Biology subjects

Aleman, A.

Publications and source records attributed to Aleman, A..

At least 19 recordsLinked to original sources

bioRxiv2026

Effect of stimulation parameters for transcranial temporal interference stimulation to enhance occipital alpha-oscillations in healthy participants

Transcranial temporal interference stimulation (tTIS) has recently emerged as a non-invasive method for modulating neural activity using interfering high-frequency electric fields. Although behavioral and neuroimaging effects of tTIS have been reported, evidence for electrophysiological modulation remains inconsistent, and it is unclear whether observed effects are attributable to temporal interference itself or to other components of the signal, e.g. high-frequency carrier signals. We therefore investigated whether tTIS at to the individual alpha frequency (IAF) modulates occipital alpha power compared with both sham stimulation and an active high-frequency carrier control. Eighteen healthy participants were enrolled in a randomized, double-blind, within-subject crossover study; 17 participants were included in the final analysis. Each participant completed three stimulation sessions consisting of three conditions (tTIS, carrier control, sham stimulation). tTIS was delivered for 20 minutes using two high-frequency signals at 1000 Hz and 1000 Hz + IAF, whereas the carrier control used two 1000-Hz signals without an envelope modulation. EEG was recorded before and after stimulation during a visual vigilance task. Individual electric-field distributions in the occipital cortex were estimated using structural MRI-based finite-element simulations. A significant main effect of stimulation condition on the change in occipital alpha power was observed (F(2,32) = 5.69, p = .008, eta2p = .26). Post-hoc comparisons showed a significantly greater increase in alpha power following tTIS compared with both the carrier control (mean difference = 0.30, adjusted p = .036, Cohens d = 0.686) and sham stimulation (mean difference = 0.31, adjusted p = .037, Cohens d = 0.683). Carrier and sham conditions did not differ (adjusted p = 1.000). No behavioral effects were observed. The magnitude of the alpha-power change was not significantly associated with simulated electric-field strength in the occipital target region. tTIS was well tolerated, with no stimulation-related study discontinuations. tTIS targeting the occipital cortex therefore produced significant post-stimulation enhancement of alpha power beyond both sham and high-frequency carrier stimulation. The absence of an effect in the carrier condition supports the interpretation that the electrophysiological aftereffect depends on temporal interference rather than high-frequency stimulation components alone. These findings provide evidence for frequency-specific modulation of cortical oscillations by tTIS and support further investigation of its underlying mechanisms and stimulation parameters.

neuroscience↗

bioRxiv2025

Time in sympatry correlates with the strength of reproductive isolation in hybridising Typha

In North America, hybridisation between Typha angustifolia and T. latifolia results in the highly impactful T. x glauca. In Europe, where its parental taxa are also sympatric, T. x glauca is scarce, suggesting stronger reproductive isolation on this continent. Using genomic data from the two species in North America and Europe, we reconstructed their demographic histories and characterised barrier loci between them. Demographic modelling suggests that the initial contact between the two species in Europe occurred [~]800,000 years ago, indicating sympatry in that region since the Middle Pleistocene. In North America, their contact likely happened within the last centuries and was potentially driven by the human-mediated dispersal of T. angustifolia. We identified 47 candidate barrier loci between species in Europe, 6 of which are associated with reproductive functions. No barrier loci were found in North America. Our results suggest that prolonged sympatry can promote the evolution of reproductive barriers, whereas prolonged allopatry can reduce the likelihood of their development. Future studies could help determine whether time in sympatry is a predictor of the strength of reproductive isolation between hybridising taxa. Preventing invasions by hybrid taxa will require limiting the human-mediated dispersal of Typha (and other allopatric species) lacking reproductive isolation.

genomics↗

bioRxiv2024

Rigosertib reverses hypertrophic cardiomyopathy in RAF1-associated Noonan syndrome

BackgroundRASopathies constitute a group of rare genetic disorders caused by mutations in genes that reside along the canonical Ras/MAPK signaling pathway, affecting cell growth and differentiation. These syndromes, which include conditions like Noonan syndrome (NS), are characterized by developmental delays, distinctive facial dysmorphia, and a variety of cardiac defects, notably hypertrophic cardiomyopathy (HCM). Despite their prevalence and impact, therapeutic options for RASopathies remain limited. Rigosertib, a novel dual Ras/MAPK and PI3K/AKT pathway inhibitor, is currently in clinical trials for treatment of melanoma and recessive dystrophic epidermolysis bullosa. Here, we identify rigosertib as a candidate therapy for RAF1-associated HCM. Methods and ResultsOur Drosophila screen of clinically relevant drugs and compounds identified rigosertib as broadly effective across a panel of transgenic RASopathy fly transgenic models, indicating that rigosertib may be effective against multiple disease isoforms. Analysis of a Drosophila model targeting a RAF1L613V transgene to the heart found that rigosertib reduced aspects of cardiac hypertrophy. Rigosertib treatment prevented or regressed cellular hypertrophy in human induced pluripotent stem cell-(iPSC-) derived cardiomyocytes homozygous for the NS-associated RAF1S257L allele. We extended these findings to a mammalian model, using Raf1L613V/+ KI mice to explore the therapeutic implications of rigosertib on RAF1-driven HCM. Longitudinal six-week treatment with rigosertib in these mice resulted in significant improvement in left ventricular chamber dimension and posterior wall thickness, total heart mass, size of individual cardiomyocytes (CMs), as well as reversal of cardiac hypertrophy. Rigosertib treatment also led to normalized fetal gene expression and inhibition of ERK and AKT pathway activities in primary CMs isolated from Raf1L613V/+ mice. Cardiac function, as assessed by echocardiography, showed significant improvement in ejection fraction and fractional shortening, with molecular studies confirming downregulation of hypertrophic markers and signaling pathways. Together with the Drosophila data, these mammalian results support the potential and use for rigosertib to reverse pathological hypertrophy in NS through targeted pathway inhibition in patients. Moreover, in addition to its effects in the heart, rigosertib treatment in mice also significantly improved other NS-associated syndromic features, including increasing bone growth and correcting craniofacial abnormalities. ConclusionsTaken together, our findings suggest rigosertib effectively normalizes and reverses RASopathy-associated HCM as well as other NS-associated syndromic features, supporting its potential for development as a promising treatment for RAF1-associated HCM and, potentially, other RASopathies-dependent pathologies. This study not only highlights the therapeutic potential of rigosertib but also demonstrates the utility of an integrated approach using Drosophila, iPSC and mammalian models to elucidate drug effects across complex biological systems.

molecular biology↗

bioRxiv2024

Allopatric speciation in cattails: Genomic landscapes of divergence across Typha spp. suggest balancing selection, introgressions, and the absence of adaptive divergence

Understanding how speciation unfolds is a central goal of evolutionary biology. Historically, global climatic fluctuations have triggered allopatric diversification. Using genome-wide data, we reconstructed the phylogenetic relationships and demographic histories of five Typha species, a plant genus foundational to freshwater ecosystems, with widespread, partially sympatric ranges and at least one widespread, regionally invasive hybrid zone (Typha x glauca). Molecular clock and demographic analyses indicate that the species in this study diverged in the absence of gene flow, during periods roughly contemporaneous with population bottlenecks and expansions in Typha--likely triggered by geoclimatic events--and that this divergence was followed by secondary contact in more recent times. Genomic scans showed no evidence of selection with gene flow driving species differentiation, suggesting a minor role for sympatric ecological speciation underlying divergence. These observations are consistent with expectations for drift-driven allopatric speciation. Allopatric speciation in T. latifolia and T. angustifolia, their low genetic differentiation, and the scarcity of ecological divergence in building their reproductive isolation could explain these species ability to hybridise.

evolutionary biology↗

bioRxiv2023

A simple method to genetically differentiate invasive F1 Typha hybrids (T. x glauca) and advanced-generation/backcrossed hybrids from parent species (T. latifolia and T. angustifolia) in eastern Canada and northeastern USA

A widespread Typha (cattail) hybrid zone in southeastern Canada and northeastern USA comprises T. latifolia, T. angustifolia, and their hybrid T. x glauca. In this region the hybrid is considered invasive because of its detrimental impacts on wetlands and biodiversity. Researchers and managers are increasingly interested in tracking and controlling the spread of the invasive hybrid, but this can be hampered by challenges in differentiating taxa: while the parent species and F1 hybrids can often be identified from morphological characteristics, advanced-generation and backcrossed hybrids can be common, and these have obscured morphological distinctions among taxa. Here we present five species-specific genetic markers that differentiate T. latifolia, T. angustifolia, F1 hybrids, and advanced-generation/backcrossed hybrids. Unlike the pre-existing species-specific microsatellite markers, these markers require only PCR or PCR followed by a restriction enzyme digest, and can be visualized on an agarose gel. As a result, they require less specialized equipment than existing species-specific markers, and should therefore be useful for a larger group of practitioners. Future use of these markers could include monitoring range expansions, assessing the outcomes of restoration programs, and avoiding the unnecessary elimination of native Typha.

molecular biology↗

bioRxiv2022

Neurocomputational mechanisms underlying fear-biased adaptation learning in changing environments

Humans are able to adapt to the fast-changing world by estimating statistical regularities of the environment. Although fear can profoundly impact adaptive behaviors, the neural mechanisms underlying this phenomenon remain elusive. Here, we conducted a behavioral experiment (n = 21) and a functional magnetic resonance imaging experiment (n = 37) with a novel cue-biased adaptation learning task, during which we simultaneously manipulated emotional valence (fearful/neutral expressions of the cue) and environmental volatility (frequent/infrequent reversals of reward probabilities). Across two experiments, computational modelling consistently revealed a higher learning rate for the environment with frequent versus infrequent reversals following neutral cues. In contrast, this flexible adjustment was absent in the environment with fearful cues, suggesting a suppressive role of fear in adaptation to environmental volatility. This suppressive effect was underpinned by activity of the posterior parietal cortex, ventral striatum, hippocampus and dorsal anterior cingulate cortex (dACC) as well as increased functional connectivity between the dACC and temporal-parietal junction (TPJ) for fear with environmental volatility. Dynamic causal modelling identified that the driving effect was located in the TPJ and was associated with dACC activation, suggesting that the suppression of fear on adaptive behaviors occurs at the early stage of bottom-up processing. These findings provide a neuro-computational account of how fear interferes with adaptation to volatility during dynamic environments.

neuroscience↗

bioRxiv2022

Oscillatory dynamics underlying emotion-cognition integration: differential role of theta and alpha oscillations

A fundamental aspect of human mental life is the seamless ability for integration of emotion and cognition. Despite progress regarding the spatial architecture of Emotion-Cognition Integration (ECI), the time course of ECI processes remains unclear. To examine the temporal organization of brain oscillations underpinning ECI, we simultaneously manipulated emotional valence of stimuli and cognitive task demand while recording electrophysiological responses of 61 participants. They were asked to complete tasks with low (body-part judgement) and high (laterality judgement) cognitive demand while viewing other people photographs that varied on dimensions of laterality (left or right), body-part (hand or foot), and emotional valence (pain or no pain). We found increased reaction times and error rates in pain versus no pain during laterality judgement relative to body-part judgement, suggesting reciprocal inhibition between emotion and cognition. EEG results showed that 1) emotion processing (valence) occurred first in the theta band from 144 to 372 ms; 2) cognitive processing (laterality) took place in the theta band from 332 to 608 ms; 3) emotional and cognitive processes were integrated in the alpha band from 268 ms and lasted to 800 ms. These findings reveal oscillatory dynamics of the processing and integration of emotion and cognition, providing further insights into the underlying neurophysiology. This may ultimately contribute to our understanding of ECI processing in psychopathology.

neuroscience↗

bioRxiv2022

Molecular and connectomic vulnerability shape cross-disorder cortical abnormalities

Numerous brain disorders demonstrate structural brain abnormalities, which are thought to arise from molecular perturbations or connectome miswiring. The unique and shared contributions of these molecular and connectomic vulnerabilities to brain disorders remain unknown, and has yet to be studied in a single multi-disorder framework. Using MRI morphometry from the ENIGMA consortium, we construct maps of cortical abnormalities for thirteen neurodevelopmental, neurological, and psychiatric disorders from N = 21 000 patients and N = 26 000 controls, collected using a harmonized processing protocol. We systematically compare cortical maps to multiple micro-architectural measures, including gene expression, neurotransmitter density, metabolism, and myelination (molecular vulnerability), as well as global connectomic measures including number of connections, centrality, and connection diversity (connectomic vulnerability). We find that regional molecular vulnerability and macroscale brain network architecture interact to drive the spatial patterning of cortical abnormalities in multiple disorders. Local attributes, particularly neurotransmitter receptor profiles, constitute the best predictors of both disorder-specific cortical morphology and cross-disorder similarity. Finally, we find that cross-disorder abnormalities are consistently subtended by a small subset of network epicentres in bilateral sensory-motor, medial temporal lobe, precuneus, and superior parietal cortex. Collectively, our results highlight how local biological attributes and global connectivity jointly shape cross-disorder cortical abnormalities.

neuroscience↗

bioRxiv2021

Challenging the notion of a task-negative network: default mode network involvement under varying levels of cognitive effort

Everyday cognitive functioning is characterized by constant alternations between different modes of information processing, driven by fluctuations in environmental demands. At the neural level, this is realized through corresponding dynamic shifts in functional activation and network connectivity. A distinction is often made between the Default Mode Network (DMN) as a task-negative network that is upregulated in the absence of cognitive demands, and task-positive networks that are upregulated when cognitive demands such as attention and executive control are present. Such networks have been labelled the Extrinsic Mode Network (EMN). We investigated changes in brain activation and functional network connectivity during repeated alternations between levels of cognitive effort. Using fMRI and a block-design Stroop paradigm, participants switched back and forth between periods of no effort (resting), low effort (word reading, automatic processing) and high effort (color naming, cognitive control). Results showed expected EMN-activation for task versus rest, and likewise expected DMN-activation for rest versus task. The DMN was also more strongly activated during low effort contrasted with high effort, suggesting a gradual up- and down-regulation of the DMN, depending on the level of demand. The often reported "anti-correlation" between DMN and EMN was only present during periods of low effort, indicating intermittent contributions of both networks. These results challenge the traditional view of the DMN as solely a task-negative network. Instead, the present results suggest that both EMN and DMN may contribute to low-effort cognitive processing. In contrast, periods of resting and high effort are dominated by the DMN and EMN, respectively.

neuroscience↗

bioRxiv2021

Opposing subclasses of Drosophila ellipsoid body neurons promote and suppress sleep

Recent work in Drosophila has uncovered several neighboring classes of sleep-regulatory neurons within the central complex. However, the logic of connectivity and network motifs remains limited by the incomplete examination of relevant cell types. Using a recent genetic-anatomical classification of ellipsoid body ring neurons, we conducted a thermogenetic screen to assess sleep/wake behavior and discovered two opposing populations: sleep-promoting R3m and wake-promoting R3d neurons. Activation of these neurons influences sleep duration and architecture by prolonging or shortening sleep bouts, suggesting a key role in sleep maintenance. R3m and R3d neurons are GABAergic and require GABA synthesis for their effects on sleep. Finally, we use a fluorescent reporter for putative synaptic partners to embed these neurons within the known sleep-regulatory network; R3m and R3d neurons lay downstream of wake-active Helicon cells, and R3m neurons likely inhibit R3d neurons. Together, the data presented herein suggest a neural mechanism by which previously uncharacterized circuit elements operate within the sleep homeostat to stabilize sleep-wake states.

neuroscience↗

bioRxiv2021

Asymmetrical adaptations to increases and decreases in environmental volatility

Humans adapt their learning strategies to changing environments by estimating the volatility of the reinforcement conditions. Here, we examine how volatility affects learning and the underlying functional brain organizations using a probabilistic reward reversal learning task. We found that the order of conditions was critically important; participants adjusted learning rate going from volatile to stable, but not from stable to volatile, environments. Subjective volatility of the environment was encoded in the striatal reward system and its dynamic connections with the prefrontal control system. Flexibility, which captures the dynamic changes of network modularity in the brain, was higher in the environmental transition from volatile to stable than from stable to volatile. These findings suggest that behavioral adaptations and dynamic brain organizations in transitions between stable and volatile environments are asymmetric, providing critical insights into the way that people learn under uncertainty.

neuroscience↗

bioRxiv2020

Schema-induced shifts in mice navigational strategies are unveiled by a minimal behavioral model of spatial exploration.

Shifts in spatial patterns produced during the execution of a navigational task can be used to track the effects of the accumulation of knowledge and the acquisition of structured information about the environment. Here we provide a quantitative analysis of mice behavior while performing a novel goal localization task in a large, modular arena, the HexMaze. To demonstrate the effects of different forms of previous knowledge we first obtain a precise statistical characterization of animals paths with sub-trial resolution and over different phases of learning. The emergence of a flexible representation of the task is accompanied by a progressive improvement of performance, mediated by multiple, multiplexed time scales. We then use a generative mathematical model of the animal behavior to isolate the specific contributions to the final navigational strategy. We find that animal behavior can be accurately reproduced by the combined effect of a goal-oriented component, becoming stronger with the progression of learning, and of a random walk component, producing choices unrelated to the task and only partially weakened in time.

neuroscience↗

bioRxiv2020

Connectivity patterns of task-specific brain networks allow individual prediction of cognitive symptom dimension of schizophrenia and link to molecular architecture

BackgroundDespite the marked inter-individual variability in the clinical presentation of schizophrenia, it remains unclear the extent to which individual dimensions of psychopathology may be reflected in variability across the collective set of functional brain connections. Here, we address this question using network-based predictive modeling of individual psychopathology along four data-driven symptom dimensions. Follow-up analyses assess the molecular underpinnings of predictive networks by relating them to neurotransmitter-receptor distribution patterns. MethodsWe investigated resting-state fMRI data from 147 schizophrenia patients recruited at seven sites. Individual expression along negative, positive, affective, and cognitive symptom dimensions was predicted using relevance vector machine based on functional connectivity within 17 meta-analytic task-networks following a repeated 10-fold cross-validation and leave-one-site-out analyses. Results were validated in an independent sample. Networks robustly predicting individual symptom dimensions were spatially correlated with density maps of nine receptors/transporters from prior molecular imaging in healthy populations. ResultsTen-fold and leave-one-site-out analyses revealed five predictive network-symptom associations. Connectivity within theory-of-mind, cognitive reappraisal, and mirror neuron networks predicted negative, positive, and affective symptom dimensions, respectively. Cognitive dimension was predicted by theory-of-mind and socio-affective-default networks. Importantly, these predictions generalized to the independent sample. Intriguingly, these two networks were positively associated with D1 dopamine receptor and serotonin reuptake transporter densities as well as dopamine-synthesis-capacity. ConclusionsWe revealed a robust association between intrinsic functional connectivity within networks for socio-affective processes and the cognitive dimension of psychopathology. By investigating the molecular architecture, the present work links dopaminergic and serotonergic systems with the functional topography of brain networks underlying cognitive symptoms in schizophrenia.

neuroscience↗

bioRxiv2020

Connectome-based predictive modeling of individual anxiety

Anxiety-related illnesses are highly prevalent in human society. Being able to identify neurobiological markers signaling high trait anxiety could aid the assessment of individuals with high risk for mental illness. Here, we applied connectome-based predictive modeling (CPM) to whole-brain resting-state functional connectivity (rsFC) data to predict the degree of anxiety in 76 healthy participants. Using a computational "lesion" method in CPM, we then examined the weights of the identified main brain areas as well as their connectivity. Results showed that the CPM could predict individual anxiety from whole-brain rsFC, especially from limbic areas-whole brain and prefrontal cortex-whole brain. The prediction power of the model significantly decreased from (simulated) lesions of limbic areas, lesions of the connectivity within the limbic system, and lesions of the connectivity between limbic regions and the prefrontal cortex. Although the same model also predicted depression, anxiety-specific networks could be identified independently, centered at the prefrontal cortex. These findings highlight the important role of the limbic system and the prefrontal cortex in the prediction of anxiety. Our work provides evidence for the usefulness of connectome-based modeling of rsFC in predicting individual personality differences and indicates its potential for identifying personality structures at risk of developing psychopathology.

neuroscience↗

bioRxiv2019

Brain Aging in Major Depressive Disorder: Results from the ENIGMA Major Depressive Disorder working group

BackgroundMajor depressive disorder (MDD) is associated with an increased risk of brain atrophy, aging-related diseases, and mortality. We examined potential advanced brain aging in MDD patients, and whether this process is associated with clinical characteristics in a large multi-center international dataset. MethodsWe performed a mega-analysis by pooling brain measures derived from T1-weighted MRI scans from 29 samples worldwide. Normative brain aging was estimated by predicting chronological age (10-75 years) from 7 subcortical volumes, 34 cortical thickness and 34 surface area, lateral ventricles and total intracranial volume measures separately in 1,147 male and 1,386 female controls from the ENIGMA MDD working group. The learned model parameters were applied to 1,089 male controls and 1,167 depressed males, and 1,326 female controls and 2,044 depressed females to obtain independent unbiased brain-based age predictions. The difference between predicted "brain age" and chronological age was calculated to indicate brain predicted age difference (brain-PAD). FindingsOn average, MDD patients showed a higher brain-PAD of +0.90 (SE 0.21) years (Cohens d=0.12, 95% CI 0.06-0.17) compared to controls. Relative to controls, first-episode and currently depressed patients showed higher brain-PAD (+1.2 [0.3] years), and the largest effect was observed in those with late-onset depression (+1.7 [0.7] years). In addition, higher brain-PAD was associated with higher self-reported depressive symptomatology (b=0.05, p=0.004). InterpretationThis highly powered collaborative effort showed subtle patterns of abnormal structural brain aging in MDD. Substantial within-group variance and overlap between groups were observed. Longitudinal studies of MDD and somatic health outcomes are needed to further assess the predictive value of these brain-PAD estimates. FundingThis work was supported, in part, by NIH grants U54 EB020403 and R01 MH116147.

neuroscience↗

bioRxiv2019

Subcortical Shape Alterations in Major Depressive Disorder: Findings from the ENIGMA Major Depressive Disorder Working Group

Alterations in regional subcortical brain volumes have been widely investigated as part of the efforts of an international consortium, ENIGMA, to determine reliable structural brain signatures for Major Depressive Disorder (MDD). Given that subcortical structures are comprised of distinct subfields, we sought to build significantly from prior work to precisely map localized MDD-related differences in subcortical regions using shape analysis. In this meta-analysis of subcortical shape from the ENIGMA-MDD working group, we compared 1,781 patients with MDD and 2,953 healthy controls (CTL) on individual measures of shape metrics (thickness and surface area) on the surface of seven bilateral subcortical structures: nucleus accumbens, amygdala, caudate, hippocampus, pallidum, putamen, and thalamus. Harmonized data processing and statistical analyses were conducted locally at each site, and findings were aggregated by meta-analysis. Relative to CTL, patients with MDD had lower surface area in the subiculum of the hippocampus, the basolateral amygdala, and the nucleus accumbens shell. Relative to CTL, patients with adolescent-onset MDD ([≤] 21 years) had lower thickness and surface area of the subiculum of the hippocampus and the basolateral amygdala. Relative to first-episode MDD, recurrent MDD patients had lower thickness and surface area in the CA1 of the hippocampus and the basolateral amygdala. Our results suggest that previously reported MDD-associated volumetric differences may be localized to specific subfields of these structures that have been shown to be sensitive to the effects of stress, with important implications for mapping treatments to patients based on specific neural targets and key clinical features.

neuroscience↗

bioRxiv2018

Deep Learning for Quality Control of Subcortical Brain 3D Shape Models

We present several deep learning models for assessing the morphometric fidelity of deep grey matter region models extracted from brain MRI. We test three different convolutional neural net architectures (VGGNet, ResNet and Inception) over 2D maps of geometric features. Further, we present a novel geometry feature augmentation technique based on parametric spherical mapping. Finally, we present an approach for model decision visualization, allowing human raters to see the areas of subcortical shapes most likely to be deemed of failing quality by the machine. Our training data is comprised of 5200 subjects from the ENIGMA Schizophrenia MRI cohorts, and our test dataset contains 1500 subjects from the ENIGMA Major Depressive Disorder cohorts. Our final models reduce human rater time by 46-70%. ResNet outperforms VGGNet and Inception for all of our predictive tasks.

neuroscience↗

bioRxiv2018

Brain-based ranking of cognitive domains to predict schizophrenia

Schizophrenia is a devastating brain disorder that disturbs sensory perception, motor action, and abstract thought. Its clinical phenotype implies dysfunction of various mental domains, which has motivated a series of theories regarding the underlying pathophysiology. Aiming at a predictive benchmark of a catalogue of cognitive functions, we developed a bottom-up machine-learning strategy and provide a proof of principle in a multi-site clinical dataset (n=324). Existing neuroscientific knowledge on diverse cognitive domains was first condensed into neuro-topographical maps. We then examined how the ensuing meta-analytic cognitive priors can distinguish patients and controls using brain morphology and intrinsic functional connectivity. Some affected cognitive domains supported well-studied directions of research on auditory evaluation and social cognition. However, rarely suspected cognitive domains also emerged as disease-relevant, including self-oriented processing of bodily sensations in gustation and pain. Such algorithmic charting of the cognitive landscape can be used to make targeted recommendations for future mental health research.

neuroscience↗