Danilo Bzdok

Why AI Models Hallucinate and How to Fix Them

A tab representation of the Pre trained CLIP Vision Transformer

October 8, 2025

Praneet Suresh

Jack Stanley

Danilo Bzdok

Sonia Joseph

Luca Scimeca

Using LLMs to better understand autism diagnosis

March 25, 2025

Jack Stanley

Danilo Bzdok

Towards Precision Medicine: Understanding Inference and Prediction Divergence in Biomedicine

September 8, 2020

Danilo Bzdok

Publications

Patterns of autism symptoms: hidden structure in the ADOS and ADI-R instruments

Jeremy Lefort-Besnard

Kai Vogeley

Leonhard Schilbach

Gael Varoquaux

Bertrand Thirion

Guillaume Dumas

2020-07-29

Translational Psychiatry (published)

Molecular signatures of cognition and affect

Justine Y. Hansen

Ross D. Markello

Jacob W. Vogel

Jakob Seidlitz

Bratislav Misic

Regulation of gene expression drives protein interactions that govern synaptic wiring and neuronal activity. The resulting coordinated activ… (see more)ity among neuronal populations supports complex psychological processes, yet how gene expression shapes cognition and emotion remains unknown. Here we directly bridge the microscale and macroscale by mapping gene expression patterns to functional activation patterns across the cortical sheet. Applying unsupervised learning to the Allen Human Brain Atlas and Neurosynth databases, we identify a ventromedial-dorsolateral gradient of gene assemblies that separate affective and cognitive domains. This topographic molecular-psychological signature reflects the hierarchical organization of the neocortex, including systematic variations in cell type, myeloarchitecture, laminar differentiation, and intrinsic network affiliation. In addition, this molecular-psychological signature is related to individual differences in cognitive performance, strengthens over neurodevelopment, and can be replicated in two independent repositories. Collectively, our results reveal spatially covarying transcriptomic and cognitive architectures, highlighting the influence that molecular mechanisms exert on psychological processes.

2020-07-15

bioRxiv (preprint)

Bringing proportional recovery into proportion: Bayesian modelling of post-stroke motor impairment

Anna K Bonkhoff

Thomas Hope

Adrian G Guggisberg

Rachel L Hawe

Sean P Dukelow

Anne K Rehme

Gereon R Fink

Christian Grefkes

Howard Bowman

Accurate predictions of motor impairment after stroke are of cardinal importance for the patient, clinician, and healthcare system. More tha… (see more)n 10 years ago, the proportional recovery rule was introduced by promising that high-fidelity predictions of recovery following stroke were based only on the initially lost motor function, at least for a specific fraction of patients. However, emerging evidence suggests that this recovery rule is subject to various confounds and may apply less universally than previously assumed. Here, we systematically revisited stroke outcome predictions by applying strategies to avoid confounds and fitting hierarchical Bayesian models. We jointly analysed 385 post-stroke trajectories from six separate studies—one of the largest overall datasets of upper limb motor recovery. We addressed confounding ceiling effects by introducing a subset approach and ensured correct model estimation through synthetic data simulations. Subsequently, we used model comparisons to assess the underlying nature of recovery within our empirical recovery data. The first model comparison, relying on the conventional fraction of patients called ‘fitters’, pointed to a combination of proportional to lost function and constant recovery. ‘Proportional to lost’ here describes the original notion of proportionality, indicating greater recovery in case of a more severe initial impairment. This combination explained only 32% of the variance in recovery, which is in stark contrast to previous reports of >80%. When instead analysing the complete spectrum of subjects, ‘fitters’ and ‘non-fitters’, a combination of proportional to spared function and constant recovery was favoured, implying a more significant improvement in case of more preserved function. Explained variance was at 53%. Therefore, our quantitative findings suggest that motor recovery post-stroke may exhibit some characteristics of proportionality. However, the variance explained was substantially reduced compared to what has previously been reported. This finding motivates future research moving beyond solely behaviour scores to explain stroke recovery and establish robust and discriminating single-subject predictions.

2020-06-28

Brain : a journal of neurology (published)

Shared and unique brain network features predict cognition, personality and mental health in childhood

Jianzhong Chen

Angela Tam

Valeria Kebets

Csaba Orbán

Leon Qi Rong Ooi

Scott Marek

Nico U.F. Dosenbach

Simon B. Eickhoff

Avram J. Holmes

B.T. Thomas Yeo

The manner through which individual differences in brain network organization track population-level behavioral variability is a fundamental… (see more) question in systems neuroscience. Recent work suggests that resting-state and task-state functional connectivity can predict specific traits at the individual level. However, the focus of most studies on single behavioral traits has come at the expense of capturing broader relationships across behaviors. Here, we utilized a large-scale dataset of 1858 typically developing children to estimate whole-brain functional network organization that is predictive of individual differences in cognition, impulsivity-related personality, and mental health during rest and task states. Predictive network features were distinct across the broad behavioral domains: cognition, personality and mental health. On the other hand, traits within each behavioral domain were predicted by highly similar network features. This is surprising given decades of research emphasizing that distinct brain networks support different mental processes. Although tasks are known to modulate the functional connectome, we found that predictive network features were similar between resting and task states. Overall, our findings reveal shared brain network features that account for individual variation within broad domains of behavior in childhood, yet are unique to different behavioral domains.

2020-06-23

bioRxiv (preprint)

Dark control: The default mode network as a reinforcement learning agent

Elvis Dohmatob

Guillaume Dumas

The default mode network (DMN) is believed to subserve the baseline mental activity in humans. Its higher energy consumption compared to oth… (see more)er brain networks and its intimate coupling with conscious awareness are both pointing to an unknown overarching function. Many research streams speak in favor of an evolutionarily adaptive role in envisioning experience to anticipate the future. In the present work, we propose a process model that tries to explain how the DMN may implement continuous evaluation and prediction of the environment to guide behavior. The main purpose of DMN activity, we argue, may be described by Markov decision processes that optimize action policies via value estimates through vicarious trial and error. Our formal perspective on DMN function naturally accommodates as special cases previous interpretations based on (a) predictive coding, (b) semantic associations, and (c) a sentinel role. Moreover, this process model for the neural optimization of complex behavior in the DMN offers parsimonious explanations for recent experimental findings in animals and humans.

2020-06-04

Human Brain Mapping (unknown)

The Neurobiology of Social Distance

Robin I. M. Dunbar

2020-06-02

Trends in Cognitive Sciences (published)

Population variability in social brain morphology for social support, household size and friendship satisfaction

Arezoo Taebi

Hannah Kiesow

Kai Vogeley

Leonhard Schilbach

Boris C. Bernhardt

The social brain hypothesis proposes that the complexity of human brains has coevolved with increasing complexity of social interactions in … (see more)primate societies. The present study explored the possible relationships between brain morphology and the richness of more intimate ‘inner’ and wider ‘outer’ social circles by integrating Bayesian hierarchical modeling with a large cohort sample from the UK Biobank resource (n = 10 000). In this way, we examined population volume effects in 36 regions of the ‘social brain’, ranging from lower sensory to higher associative cortices. We observed strong volume effects in the visual sensory network for the group of individuals with satisfying friendships. Further, the limbic network displayed several brain regions with substantial volume variations in individuals with a lack of social support. Our population neuroscience approach thus showed that distinct networks of the social brain show different patterns of volume variations linked to the examined social indices.

2020-05-31

Social Cognitive and Affective Neuroscience (published)

CNN to detect differences in cerebral cortical anatomy of left- and right- handers

Lisa Meyer-Baese

Erik Roecher

Lucas Moesch

Klaus Mathiak

Handedness is one of the most obvious functional asymmetries, but its relation to anatomical asymmetry in the brain has not yet been clearly… (see more) demonstrated. However, there is no significant evidence to prove or disprove this structure-function correlation, thus left-handed patients are often excluded from magnetic resonance imaging (MRI) studies. MRI classification of left and right hemispheres is a difficult task on its own due to the complexity of the images and the structural similarities between the two halves. We demonstrate a deep artificial neural network approach in connection with a detailed preprocessing pipeline for the classification of lateralization in T1-weighted MR images of the human brain. Preprocessing includes bias field correction and registration on the MNI template. Our classifier is a convolutional neural network (CNN) that was trained on 287 images. Each image was duplicated and mirrored on the mid-sagittal plane. The best model reached an accuracy of 97.594% with a mean of 95.42% and standard deviation of 1.37%. Additionally, our model’s performance was evaluated on an independent set of 118 images and reached a classification accuracy of 97%. In a larger study we tested the model on grey-matter images of 927 left and 927 right-handed patients from the UK Biobank. Here all right-handed images and all left-handed images were classified as belonging to one class. The results suggest that there is no structural difference in grey-matter between the two hemispheres that can be distinguished by the deep learning classifier.

2020-05-19

Defense + Commercial Sensing (published)

General Principles of Gene Dosage Effects on Brain Structure

Claudia Modenato

Kuldeep Kumar

Clara A. Moreau

Catherine Schramm

Guillaume Huguet

Sandra Martin-Brevet

Aurélie Pain

Anne M. Maillard

Sonia Richetin

Borja Rodriguez-Herreros

Lester Melie-Garcia

Ana Dos Santos Silva

Marianne B.M. van den Bree

David E.J. Linden

Carrie E. Bearden

Sarah Lippé

Mallar Chakravarty

Bogdan Draganski

Sébastien Jacquemont

2020-04-30

Biological Psychiatry (published)

What Can Machine Learning Do for Psychiatry?

Daniel S. Barron

John H. Krystal

R. Todd Constable

2020-04-30

Biological Psychiatry (published)

Autism spectrum heterogeneity: fact or artifact?

Laurent Mottron

2020-04-29

Molecular Psychiatry (published)

Meta‐analytic evidence for a joint neural mechanism underlying response inhibition and state anger

Andrei A. Puiu

Olga Wudarczyk

Gregor Kohls

Beate Herpertz‐Dahlmann

Kerstin Konrad

Although anger may weaken response inhibition (RI) by allowing outbursts to bypass deliberate processing, it is equally likely that RI defic… (see more)its precipitate a state of anger (SA). In adolescents, for instance, anger occurs more frequently and often leads to escalating aggressive behaviors. Even though RI is considered a key component in explaining individual differences in SA expression, the neural overlap between SA and RI remains elusive. Here, we aimed to meta‐analytically revisit and update the neural correlates of motor RI, to determine a consistent neural architecture of SA, and to identify their joint neural network. Considering that inhibitory abilities follow a protracted maturation until early adulthood, we additionally computed RI meta‐analyses in youths and adults. Using activation likelihood estimation, we calculated twelve meta‐analyses across 157 RI and 39 SA experiments on healthy individuals. Consistent with previous findings, RI was associated with a broad frontoparietal network including the anterior insula/inferior frontal gyrus (aI/IFG), premotor and midcingulate cortices, extending into right temporoparietal areas. Youths showed convergent activity in right midcingulate and medial prefrontal areas, left aI/IFG, and the temporal poles. SA, on the other hand, reliably recruited the right aI/IFG and anterior cingulate cortex. Conjunction analyses between RI and SA yielded a single convergence cluster in the right aI/IFG. While frontoparietal networks and bilateral aI are ubiquitously recruited during RI, the right aI/IFG cluster likely represents a node in a dynamically‐adjusting monitoring network that integrates salient information thereby facilitating the execution of goal‐directed behaviors under highly unpredictable scenarios.

2020-04-20

Human Brain Mapping (published)