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Article ; Online: Audiovisual adaptation is expressed in spatial and decisional codes.

Aller, Máté / Mihalik, Agoston / Noppeney, Uta

2022 Volume 13, Issue 1, Page(s) 3924

Abstract: The brain adapts dynamically to the changing sensory statistics of its environment. Recent research has started to delineate the neural circuitries and representations that support this cross-sensory plasticity. Combining psychophysics and model-based ... ...

Abstract	The brain adapts dynamically to the changing sensory statistics of its environment. Recent research has started to delineate the neural circuitries and representations that support this cross-sensory plasticity. Combining psychophysics and model-based representational fMRI and EEG we characterized how the adult human brain adapts to misaligned audiovisual signals. We show that audiovisual adaptation is associated with changes in regional BOLD-responses and fine-scale activity patterns in a widespread network from Heschl's gyrus to dorsolateral prefrontal cortices. Audiovisual recalibration relies on distinct spatial and decisional codes that are expressed with opposite gradients and time courses across the auditory processing hierarchy. Early activity patterns in auditory cortices encode sounds in a continuous space that flexibly adapts to misaligned visual inputs. Later activity patterns in frontoparietal cortices code decisional uncertainty consistent with these spatial transformations. Our findings suggest that regions within the auditory processing hierarchy multiplex spatial and decisional codes to adapt flexibly to the changing sensory statistics in the environment.
MeSH term(s)	Acoustic Stimulation ; Adult ; Auditory Cortex/physiology ; Auditory Perception/physiology ; Brain Mapping ; Humans ; Magnetic Resonance Imaging ; Photic Stimulation ; Psychophysics ; Visual Perception/physiology
Language	English
Publishing date	2022-07-07
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2553671-0
ISSN	2041-1723 ; 2041-1723
ISSN (online)	2041-1723
ISSN	2041-1723
DOI	10.1038/s41467-022-31549-0
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Article ; Online: Causal Inference in Audiovisual Perception.

Mihalik, Agoston / Noppeney, Uta

The Journal of neuroscience : the official journal of the Society for Neuroscience

2020 Volume 40, Issue 34, Page(s) 6600–6612

Abstract: In our natural environment the senses are continuously flooded with a myriad of signals. To form a coherent representation of the world, the brain needs to integrate sensory signals arising from a common cause and segregate signals coming from separate ... ...

Abstract	In our natural environment the senses are continuously flooded with a myriad of signals. To form a coherent representation of the world, the brain needs to integrate sensory signals arising from a common cause and segregate signals coming from separate causes. An unresolved question is how the brain solves this binding or causal inference problem and determines the causal structure of the sensory signals. In this functional magnetic resonance imaging (fMRI) study human observers (female and male) were presented with synchronous auditory and visual signals at the same location (i.e., common cause) or different locations (i.e., separate causes). On each trial, observers decided whether signals come from common or separate sources(i.e., "causal decisions"). To dissociate participants' causal inference from the spatial correspondence cues we adjusted the audiovisual disparity of the signals individually for each participant to threshold accuracy. Multivariate fMRI pattern analysis revealed the lateral prefrontal cortex as the only region that encodes predominantly the outcome of observers' causal inference (i.e., common vs separate causes). By contrast, the frontal eye field (FEF) and the intraparietal sulcus (IPS0-4) form a circuitry that concurrently encodes spatial (auditory and visual stimulus locations), decisional (causal inference), and motor response dimensions. These results suggest that the lateral prefrontal cortex plays a key role in inferring and making explicit decisions about the causal structure that generates sensory signals in our environment. By contrast, informed by observers' inferred causal structure, the FEF-IPS circuitry integrates auditory and visual spatial signals into representations that guide motor responses.
MeSH term(s)	Acoustic Stimulation ; Adolescent ; Adult ; Auditory Perception/physiology ; Brain/physiology ; Brain Mapping ; Discrimination, Psychological/physiology ; Female ; Frontal Lobe/physiology ; Humans ; Magnetic Resonance Imaging ; Male ; Multivariate Analysis ; Parietal Lobe/physiology ; Photic Stimulation ; Prefrontal Cortex/physiology ; Psychophysics ; Sound Localization/physiology ; Visual Perception/physiology ; Young Adult
Language	English
Publishing date	2020-07-15
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	604637-x
ISSN	1529-2401 ; 0270-6474
ISSN (online)	1529-2401
ISSN	0270-6474
DOI	10.1523/JNEUROSCI.0051-20.2020
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Article ; Online: Connectome-based reservoir computing with the conn2res toolbox.

Suárez, Laura E / Mihalik, Agoston / Milisav, Filip / Marshall, Kenji / Li, Mingze / Vértes, Petra E / Lajoie, Guillaume / Misic, Bratislav

Nature communications

2024 Volume 15, Issue 1, Page(s) 656

Abstract: The connection patterns of neural circuits form a complex network. How signaling in these circuits manifests as complex cognition and adaptive behaviour remains the central question in neuroscience. Concomitant advances in connectomics and artificial ... ...

Abstract	The connection patterns of neural circuits form a complex network. How signaling in these circuits manifests as complex cognition and adaptive behaviour remains the central question in neuroscience. Concomitant advances in connectomics and artificial intelligence open fundamentally new opportunities to understand how connection patterns shape computational capacity in biological brain networks. Reservoir computing is a versatile paradigm that uses high-dimensional, nonlinear dynamical systems to perform computations and approximate cognitive functions. Here we present conn2res: an open-source Python toolbox for implementing biological neural networks as artificial neural networks. conn2res is modular, allowing arbitrary network architecture and dynamics to be imposed. The toolbox allows researchers to input connectomes reconstructed using multiple techniques, from tract tracing to noninvasive diffusion imaging, and to impose multiple dynamical systems, from spiking neurons to memristive dynamics. The versatility of the conn2res toolbox allows us to ask new questions at the confluence of neuroscience and artificial intelligence. By reconceptualizing function as computation, conn2res sets the stage for a more mechanistic understanding of structure-function relationships in brain networks.
MeSH term(s)	Artificial Intelligence ; Connectome ; Adaptation, Psychological ; Brain/diagnostic imaging ; Cognition
Language	English
Publishing date	2024-01-22
Publishing country	England
Document type	Journal Article
ZDB-ID	2553671-0
ISSN	2041-1723 ; 2041-1723
ISSN (online)	2041-1723
ISSN	2041-1723
DOI	10.1038/s41467-024-44900-4
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Article ; Online: Voxel-wise multivariate analysis of brain-psychosocial associations in adolescents reveals six latent dimensions of cognition and psychopathology.

Adams, Rick A / Zor, Cemre / Mihalik, Agoston / Tsirlis, Konstantinos / Brudfors, Mikael / Chapman, James / Ashburner, John / Paulus, Martin P / Mourão-Miranda, Janaina

Biological psychiatry. Cognitive neuroscience and neuroimaging

2024

Abstract: Background: Adolescence heralds the onset of much psychopathology, which may be conceptualized as an emergence of altered covariation between symptoms and brain measures. Multivariate methods can detect such modes of covariation or latent dimensions, ... ...

Abstract	Background: Adolescence heralds the onset of much psychopathology, which may be conceptualized as an emergence of altered covariation between symptoms and brain measures. Multivariate methods can detect such modes of covariation or latent dimensions, but none specifically relating to psychopathology have yet been found using population-level structural brain data. Using voxel-wise (instead of parcellated) brain data may strengthen latent dimensions' brain-psychosocial relationships, but this creates computational challenges. Methods: We obtained voxel-wise grey matter density and psychosocial variables from the baseline (aged 9-10 years) Adolescent Brain and Cognitive Development cohort (n=11288), and employed a state-of-the-art segmentation method, sparse partial least squares, and a rigorous machine learning framework to prevent overfitting. Results: We found six latent dimensions, four pertaining specifically to mental health. The mental health dimensions related to overeating, anorexia/internalizing, oppositional symptoms (all p<0.002) and ADHD symptoms (p=0.03). ADHD related to increased and internalizing related to decreased grey matter density in dopaminergic and serotonergic midbrain areas, whereas oppositional symptoms related to increased grey matter in a noradrenergic nucleus. Internalizing related to increased and oppositional symptoms to reduced grey matter density in insula, cingulate and auditory cortices. Striatal regions featured strongly, with reduced caudate nucleus grey matter in ADHD, and reduced putamen grey matter in oppositional/conduct problems. Voxel-wise grey matter density generated stronger brain-psychosocial correlations than brain parcellations. Conclusions: Voxel-wise brain data strengthen latent dimensions of brain-psychosocial covariation and sparse multivariate methods increase their psychopathological specificity. Internalizing and externalizing are associated with opposite grey matter changes in similar cortical and subcortical areas.
Language	English
Publishing date	2024-04-06
Publishing country	United States
Document type	Journal Article
ZDB-ID	2879089-3
ISSN	2451-9030 ; 2451-9022
ISSN (online)	2451-9030
ISSN	2451-9022
DOI	10.1016/j.bpsc.2024.03.006
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Article ; Online: Canonical Correlation Analysis for Identifying Biotypes of Depression.

Mihalik, Agoston / Adams, Rick A / Huys, Quentin

Biological psychiatry. Cognitive neuroscience and neuroimaging

2020 Volume 5, Issue 5, Page(s) 478–480

MeSH term(s)	Depression/diagnosis ; Humans
Language	English
Publishing date	2020-02-13
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't ; Comment
ZDB-ID	2879089-3
ISSN	2451-9030 ; 2451-9022
ISSN (online)	2451-9030
ISSN	2451-9022
DOI	10.1016/j.bpsc.2020.02.002
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Article ; Online: A hierarchical Bayesian model to find brain-behaviour associations in incomplete data sets.

Ferreira, Fabio S / Mihalik, Agoston / Adams, Rick A / Ashburner, John / Mourao-Miranda, Janaina

NeuroImage

2021 Volume 249, Page(s) 118854

Abstract: Canonical Correlation Analysis (CCA) and its regularised versions have been widely used in the neuroimaging community to uncover multivariate associations between two data modalities (e.g., brain imaging and behaviour). However, these methods have ... ...

Abstract	Canonical Correlation Analysis (CCA) and its regularised versions have been widely used in the neuroimaging community to uncover multivariate associations between two data modalities (e.g., brain imaging and behaviour). However, these methods have inherent limitations: (1) statistical inferences about the associations are often not robust; (2) the associations within each data modality are not modelled; (3) missing values need to be imputed or removed. Group Factor Analysis (GFA) is a hierarchical model that addresses the first two limitations by providing Bayesian inference and modelling modality-specific associations. Here, we propose an extension of GFA that handles missing data, and highlight that GFA can be used as a predictive model. We applied GFA to synthetic and real data consisting of brain connectivity and non-imaging measures from the Human Connectome Project (HCP). In synthetic data, GFA uncovered the underlying shared and specific factors and predicted correctly the non-observed data modalities in complete and incomplete data sets. In the HCP data, we identified four relevant shared factors, capturing associations between mood, alcohol and drug use, cognition, demographics and psychopathological measures and the default mode, frontoparietal control, dorsal and ventral networks and insula, as well as two factors describing associations within brain connectivity. In addition, GFA predicted a set of non-imaging measures from brain connectivity. These findings were consistent in complete and incomplete data sets, and replicated previous findings in the literature. GFA is a promising tool that can be used to uncover associations between and within multiple data modalities in benchmark datasets (such as, HCP), and easily extended to more complex models to solve more challenging tasks.
MeSH term(s)	Bayes Theorem ; Behavior/physiology ; Brain/diagnostic imaging ; Brain/physiology ; Connectome/methods ; Datasets as Topic ; Default Mode Network/diagnostic imaging ; Default Mode Network/physiology ; Factor Analysis, Statistical ; Humans ; Magnetic Resonance Imaging ; Mental Processes/physiology ; Models, Theoretical ; Nerve Net/diagnostic imaging ; Nerve Net/physiology
Language	English
Publishing date	2021-12-29
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	1147767-2
ISSN	1095-9572 ; 1053-8119
ISSN (online)	1095-9572
ISSN	1053-8119
DOI	10.1016/j.neuroimage.2021.118854
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Zs.A 3664: Show issues			Location: Je nach Verfügbarkeit (siehe Angabe bei Bestand) bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular Jg. 1995 - 2021: Lesesall (2.OG) ab Jg. 2022: Lesesaal (EG)
Zs.MO 7: Show issues

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Article ; Online: Canonical Correlation Analysis and Partial Least Squares for Identifying Brain-Behavior Associations: A Tutorial and a Comparative Study.

Mihalik, Agoston / Chapman, James / Adams, Rick A / Winter, Nils R / Ferreira, Fabio S / Shawe-Taylor, John / Mourão-Miranda, Janaina

Biological psychiatry. Cognitive neuroscience and neuroimaging

2022 Volume 7, Issue 11, Page(s) 1055–1067

Abstract: Canonical correlation analysis (CCA) and partial least squares (PLS) are powerful multivariate methods for capturing associations across 2 modalities of data (e.g., brain and behavior). However, when the sample size is similar to or smaller than the ... ...

Abstract	Canonical correlation analysis (CCA) and partial least squares (PLS) are powerful multivariate methods for capturing associations across 2 modalities of data (e.g., brain and behavior). However, when the sample size is similar to or smaller than the number of variables in the data, standard CCA and PLS models may overfit, i.e., find spurious associations that generalize poorly to new data. Dimensionality reduction and regularized extensions of CCA and PLS have been proposed to address this problem, yet most studies using these approaches have some limitations. This work gives a theoretical and practical introduction into the most common CCA/PLS models and their regularized variants. We examine the limitations of standard CCA and PLS when the sample size is similar to or smaller than the number of variables. We discuss how dimensionality reduction and regularization techniques address this problem and explain their main advantages and disadvantages. We highlight crucial aspects of the CCA/PLS analysis framework, including optimizing the hyperparameters of the model and testing the identified associations for statistical significance. We apply the described CCA/PLS models to simulated data and real data from the Human Connectome Project and Alzheimer's Disease Neuroimaging Initiative (both of n > 500). We use both low- and high-dimensionality versions of these data (i.e., ratios between sample size and variables in the range of ∼1-10 and ∼0.1-0.01, respectively) to demonstrate the impact of data dimensionality on the models. Finally, we summarize the key lessons of the tutorial.
MeSH term(s)	Humans ; Least-Squares Analysis ; Canonical Correlation Analysis ; Algorithms ; Brain ; Connectome
Language	English
Publishing date	2022-08-08
Publishing country	United States
Document type	Journal Article ; Review
ZDB-ID	2879089-3
ISSN	2451-9030 ; 2451-9022
ISSN (online)	2451-9030
ISSN	2451-9022
DOI	10.1016/j.bpsc.2022.07.012
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Article ; Online: Author Correction: A cross-cohort replicable and heritable latent dimension linking behaviour to multi-featured brain structure.

Nicolaisen-Sobesky, Eliana / Mihalik, Agoston / Kharabian-Masouleh, Shahrzad / Ferreira, Fabio S / Hoffstaedter, Felix / Schwender, Holger / Maleki Balajoo, Somayeh / Valk, Sofie L / Eickhoff, Simon B / Yeo, B T Thomas / Mourao-Miranda, Janaina / Genon, Sarah

Communications biology

2023 Volume 6, Issue 1, Page(s) 281

Language	English
Publishing date	2023-03-17
Publishing country	England
Document type	Published Erratum
ISSN	2399-3642
ISSN (online)	2399-3642
DOI	10.1038/s42003-023-04697-2
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Book ; Online: A hierarchical Bayesian model to find brain-behaviour associations in incomplete data sets

Ferreira, Fabio S. / Mihalik, Agoston / Adams, Rick A. / Ashburner, John / Mourao-Miranda, Janaina

2021

Abstract	Canonical Correlation Analysis (CCA) and its regularised versions have been widely used in the neuroimaging community to uncover multivariate associations between two data modalities (e.g., brain imaging and behaviour). However, these methods have inherent limitations: (1) statistical inferences about the associations are often not robust; (2) the associations within each data modality are not modelled; (3) missing values need to be imputed or removed. Group Factor Analysis (GFA) is a hierarchical model that addresses the first two limitations by providing Bayesian inference and modelling modality-specific associations. Here, we propose an extension of GFA that handles missing data, and highlight that GFA can be used as a predictive model. We applied GFA to synthetic and real data consisting of brain connectivity and non-imaging measures from the Human Connectome Project (HCP). In synthetic data, GFA uncovered the underlying shared and specific factors and predicted correctly the non-observed data modalities in complete and incomplete data sets. In the HCP data, we identified four relevant shared factors, capturing associations between mood, alcohol and drug use, cognition, demographics and psychopathological measures and the default mode, frontoparietal control, dorsal and ventral networks and insula, as well as two factors describing associations within brain connectivity. In addition, GFA predicted a set of non-imaging measures from brain connectivity. These findings were consistent in complete and incomplete data sets, and replicated previous findings in the literature. GFA is a promising tool that can be used to uncover associations between and within multiple data modalities in benchmark datasets (such as, HCP), and easily extended to more complex models to solve more challenging tasks. Comment: 52 pages, 18 figures (including supplementary material)
Keywords	Statistics - Machine Learning ; Computer Science - Machine Learning
Subject code	310
Publishing date	2021-03-11
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: A cross-cohort replicable and heritable latent dimension linking behaviour to multi-featured brain structure.

Communications biology

2022 Volume 5, Issue 1, Page(s) 1297

Abstract: Identifying associations between interindividual variability in brain structure and behaviour requires large cohorts, multivariate methods, out-of-sample validation and, ideally, out-of-cohort replication. Moreover, the influence of nature vs nurture on ... ...

Abstract	Identifying associations between interindividual variability in brain structure and behaviour requires large cohorts, multivariate methods, out-of-sample validation and, ideally, out-of-cohort replication. Moreover, the influence of nature vs nurture on brain-behaviour associations should be analysed. We analysed associations between brain structure (grey matter volume, cortical thickness, and surface area) and behaviour (spanning cognition, emotion, and alertness) using regularized canonical correlation analysis and a machine learning framework that tests the generalisability and stability of such associations. The replicability of brain-behaviour associations was assessed in two large, independent cohorts. The load of genetic factors on these associations was analysed with heritability and genetic correlation. We found one heritable and replicable latent dimension linking cognitive-control/executive-functions and positive affect to brain structural variability in areas typically associated with higher cognitive functions, and with areas typically associated with sensorimotor functions. These results revealed a major axis of interindividual behavioural variability linking to a whole-brain structural pattern.
MeSH term(s)	Humans ; Magnetic Resonance Imaging ; Brain/diagnostic imaging ; Gray Matter ; Cognition ; Executive Function
Language	English
Publishing date	2022-11-26
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't ; Research Support, N.I.H., Extramural
ISSN	2399-3642
ISSN (online)	2399-3642
DOI	10.1038/s42003-022-04244-5
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