)

Graphical abstract

Figure 1 A densely annotated and cross-matched male CNS connectome

Figure 2 Information flow from sensory input to motor output organizes circuits spanning the brain and nerve cord

Figure 3 Sexual dimorphism in the fly brain

Overlooked Brain Connections Hold Clues to Cognition and Mental Health

Amazing stuff!

"Key points

• Scientists who use imaging to understand the brain’s complexity often focus on the strongest signals and discard the rest.
• A new study reveals that connections routinely overlooked as “noise” during neuroimaging data analysis can predict behavior with remarkable accuracy.
• The finding could help explain why some people with psychiatric illness don’t respond to treatments, and it could identify new targets for therapeutics.

...

For the study, researchers investigated whether signals discarded by feature selection could reveal meaningful insights about brain and behavior. The team examined brain imaging and behavioral data from more than 12,000 participants across four major U.S. datasets. For every participant, the team calculated the strength of association between brain connections and the outcome they wanted to predict.

All the connections were then ranked from the strongest to weakest associated and divided into 10 non-overlapping groups.

Group one contained the top 10% of connections, those that scientists usually select, while groups two through 10 held the remaining 90% of connections—the connections often dismissed as noise. The team then built 10 prediction models, one for each group. ...

The team found that lower-ranked connections—groups two through nine—consistently achieved prediction accuracy similar to the top 10% of connections. 

In some cases, models built on lower groups of connections performed better than those trained on the top group. The authors suggest this might be because predictive information is widely distributed throughout brain connections and not just concentrated within the strongest ones. ..."

From the abstract:

"A central objective in human neuroimaging is to understand the neurobiology underlying cognition and mental health.

Machine learning models trained on neuroimaging data are increasingly used as tools for predicting behavioural phenotypes, enhancing precision medicine and improving generalizability compared with traditional MRI studies.

However, the high dimensionality of brain connectivity data makes model interpretation challenging. Prevailing practices rely on selecting features and, implicitly, interpreting identified feature networks as uniquely representative of a given phenotype while overlooking others. Despite its widespread use, how univariate feature selection balances the trade-off between simplification for optimizing modelling and oversimplification that misrepresents true neurobiology remains understudied.

Here, using four large-scale neuroimaging datasets spanning over 12,000 participants and 13 outcomes, we demonstrate that edges discarded by feature selection can achieve significant prediction accuracies while yielding different neurobiological interpretations. These results are observed across cognitive, developmental and psychiatric phenotypes, extend to both functional connectivity (functional MRI) and structural connectomes (diffusion tensor imaging) , and remain evident in external validation.

They suggest that focusing on only the top features may simplify the neurobiological bases of brain–behaviour associations. Such interpretations present only the tip of the iceberg when certain disregarded features may be just as meaningful, potentially contributing to ongoing issues surrounding reproducibility within the field. More broadly, our results reinforce that subtle brain-wide signals should not be ignored."

Overlooked Brain Connections Hold Clues to Cognition and Mental Health | Yale School of Medicine

Feature selection leads to divergent neurobiological interpretations of brain-based machine learning biomarkers (open access)

Fig. 1: CPM [connectome-based predictive modelling ] across non-overlapping decile-ranked brain connectivity features.

a, Workflow illustrating the decile-based CPM pipeline, including the initial correlation of connectivity features with phenotypic outcome, ranking features on the basis of group-level correlations between edges and phenotype, splitting features into deciles, and evaluating each decile-based model. DTI, diffusion tensor imaging.

b, Violin plot showing the predictive performance of models trained on each decile of features within the PNC dataset for executive function.

c–e, Radar plots depicting predictive performance across deciles for PNC executive function (c, left), PNC language abilities (c, right), HCPD executive function (d, left), HCPD language abilities (d, right), HBN executive function (e, left) and HBN language abilities (e, right). Bold decile numbers indicate significant predictions.

Scientists Release "Wiring Diagram" Encompassing Entire Central Nervous System of an Adult Fruit Fly

Amazing stuff!

Scientists build ‘digital twin’ of mouse brain, but only from movie clip watching

Amazing stuff! However, this is still a very humble beginning!

"... The digital twin was trained on large datasets of brain activity collected from the visual cortex of real mice as they watched movie clips. It could then predict the response of tens of thousands of neurons to new videos and images. ...

Over many short viewing sessions, the researchers recorded more than 900 minutes of brain activity from eight mice watching clips of action-packed movies, such as Mad Max. Cameras monitored their eye movements and behavior. ...

The researchers verified these predictions against high-resolution, electron microscope imaging of that mouse’s visual cortex, which was part of a larger project to map the structure and function of the mouse visual cortex in unprecedented detail. ..."

From the abstract:

"Understanding the brain requires understanding neurons’ functional responses to the circuit architecture shaping them. Here we introduce the MICrONS functional connectomics dataset with dense calcium imaging of around 75,000 neurons in primary visual cortex (VISp) and higher visual areas (VISrl, VISal and VISlm) in an awake mouse that is viewing natural and synthetic stimuli. These data are co-registered with an electron microscopy reconstruction containing more than 200,000 cells and 0.5 billion synapses. 

Proofreading of a subset of neurons yielded reconstructions that include complete dendritic trees as well the local and inter-areal axonal projections that map up to thousands of cell-to-cell connections per neuron. Released as an open-access resource, this dataset includes the tools for data retrieval and analysis. Accompanying studies describe its use for comprehensive characterization of cell types, a synaptic level connectivity diagram of a cortical column, and uncovering cell-type-specific inhibitory connectivity that can be linked to gene expression data. Functionally, we identify new computational principles of how information is integrated across visual space, characterize novel types of neuronal invariances and bring structure and function together to uncover a general principle for connectivity between excitatory neurons within and across areas."

Scientists build ‘digital twin’ of mouse brain | Stanford Report "In a new study, researchers created an AI model of the mouse visual cortex that predicts neuronal responses to visual images."

Functional connectomics spanning multiple areas of mouse visual cortex (open access)

Fig. 1: Resource data type and data products.

 Fig. 2: Major experimental steps in the data acquisition workflow.

Researchers Create First-Ever Map of a Single adult insect’s Early Visual System

Good news! Amazing stuff! Why did they choose a micro parasitic wasp for this?

"... The wasp’s brain turned out to be “very similar to bigger brains, but simpler and smaller,”  ..."

From the highlights and abstract:

"Highlights

• Megaphragma lamina connectome is similar to but simpler than that in larger insects

• Denucleation of Megaphragma neurons is class-specific, suggesting a functional role

• Cartridge connectome variation reflects specialization of the dorsal rim ommatidia

Summary

For most model organisms in neuroscience, research into visual processing in the brain is difficult because of a lack of high-resolution maps that capture complex neuronal circuitry. The microinsect Megaphragma viggianii, because of its small size and non-trivial behavior, provides a unique opportunity for tractable whole-organism connectomics. We image its whole head using serial electron microscopy. We reconstruct its compound eye and analyze the optical properties of the ommatidia as well as the connectome of the first visual neuropil—the lamina. Compared with the fruit fly and the honeybee, Megaphragma visual system is highly simplified: it has 29 ommatidia per eye and 6 lamina neuron types. We report features that are both stereotypical among most ommatidia and specialized to some. By identifying the “barebones” circuits critical for flying insects, our results will facilitate constructing computational models of visual processing in insects."

Researchers Create First-Ever Map of a Single Animal’s Early Visual System A team of scientists at the Flatiron Institute and their colleagues have mapped out the entire early visual system — from eyes to neurons — for a tiny parasitic wasp, the first time such a system has been reconstructed at the synaptic level for a single specimen.

A complete reconstruction of the early visual system of an adult insect (open access)

A specimen of Megaphragma viggianiiI, a parasitic wasp the size of some single-celled organisms such as amoebas. The tiny insect exhibits complex behaviors such as flight despite its small stature and brain, making it a useful tool for understanding how brains work.

Researchers mapped the early visual system of a tiny parasitic wasp. Each of the wasp’s 29 compound eye facets (top right) sends signals to a part of the brain called the lamina (left). The researchers found that the patterns of how synapses connect the various neurons (colored lines) reveal how different parts of the eye contribute to the wasp’s sight. This is a brain slice

Figure 1 Optics of the miniaturized compound eye

MRI imaging at 64 million higher resolution give a new look at the whole mouse brain in vivo

Amazing stuff! This level of detail is mind blowing!

"... “We can start looking at neurodegenerative diseases in an entirely different way.” ... The culmination of almost 40 years of work ... this MRI resolution was only made possible with some impressive technology. The team used a powerful 9.4-Tesla magnet (clinical MRIs generally have a 1.5-to-3-Tesla magnet), a set of gradient coils 100 times stronger than in standard scans, and a super-computer equivalent to 800 laptops, all working to capture the single mouse brain.
What’s more, after the MRI visuals were complete, the researchers had the brain tissue scanned by light sheet microscopy. This enabled the scientists to label specific groups of cells, allowing them to watch how neurodegenerative diseases progress over time. ..."

"... A single voxel of the new images – think of it as a cubic pixel – measures just 5 microns. That’s 64 million times smaller than a clinical MRI voxel. ...

One set of MRI images shows how brain-wide connectivity changes as mice age, as well as how specific regions, like the memory-involved subiculum, change more than the rest of the mouse’s brain.

Another set of images showcases a spool of rainbow-colored brain connections that highlight the remarkable deterioration of neural networks in a mouse model of Alzheimer’s disease. ..."

From the significance and abstract:

"Significance

We demonstrate the highest-resolution MR images ever obtained of the mouse brain. The diffusion tensor images (DTI) @ 15 μm spatial resolution are 1,000 times the resolution of most preclinical rodent DTI/MRI. Superresolution track density images are 27,000 times that of typical preclinical DTI/MRI. High angular resolution yielded the most detailed MR connectivity maps ever generated. High-performance computing pipelines merged the DTI with light sheet microscopy of the same specimen, providing a comprehensive picture of cells and circuits. The methods have been used to demonstrate how strain differences result in differential changes in connectivity with age. We believe the methods will have broad applicability in the study of neurodegenerative diseases.

Abstract

We have developed workflows to align 3D magnetic resonance histology (MRH) of the mouse brain with light sheet microscopy (LSM) and 3D delineations of the same specimen. We start with MRH of the brain in the skull with gradient echo and diffusion tensor imaging (DTI) at 15 μm isotropic resolution which is ~ 1,000 times higher than that of most preclinical MRI. Connectomes are generated with superresolution tract density images of ~5 μm. Brains are cleared, stained for selected proteins, and imaged by LSM at 1.8 μm/pixel. LSM data are registered into the reference MRH space with labels derived from the ABA common coordinate framework. The result is a high-dimensional integrated volume with registration (HiDiver) with alignment precision better than 50 µm. Throughput is sufficiently high that HiDiver is being used in quantitative studies of the impact of gene variants and aging on mouse brain cytoarchitecture and connectomics."

Scans that are 64 million times clearer give a new look at the brain Fifty years on from American chemist Pal Laterbur detailing the first magnetic resonance imaging (MRI), scientists have marked this historic medical anniversary with the sharpest-ever scans of a mouse brain.

Brain Images Just Got 64 Million Times Sharper MRI technology from Duke-led effort reveals the entire mouse brain in the highest resolution

Merged magnetic resonance and light sheet microscopy of the whole mouse brain (no public access) 

Scientists complete first map of an larval insect brain with just over 3,000 neurons with 93 neuron types

Amazing stuff! Very exciting! However, do insects and humans think alike?

"Researchers have completed the most advanced brain map to date, that of an insect—a landmark achievement in neuroscience that brings scientists closer to true understanding of the mechanism of thought.
The international team led by Johns Hopkins University and the University of Cambridge produced a breathtakingly detailed diagram tracing every neural connection in the brain of a larval fruit fly, an archetypal scientific model with brains comparable to humans. ..."

From the abstract:

"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."

Scientists complete first map of an insect brain | Hub In the quest to understand how we think, 'everything has been working up to this,' says biomedical engineer Joshua T. Vogelstein, part of the international team led by Johns Hopkins University and the University of Cambridge

The connectome of an insect brain (no public access)

The connectome of the Drosophila larval brain.

Connectomics

Recommendable! Short and sweet! 

New Brain Maps Can Predict Behaviors

Very recommendable! Amazing stuff! Comprehensive review and critique of connectome research. Learn that often dispassionate scientists can be voyeurs too. :-)

In some sense connectomics is a rather primitive approach to understand the functioning of the brain. E.g. it does not tell us much about the chemical communication between neurons etc.

"Last summer a group of Harvard University neuroscientists and Google engineers released the first wiring diagram of a piece of the human brain. The tissue, about the size of a pinhead, had been preserved, stained with heavy metals, cut into 5,000 slices and imaged under an electron microscope. This cubic millimeter of tissue accounts for only one-millionth of the entire human brain. Yet the vast trove of data depicting it comprises 1.4 petabytes [data]’ worth of brightly colored microscopy images of nerve cells, blood vessels and more. ...
including new types of cells never seen in other animals, such as neurons with axons that curl up and spiral atop each other and neurons with two axons instead of one. These findings just scratched the surface ...
In fact, the only species for which there is yet a comprehensive connectome is Caenorhabditis elegans, the humble roundworm.
Nevertheless, the masses of connectome data that scientists have amassed from worms, flies, mice and humans are already having a potent effect on neuroscience. ...
Recent work with C. elegans has demonstrated the power of large-scale connectomics. One experiment showed that it’s sometimes possible for scientists to predict the behavior of an animal from a knowledge of its connectome; another hinted at rules governing the linkage of neurons into working circuits. Those successes, however, also underscore how far large-scale connectomics still needs to go before it can tackle much more complex creatures. ...
About 35 years ago, the first full-brain wiring diagram was completed for the roundworm. For its time, the effort was heroic, even though the animal has only 302 neurons in its brain. It was carried out through the painstaking process of hand-drawing neuronal connections on printouts of electron microscopy images. It took more than 15 years to complete. ...
This approach is leading to impressive progress in understanding these animals. In a report in Cell published in September, scientists used the worm connectome to describe one of the most complex behaviors in the natural world: sex. Using video and calcium imaging — which measures and traces activity in brain cells — they recorded C. elegans during the act of mating. Videos showed the worms slithering around one another in serpentine patterns while white light from fluorescent proteins indicating neuronal activity flickered on and off along the length of their slender bodies. ...

For example, it has been known for some time that in C. elegans, the connections between neurons dramatically reorganize themselves between birth and adulthood. To understand how the brain changes throughout development, in a recent paper in Nature ... compared the connectomes of eight genetically identical roundworms ranging between larval and adult stages.

The most interesting finding of the study ... was that even though the worms were genetically identical, as much as 40% of the connections between nerve cells in their brains differed. Moreover, the connections that varied between individuals were weaker than those that were similar. Stronger connections that contained 100 synapses or more were consistent across multiple organisms.

... this finding points to the power of brain-map comparisons in bulk ... because each connectome is slightly different,” ... finding points to the existence of two classes of connections: variable ones and consistent ones. ...
When ... mapped the snippet of human brain, for example, they had no idea whether the strange things they saw were normal or one-offs due to the unique history and genetic makeup of the person. If they could map equivalent samples from 100 human brains, then they would get some clarity on these unknowns, but at 1.4 petabytes per brain, that is unlikely to happen anytime soon. ...
Nevertheless, connectomics is making important progress even where it can’t yet be large scale and where only partial connectomes exist. Work in the fruit fly, Drosophila melanogaster, is particularly far along, both in the larva (which has about 10,000 neurons) and in the adult (with about 135,000 neurons). Last year, researchers ... released a synapse-level “hemi-brain” connectome that mapped many important control centers in the fly’s brain. This led to an important announcement in October, when neuroscientists uncovered dozens of new neuron types and circuits that seem to aid in fly navigation. ...

What Connections Can’t Do

The successes of connectomics can be bittersweet. For many years, a central criticism of connectomics has been that it is insufficient to explain how the brain functions. ...
Another limitation of the connectome is that it doesn’t tell us anything about the quality of the connections: whether they are strong or weak. It simply tells us that there is a connection. ... neurons make thousands of connections with other neurons in vast networks full of redundancies and pathways with overlapping functions. ... Connectomics also tells us almost nothing about the brain chemicals called neuromodulators, which circulate through the fluid surrounding neurons, unlike the neurotransmitter chemicals released precisely within the synaptic connections between neurons. They represent another way that cells in the brain communicate with one another. ..."

From the abstract:

"We acquired a rapidly preserved human surgical sample from the temporal lobe of the cerebral cortex. We stained a 1 mm3 volume with heavy metals, embedded it in resin, cut more than 5000 slices at ∼30 nm and imaged these sections using a high-speed multibeam scanning electron microscope. We used computational methods to render the three-dimensional structure containing 57,216 cells, hundreds of millions of neurites and 133.7 million synaptic connections. The 1.4 petabyte electron microscopy volume, the segmented cells, cell parts, blood vessels, myelin, inhibitory and excitatory synapses, and 104 manually proofread cells are available to peruse online. Many interesting and unusual features were evident in this dataset. Glia outnumbered neurons 2:1 and oligodendrocytes were the most common cell type in the volume. Excitatory spiny neurons comprised 69% of the neuronal population, and excitatory synapses also were in the majority (76%). The synaptic drive onto spiny neurons was biased more strongly toward excitation (70%) than was the case for inhibitory interneurons (48%). Despite incompleteness of the automated segmentation caused by split and merge errors, we could automatically generate (and then validate) connections between most of the excitatory and inhibitory neuron types both within and between layers. In studying these neurons we found that deep layer excitatory cell types can be classified into new subsets, based on structural and connectivity differences, and that chandelier interneurons not only innervate excitatory neuron initial segments as previously described, but also each other’s initial segments. Furthermore, among the thousands of weak connections established on each neuron, there exist rarer highly powerful axonal inputs that establish multi-synaptic contacts (up to ∼20 synapses) with target neurons. Our analysis indicates that these strong inputs are specific, and allow small numbers of axons to have an outsized role in the activity of some of their postsynaptic partners."

New Brain Maps Can Predict Behaviors | Quanta Magazine Rapid advances in large-scale connectomics are beginning to spotlight the importance of individual variations in the neural circuitry. They also highlight the limitations of “wiring diagrams” alone.

A connectomic study of a petascale fragment of human cerebral cortex (open access)

Why This Lab Is Slicing Human Brains Into Little Pieces

Amazing stuff! Observe hundreds if not thousands of zebra fish from day 5-8 in their lives, because their brains are translucent!

The extended product placement for a certain brand of sunglasses was annoying!

What is the challenge?