Bratislav Misic

does brain connectivity drive spread of pathological proteins in Alzheimer’s disease?

✨ preprint: www.biorxiv.org/content/10.1...

if the question intrigues you, please read on 🧵⤵️

We are delighted to announce the launch of a new Master's Programme in Brain Health and Disease within the Department of Clinical Neurosciences at the University of Cambridge.

Applications are now open!

@camneurodept.bsky.social

www.clinical-neuroscience.cam.ac.uk/education/ta...

Excited to share that our work introducing the Reproducible Brain Charts (RBC) data resource is now published in Neuron!! 🎉

📚 Read the paper: authors.elsevier.com/c/1lpaF3BtfH...
🧠 Explore the RBC dataset: reprobrainchart.github.io

Interested in the mechanisms shaping the extraordinary complexity of the connectome?

Then check out our new preprint, lead by
Francis Normand with a stellar team, showing how geometry constrains connectome architecture:

biorxiv.org/content/10.1...

Full thread here:
tinyurl.com/sfv3yf73

Neuromorphic hierarchical modular reservoirs | doi.org/10.1101/2025...

How does hierarchical modularity shape computational function? ⤵️

Connectome-based reservoir computing with the conn2res toolbox - Nature Communications Brain connectivity patterns shape computational capacity of biological neural networks, however mapping empirically measured connectivity to artificial networks remains challenging. The authors presen...

8️⃣ And if you want more, we also have a connectome-based reservoir computing toolbox (conn2res) and mini-review:

www.nature.com/articles/s41...

GitHub - netneurolab/milisav_hierarchical_modularity: Data and code supporting Milisav et al., 2025 "Neuromorphic hierarchical modular reservoirs" Data and code supporting Milisav et al., 2025 "Neuromorphic hierarchical modular reservoirs" - netneurolab/milisav_hierarchical_modularity

7️⃣ Altogether, across multiple benchmarks, we show that hierarchical modularity endows networks with computationally advantageous properties, providing insight into the relationship between neural network structure and function.

Code: github.com/netneurolab/...

6️⃣ So far, we considered considered synthetic graphs, but what about real brains?

We implement dMRI brain networks as reservoirs. Again, hierarchical modularity positively contributes to computational performance.

Amazingly, reservoir timescales correlate with empirical timescales derived from MEG.

5️⃣ How well do these networks perform multiple tasks simultaneously? We assign memory tasks to half the modules, and non-linear transformation tasks to the other half.

Again, we find that higher-order hierarchical modular networks consistently outperform their lower-order counterparts.

4️⃣ To uncover the topological underpinnings of these differences in dynamics, we consider the motif composition of the reservoir.

More complex motifs containing at least three edges are all enriched in higher-order hierarchical modular networks, supporting more complex computations.

3️⃣ How does hierarchical modularity shape dynamics to improve memory? We compute timescales from nodal time series at criticality.

Higher-order hierarchical modular reservoirs show more variability in timescales, yielding a bigger pool of timescales and richer temporal expansion of input signals.

2️⃣ We start by evaluating the reservoir’s ability to preserve representations of past stimuli with the widely used memory capacity task.

Higher-order hierarchical modular networks consistently perform best, particularly at criticality.

1️⃣ We use stochastic block models to generate synthetic multi-level hierarchical modular networks.

We then implement them as reservoirs to evaluate their cognitive capacity.

Neuromorphic hierarchical modular reservoirs | doi.org/10.1101/2025...

How does hierarchical modularity shape computational function? ⤵️

5️⃣

GitHub - netneurolab/Farahani_ALS: Network spreading and local biological vulnerability in amyotrophic lateral sclerosis Network spreading and local biological vulnerability in amyotrophic lateral sclerosis - netneurolab/Farahani_ALS

Huge thanks to Sanjay Kalra and the CALSNIC team, J Hansen, @vincebaz.bsky.social @goliashf.bsky.social L Collins @dadarmahsa.bsky.social @alaindagher.bsky.social !!

code: github.com/netneurolab/...

8️⃣ Finally, we consider spinal- and bulbar-onset subtypes. Epicenters in spinal-onset are mainly in primary motor cortex and paracentral lobule. In bulbar-onset, epicenters are prominent in lower paracentral gyrus and inferior frontal gyrus, aligning with the clinical presentation of the subtypes.

7️⃣ If cortical epicenters reflect the spatial focus of ALS pathology, do they also correlate with the clinical manifestation? Indeed: epicenter maps are correlated with poor motor function, including abnormal index finger and foot tapping scores, daily physical function, and muscle tone.

6️⃣ We next ask whether the network epicenters of ALS atrophy are enriched for specific biological processes, cellular components, and cell types.

5️⃣ We next investigate whether spreading is more likely between regions that share biological features, including (1) gene expression, (2) neurotransmitter receptors, (3) laminar differentiation, (4) metabolism, and (5) hemodynamics.

4️⃣ We apply two methods to back-reconstruct the spreading trajectory and infer the most likely cortical location of the epicenter: (1) a network-based node ranking method, and (2) a susceptible-infected-removed (SIR) dynamical model.

Epicenter rankings are consistent with ALS pathological staging.

3️⃣ We next assess the extent to which the spatial patterning of atrophy is related to structural connectivity.

Regional atrophy is correlated with the mean atrophy of its structurally connected neighbours, consistent with the notion of network spread of pathology.

2️⃣ We analyze the Canadian ALS Neuroimaging Consortium (CALSNIC) dataset. Atrophy is concentrated in pre-central gyrus, as well as bilateral corticospinal tracts, bilateral anterior thalamic radiation, and bilateral superior longitudinal fasciculus bundles.

1️⃣ Accounts of ALS revolve around two notions: network spreading of pathogenic proteins via synapses, and intrinsic local vulnerability of specific cells.

Both may be true: pathogenic spread via synaptic contacts is amplified by local vulnerability, guiding the network spread of atrophy.

Network spreading and local biological vulnerability in amyotrophic lateral sclerosis |
doi.org/10.1038/s420...

How do brain network structure and local biological features shape the spatial patterning of atrophy in ALS? @asafarahani.bsky.social investigates ⤵️

🚨 Excited to share the latest preprint form the lab ➡️https://tinyurl.com/32d3be9f

Here we tackle a long-standing chicken-or-egg 🐣🥚question in #autism and developmental neuroscience

➡️ Is excitation–inhibition (E:I) imbalance a "cause" or a "consequence" of #autism?

Check out what we found!
🧵1/n

Our recent work on spin tests is now published in Imaging Neuroscience🎉

As always, the code to reproduce our results and figures is available on github: github.com/netneurolab/...

4️⃣ As an aside, we also take a deep dive into how spherical projections distort local spatial autocorrelation.

3️⃣ Finally, we show how targeted removal of high-error surrogate maps can be used to reduce the false positive rates.

2️⃣ We show that these errors directly result in greater false positive rates.