Biology subjects

Adolphs, R.

Publications and source records attributed to Adolphs, R..

16 recordsLinked to original sources

bioRxiv2026

Large-Vessel Venous Signal Confounds Apparent Amygdala Activation in BOLD fMRI

Amygdala responses are widely reported in BOLD fMRI studies of human emotions and mood disorders. Although modern acquisition methods have improved image quality, the hemodynamic nature of the BOLD signal continues to complicate interpretation of these responses. Across an array of both visual and auditory tasks, we here show that apparent amygdala BOLD signal is substantially confounded by signal from nearby deep veins, including the basal vein of Rosenthal. In contrast to prior work, we show that the amygdala, when engaged, is the leading but not exclusive contributor to this venous signal. Across the tested conditions, this signal was sensitive to, but not specific for, amygdala engagement. This confound mislocalizes apparent activation and prevents reliable attribution of signal to amygdala subnuclei with conventional BOLD fMRI. We further show that spatial smoothing exacerbates this mislocalization by merging peri-amygdalar venous signal with the anatomical amygdala. We reproduce our findings from precision imaging in a larger sample obtained from the Human Connectome Project. While broad conclusions about amygdala engagement remain largely valid when based on unsmoothed signal within the anatomical amygdala, further conclusions about subnuclear function drawn from conventional fMRI alone warrant considerable caution.

neuroscience↗

bioRxiv2024

Neural dynamics underlying minute-timescale persistent behavior in the human brain

The ability to pursue long-term goals relies on a representations of task context that can both be maintained over long periods of time and switched flexibly when goals change. Little is known about the neural substrate for such minute-scale maintenance of task sets. Utilizing recordings in neurosurgical patients, we examined how groups of neurons in the human medial frontal cortex and hippocampus represent task contexts. When cued explicitly, task context was encoded in both brain areas and changed rapidly at task boundaries. Hippocampus exhibited a temporally dynamic code with fast decorrelation over time, preventing cross-temporal generalization. Medial frontal cortex exhibited a static code that decorrelated slowly, allowing generalization across minutes of time. When task context needed to be inferred as a latent variable, hippocampus encoded task context with a static code. These findings reveal two possible regimes for encoding minute-scale task-context representations that were engaged differently based on task demands.

neuroscience↗

bioRxiv2023

Unsupervised mapping of causal relations between brain and behavior

Human lesion studies offer one of the most direct routes to investigating the relations between brain regions and behavioral outcomes in circumstances where experimental interventions are highly restricted. However, these studies face a major challenge in identifying the right level of granularity at which brain regions and behavioral outcomes should be analyzed to identify the relation between lesions to specific brain regions and specific behavioral outcomes. Here we showcase a novel data-driven approach, Causal Feature Learning (CFL), that learns the appropriate level of analysis and the relation between lesion and cognitive impairment at the same time. The method avoids specifying brain regions and specific outcome measures a priori, allowing for the discovery of new cross-cutting lesion-behavior maps. We show that CFL robustly recovers lesion behavior maps in a simulated dataset where Canonical Correlation Analysis fails to provide interpretable results. We then show that CFL recovers known lesion-behavior maps for language deficits and visuospatial processing using a large dataset of lesion subjects, and we illustrate how CFL can be used to identify new groupings of outcomes when mapping lesions to depression symptoms.

neuroscience↗

bioRxiv2023

Abstract representations emerge in human hippocampal neurons during inference behavior

Humans have the remarkable cognitive capacity to rapidly adapt to changing environments. Central to this capacity is the ability to form high-level, abstract representations that take advantage of regularities in the world to support generalization1. However, little is known about how these representations are encoded in populations of neurons, how they emerge through learning, and how they relate to behavior2,3. Here we characterized the representational geometry of populations of neurons (single-units) recorded in the hippocampus, amygdala, medial frontal cortex, and ventral temporal cortex of neurosurgical patients who are performing an inferential reasoning task. We find that only the neural representations formed in the hippocampus simultaneously encode multiple task variables in an abstract, or disentangled, format. This representational geometry is uniquely observed after patients learn to perform inference, and consisted of disentangled directly observable and discovered latent task variables. Interestingly, learning to perform inference by trial and error or through verbal instructions led to the formation of hippocampal representations with similar geometric properties. The observed relation between representational format and inference behavior suggests that abstract/disentangled representational geometries are important for complex cognition.

neuroscience↗

bioRxiv2021

Video-evoked fMRI BOLD responses are highly consistent across different data acquisition sites

Naturalistic imaging paradigms, in which participants view complex videos in the scanner, are increasingly used in human cognitive neuroscience. Videos evoke temporally synchronized brain responses that are similar across subjects as well as within subjects, but the reproducibility of these brain responses across different data acquisition sites has not yet been quantified. Here we characterize the consistency of brain responses across independent samples of participants viewing the same videos in fMRI scanners at different sites (Indiana University and Caltech). We compared brain responses collected at these different sites for two carefully matched datasets with identical scanner models, acquisition, and preprocessing details, along with a third unmatched dataset in which these details varied. Our overall conclusion is that for matched and unmatched datasets alike, video-evoked brain responses have high consistency across these different sites, both when compared across groups and across pairs of individuals. As one might expect, differences between sites were larger for unmatched datasets than matched datasets. Residual differences between datasets could in part reflect participant-level variability rather than scanner- or data-related effects. Altogether our results indicate promise for the development and, critically, generalization of video fMRI studies of individual differences in healthy and clinical populations alike.

neuroscience↗

bioRxiv2021

The geometry of domain-general performance monitoring representations in the human medial frontal cortex

Controlling behavior to flexibly achieve desired goals depends on the ability to monitor ones own performance. It is unknown how performance monitoring can be both flexible to support different tasks and specialized to perform well on each. We recorded single neurons in the human medial frontal cortex while subjects performed two tasks that involve three types of cognitive conflict. Neurons encoding predicted conflict, conflict, and error in one or both tasks were intermixed, forming a representational geometry that simultaneously allowed task specialization and generalization. Neurons encoding conflict retrospectively served to update internal estimates of control demand. Population representations of conflict were compositional. These findings reveal how representations of evaluative signals can be both abstract and task-specific and suggest a neuronal mechanism for estimating control demand.

neuroscience↗

bioRxiv2021

Atypical gaze patterns in autism are heterogeneous across subjects but reliable within individuals

People with autism spectrum disorder (ASD) have atypical gaze onto both static visual images 1,2 and dynamic videos 3,4 that could be leveraged for diagnostic purposes 5,6. Eye tracking is important for characterizing ASD across the lifespan 7 and nowadays feasible at home (e.g., from smartphones 8). Yet gaze-based classification has been difficult to achieve, due to sources of variance both across and within subjects. Here we test three competing hypotheses: (a) that ASD could be successfully classified from the fact that gaze patterns are less reliable or noisier than in controls, (b) that gaze patterns are atypical and heterogeneous across ASD subjects but reliable over time within a subject, or (c) that gaze patterns are individually reliable and also homogenous among individuals with ASD. Leveraging dense eye tracking data from two different full-length television sitcom episodes in a total of over 150 subjects (N = 53 ASD, 107 controls) collected at two different sites, we demonstrate support for the second of these hypotheses. The findings pave the way for the investigation of autism subtypes, and for elucidating the specific visual features that best discriminate gaze patterns -- directions that will also inform neuroimaging and genetic studies of this complex disorder.

neuroscience↗

bioRxiv2021

Intracranial electrical stimulation alters meso-scale network integration as a function of network topology

Human brain dynamics are organized into a multi-scale network structure that contains multiple tight-knit, meso-scale communities. Recent work has demonstrated that many psychological capacities, as well as impairments in cognitive function secondary to damage, can be mapped onto organizing principles at this mesoscopic scale. However, we still dont know the rules that govern the dynamic interactions between regions that are constrained by the topology of the broader network. In this preregistered study, we utilized a unique human dataset in which whole brain BOLD-fMRI activity was recorded simultaneously with intracranial electrical stimulation, to characterize the effects of direct neural stimulation on the dynamic reconfiguration of the broader network. Direct neural stimulation increased the extent to which the stimulation sites own mesoscale community integrated with the rest of the brain. Further, we found that these network changes depended on the topological role of the stimulation site itself: stimulating regions with high participation coefficients led to global integration, whereas stimulating sites with low participation coefficients integrated that regions own community with the rest of the brain. These findings provide direct causal evidence for how network topology shapes and constrains inter-regional coordination, and suggest applications for targeted therapeutic interventions in patients with deep-brain stimulation.

neuroscience↗

bioRxiv2020

Human es-fMRI Resource: Concurrent deep-brain stimulation and whole-brain functional MRI

Mapping the causal effects of one brain region on another (effective connectivity) is a challenging problem in neuroscience, since it requires invasive direct manipulation of brain function, together with whole-brain measurement of the effects produced. Here we establish a unique resource and present data from 26 human patients who underwent electrical stimulation during functional magnetic resonance imaging (es-fMRI). The patients had medically refractory epilepsy requiring surgically implanted intracranial electrodes in cortical and subcortical locations. One or multiple contacts on these electrodes were stimulated while simultaneously recording BOLD-fMRI activity in a block design. Multiple runs exist for patients with different stimulation sites. We describe the resource, data collection process, preprocessing using the fMRIPrep analysis pipeline and management of artifacts, and provide end-user analyses to visualize distal brain activation produced by site-specific electrical stimulation. The data are organized according to the brain imaging data structure (BIDS) specification, and are available for analysis or future dataset contributions on openneuro.org including both raw and preprocessed data.

neuroscience↗

bioRxiv2020

Common Fronto-temporal Effective Connectivity in Humans and Monkeys

Cognitive pathways supporting human language and declarative memory are thought to have uniquely evolutionarily differentiated in our species. However, cross-species comparisons are missing on site-specific effective connectivity between regions important for cognition. We harnessed a new approach using functional imaging to visualize the impact of direct electrical brain stimulation in human neurosurgery patients. Applying the same approach with macaque monkeys, we found remarkably comparable patterns of effective connectivity between auditory cortex and ventro-lateral prefrontal cortex (vlPFC) and parahippocampal cortex in both species. Moreover, in humans electrical tractography revealed rapid evoked potentials in vlPFC from stimulating auditory cortex and speech sounds drove vlPFC, consistent with prior evidence in monkeys of direct projections from auditory cortex to vocalization responsive regions in vlPFC. The results identify a common effective connectivity signature that from auditory cortex is equally direct to vlPFC and indirect to the hippocampus (via parahippocampal cortex) in human and nonhuman primates. HighlightsO_LIPrivileged human auditory to inferior frontal connectivity, linked to monkeys C_LIO_LICommon auditory to parahippocampal effective connectivity in both species C_LIO_LIGreater lateralization in human effective connectivity, more symmetrical in monkeys C_LIO_LIHuman fronto-temporal network function rooted in evolutionarily conserved signature C_LI eTOC short summaryFunctional connectivity between regions crucial for language and declarative memory is thought to have substantially differentiated in humans. Using a new technique to similarly visualize directional effective connectivity in humans and monkeys, we found remarkably comparable connectivity patterns in both species between fronto-temporal regions crucial for cognition.

neuroscience↗

bioRxiv2019

No strong evidence that social network index is associated with gray matter volume from a data-driven investigation

Recent studies in adult humans have reported correlations between individual differences in peoples Social Network Index (SNI) and gray matter volume (GMV) across multiple regions of the brain. However, the cortical and subcortical loci identified are inconsistent across studies. These discrepancies might arise because different regions of interest were hypothesized and tested in different studies without controlling for multiple comparisons, and/or from insufficiently large sample sizes to fully protect against statistically unreliable findings. Here we took a data-driven approach in a pre-registered study to comprehensively investigate the relationship between SNI and GMV in every cortical and subcortical region, using three predictive modeling frameworks. We also included psychological predictors such as cognitive and emotional intelligence, personality, and mood. In a sample of healthy adults (n = 92), neither multivariate frameworks (e.g., ridge regression with cross-validation) nor univariate frameworks (e.g., univariate linear regression with cross-validation) showed a significant association between SNI and any GMV or psychological feature after multiple comparison corrections (all R-squared values [≤] 0.1). These results emphasize the importance of large sample sizes and hypothesis-driven studies to derive statistically reliable conclusions, and suggest that future meta-analyses will be needed to more accurately estimate the true effect sizes in this field.

neuroscience↗

bioRxiv2019

Flexible recruitment of memory-based choice representations by human medial-frontal cortex

Decisions in complex environments rely on flexibly utilizing past experience as required by context and instructions1. This process depends on the medial frontal cortex (MFC) and the medial temporal lobe (MTL)2-5, but it remains unknown how these structures jointly implement flexible memory retrieval6,7. We recorded single neurons in MFC and MTL while human subjects switched8 between making memory- and categorization-based decisions9,10. Here we show that MFC rapidly implements changing task demands by utilizing different subspaces of neural activity during different types of decisions. In contrast, no effect of task demands was seen in the MTL. Choices requiring memory retrieval selectively engaged phase-locking of MFC neurons to field potentials in the theta-frequency band in the MTL. Choice-selective neurons in MFC signaled abstract yes-no decisions independent of behavioral response modality (button press or saccade). These findings reveal a novel mechanism for flexibly and selectively engaging memory retrieval11-14 and show that unlike perceptual decision-making15, memory-related information is only represented in frontal cortex when choices require it.

neuroscience↗

bioRxiv2019

Estimating the heritability of psychological measures in the Human Connectome Project dataset

The Human Connectome Project (HCP) is a large structural and functional MRI dataset with a rich array of behavioral measures and extensive family structure. This makes it a valuable resource for investigating questions about individual differences, including questions about heritability. While its MRI data have been analyzed extensively in this regard, to our knowledge a comprehensive estimation of the heritability of the behavioral dataset has never been conducted. Using a set of behavioral measures of personality, emotion and cognition, we show that it is possible to re-identify the same individual across two testing times, and identify identical twins. Using machine-learning (univariate linear model, Ridge classifier and Random Forest model) we estimated the heritability of 37 behavioral measures and compared the results to those derived from twin correlations. Correlations between the standard heritability metric and each set of model weights ranged from 0.42 to 0.67, and questionnaire-based and task-based measures did not differ significantly in their heritability. We further derived nine latent factors from the 37 measures and repeated the heritability estimation; in this case, the correlations between the standard heritability and each set of model weights were lower, ranging from 0.15 to 0.38. One specific discrepancy arose for the general intelligence factor, which all models assigned high importance, but the standard heritability calculation did not. We present an alternative method for qualitatively estimating the heritability of the behavioral measures in the HCP as a resource for other investigators, and recommend the use of machine-learning models for estimating heritability.

genetics↗

bioRxiv2018

A distributed brain network predicts general intelligence from resting-state human neuroimaging data

Individual people differ in their ability to reason, solve problems, think abstractly, plan and learn. A reliable measure of this general ability, also known as intelligence, can be derived from scores across a diverse set of cognitive tasks. There is great interest in understanding the neural underpinnings of individual differences in intelligence, since it is the single best predictor of longterm life success. The most replicated neural correlate of human intelligence to date is total brain volume; however, this coarse morphometric correlate says little about function. Here we ask whether measurements of the activity of the resting brain (resting-state fMRI) might also carry information about intelligence. We used the final release of the Young Adult Human Connectome Project (N=884 subjects after exclusions), providing a full hour of resting-state fMRI per subject; controlled for gender, age, and brain volume; and derived a reliable estimate of general intelligence from scores on multiple cognitive tasks. Using a cross-validated predictive framework, we predicted 20% of the variance in general intelligence in the sampled population from their resting-state connectivity matrices. Interestingly, no single anatomical structure or network was responsible or necessary for this prediction, which instead relied on redundant information distributed across the brain.

neuroscience↗

bioRxiv2017

Predicting personality traits from resting-state fMRI

Personality neuroscience aims to find associations between brain measures and personality traits. Findings to date have been severely limited by a number of factors, including small sample size and omission of out-of-sample prediction. We capitalized on the recent availability of a large database, together with the emergence of specific criteria for best practices in neuroimaging studies of individual differences. We analyzed resting-state functional magnetic resonance imaging data from 884 young healthy adults in the Human Connectome Project (HCP) database. We attempted to predict personality traits from the \"Big Five\", as assessed with the NEO-FFI test, using individual functional connectivity matrices. After regressing out potential confounds (such as age, sex, handedness and fluid intelligence), we used a cross-validated framework, together with test-retest replication (across two sessions of resting-state fMRI for each subject), to quantify how well the neuroimaging data could predict each of the five personality factors. We tested three different (published) denoising strategies for the fMRI data, two inter-subject alignment and brain parcellation schemes, and three different linear models for prediction. As measurement noise is known to moderate statistical relationships, we performed final prediction analyses using average connectivity across both imaging sessions (1 h of data), with the analysis pipeline that yielded the highest predictability overall. Across all results (test/retest; 3 denoising strategies; 2 alignment schemes; 3 models), Openness to experience emerged as the only reliably predicted personality factor. Using the full hour of resting-state data and the best pipeline, we could predict Openness to experience (NEOFAC_O: r=0.24, R2=0.024) almost as well as we could predict the score on a 24-item intelligence test (PMAT24_A_CR: r=0.26, R2=0.044). Other factors (Extraversion, Neuroticism, Agreeableness and Conscientiousness) yielded weaker predictions across results that were not statistically significant under permutation testing. We also derived two superordinate personality factors (\"\" and \"{beta}\") from a principal components analysis of the NEO-FFI factor scores, thereby reducing noise and enhancing the precision of these measures of personality. We could account for 5% of the variance in the {beta} superordinate factor (r=0.27, R2=0.050), which loads highly on Openness to experience. We conclude with a discussion of the potential for predicting personality from neuroimaging data and make specific recommendations for the field.

neuroscience↗

bioRxiv2017

Causal Mapping of Emotion Networks in the Human Brain: Framework and Preliminary Findings

Emotions involve many cortical and subcortical regions, prominently including the amygdala. It remains unknown how these multiple network components interact, and it remains unknown how they cause the behavioral, autonomic, and experiential effects of emotions. Here we describe a framework for combining a novel technique, concurrent electrical stimulation with fMRI (es-fMRI), together with a novel analysis, inferring causal structure from fMRI data (causal discovery). We outline a research program for investigating human emotion with these new tools, and provide initial findings from two large resting-state datasets as well as case studies in neurosurgical patients with electrical stimulation of the amygdala. The overarching goal is to use causal discovery methods on fMRI data to infer causal graphical models of how brain regions interact, and then to further constrain these models with direct stimulation of specific brain regions and concurrent fMRI. We conclude by discussing limitations and future extensions. The approach could yield anatomical hypotheses about brain connectivity, motivate rational strategies for treating mood disorders with deep brain stimulation, and could be extended to animal studies that use combined optogenetic fMRI.

neuroscience↗