Brain-behavior Relationships Research Articles

Behavior can be decoded across the cortex when individual differences are considered

Abstract Group-level analyses have typically linked behavioral signatures to a constrained set of brain areas. Here we show that two behavioral metrics — reaction time (RT) and confidence — can be decoded across the cortex when each individual is considered separately. Subjects (N=50) completed a perceptual decision-making task with confidence. We built models decoding trial-level RT and confidence separately for each subject using the activation patterns in one brain area at a time after splitting the entire cortex into 200 regions of interest (ROIs). First, we developed a simple test to determine the robustness of decoding performance, which showed that several hundred trials per subject are required for robust decoding. We then examined the decoding performance at the group and subject levels. At the group level, we replicated previous results by showing that both RT and confidence could be decoded from a small number of ROIs (12.0% and 3.5%, respectively). Critically, at the subject level, both RT and confidence could be decoded from most brain regions even after Bonferroni correction (90.0% and 72.5%, respectively). Surprisingly, we observed that many brain regions exhibited opposite brain-behavior relationships across individuals, such that, for example, higher activations predicted fast RTs in some subjects but slow RTs in others. All results were replicated in a second dataset. These findings show that behavioral signatures can be decoded from a much broader range of cortical areas than previously recognized and suggest the need to study the brain-behavior relationship at both the group and subject levels.

Extending insights from LeDoux: using movies to study subjective, clinically meaningful experiences in neuroscience.

Neuroscience research with public health relevance to emotional disorders examines brain-behavior relations. Joe LeDoux's legacy advances these efforts in ways that remain truly unique. While recognized for his basic science research, he also inspires applied researchers, guiding an agenda for clinical scientists: understanding the pathophysiology of altered subjective experiences in emotional disorders. For brain imaging, movie-watching approaches help clinicians realize this agenda due to movies' relative strength in evoking rich, meaningful subjective experiences. Here, we describe methodological advances in movie-watching paradigms that might sustain LeDoux's impact by facilitating the discovery of neural mechanisms generating complex emotional responses. Of note, while linking subjective emotion to pathophysiology is a first step, innovations in movie-watching designs, especially involving therapeutic techniques for emotional disorders, can boost clinical application. Leveraging research on pathophysiology to generate novel therapy reflects the clinical legacy sustained through Joe LeDoux's rousing career.

Using multivariate partial least squares on fNIRS data to examine load-dependent brain-behaviour relationships in aging.

Researchers implementing non-invasive neuroimaging have reported distinct load-dependent brain activity patterns in older adults compared with younger adults. Although findings are mixed, these age-related patterns are often associated with compensatory mechanisms of cognitive decline even in the absence of direct comparisons between brain activity and cognitive performance. This study investigated the effects of cognitive load on brain-behavior relationships in younger and older adults using a data-driven, multivariate partial least squares (PLS) analysis of functional near-infrared spectroscopy (fNIRS) data. We measured bilateral prefrontal brain activity in 31 older and 27 younger adults while they performed single and dual 2-back tasks. Behavioral PLS analysis was used to determine relationships between performance metrics (reaction time and error rate) and brain oxygenation (HbO) and deoxygenation (HbR) patterns across groups and task loads. Results revealed significant age-group differences in brain-behavior relationships. In younger adults, increased brain activity (i.e., increased HbO and decreased HbR) was associated with faster reaction times and better accuracy in the single task, indicating sufficient neural capacity. Conversely, older adults showed a negative correlation between HbR and error rates in the single task; however, in the dual task, they demonstrated a positive relationship between HbO and performance, indicative of compensatory mechanisms under the higher cognitive load. Overall, older adults' showed relationships with either HbR or HbO, but not both, indicating that the robustness of the relationship between brain activity and behavior varies across task load conditions. Our PLS approach revealed distinct load-dependent brain activity between age groups, providing further insight into neurocognitive aging patterns, such as compensatory mechanisms, by emphasizing the variability and complexity of brain-behavior relationships. Our findings also highlight the importance of considering task complexity and cognitive demands in interpreting age-related brain activity patterns.

Fear, learning, and the amygdala: a personal reflection in honor of Joseph LeDoux.

In honor of Joseph LeDoux's retirement from an illustrious career in science, I offer a personal reflection on how my graduate training experiences in his lab shaped my subsequent career trajectory and the development of my views on human amygdala function and the scientific enterprise. I discuss the values of openness to scientific exploration and of multi-methodological integration, both of which distinguished his laboratory over the years. I highlight the unique historical context in which the lab's foundational discoveries on the emotional brain occurred and the importance of embracing new technologies to advance an understanding of brain-behavior relationships in affective neuroscience.

Working memory related brain-behavior associations in the context of socioeconomic and psychosocial deprivation

Burgeoning neuroimaging research documents the associations between working memory (WM)-associated neural and behavioral responses. However, these associations have yielded small and inconsistent effect sizes. We hypothesize that one reason for the weakened brain-behavior associations stems from different environmental contexts. Specifically, little research has examined how exposure to adverse rearing environments accounts for variability in brain-behavior relations. Deprivation, characterized by an absence of cognitive and positive social stimulation, has been shown to compromise children's neurocognitive development. Hence, informed by an ecological approach to developmental neuroscience, the present study aims to investigate if psychosocial and socioeconomic deprivation serves as moderators in the associations between neural responses and behaviors during a WM task. Using data from the Adolescent Brain Cognitive Development study (N = 11, 878, Mage = 9.48, 47.8% female, 52.0% White), we found that psychosocial, but not socioeconomic deprivation, significantly attenuated the positive association between WM-related neural activation within the frontoparietal network and attendant behavioral performance. Specifically, children exposed to higher levels of psychosocial deprivation exhibited weaker brain-behavior relations during a WM task. This finding suggests that a certain level of neural response during cognitive tasks may correspond to different levels of behavioral performance depending on children's rearing environment, highlighting the importance of contextual factors in understanding the brain and cognitive development.

Neural Circuitry and Therapeutic Targeting of Depressive Symptoms in Schizophrenia Spectrum Disorders.

Conceptual similarities between depressive and negative symptoms complicate biomarker and intervention development. This study employed a data-driven approach to delineate the neural circuitry underlying depressive and negative symptoms in schizophrenia spectrum disorders (SSDs). Data from three studies were analyzed (157 participants with SSDs) to assess brain-behavior relationships: two neuroimaging studies and a randomized trial of repetitive transcranial magnetic stimulation (rTMS). Partial least squares correlation (PLSC) was used to investigate associations between resting-state functional connectivity and depressive and negative symptoms. Secondary analyses of rTMS trial data (active, N=37; sham, N=33) were used to assess relationships between PLSC-derived symptom profiles and treatment outcomes. PLSC identified three latent variables (LVs) relating functional brain circuitry with symptom profiles. LV1 related a general depressive symptom factor with positive associations between and within the default mode network (DMN), the frontoparietal network (FPN), and the cingulo-opercular network (CON). LV2 related negative symptoms (no depressive symptoms) via negative associations, especially between the FPN and the CON, but also between the DMN and the FPN and the CON. LV3 related a guilt and early wakening depression factor via negative rather than positive associations with the DMN, FPN, and CON. The secondary visual network had a positive association with general depressive symptoms and negative associations with guilt and negative symptoms. Active (but not sham) rTMS applied bilaterally to the dorsolateral prefrontal cortex (DLPFC) reduced general depressive but not guilt-related or negative symptoms. The results clearly differentiate the neural circuitry underlying depressive and negative symptoms, and segregated across the two-factor structure of depression in SSDs. These findings support divergent neurobiological pathways of depressive symptoms and negative symptoms in people with SSDs. As treatment options are currently limited, bilateral rTMS to the DLPFC is worth exploring further for general depressive symptoms in people with SSDs.

Dynamic brain networks in spontaneous gestural communication

Gestures accent and illustrate our communication. Although previous studies have uncovered the positive effects of gestures on communication, little is known about the specific cognitive functions of different types of gestures, or the instantaneous multi-brain dynamics. Here we used the fNIRS-based hyperscanning technique to track the brain activity of two communicators, examining regions such as the PFC and rTPJ, which are part of the mirroring and mentalizing systems. When participants collaboratively solved open-ended realistic problems, we characterised the dynamic multi-brain states linked with specific social behaviours. Results demonstrated that gestures are associated with enhanced team performance, and different gestures serve distinct cognitive functions: interactive gestures are accompanied by better team originality and a more efficient inter-brain network, while fluid gestures correlate with individual cognitive fluency and efficient intra-brain states. These findings reveal a close association between social behaviours and multi-brain networks, providing a new way to explore the brain-behaviour relationship.

B - 2 Life-Changing Treatment, Lifelong Challenges: a Case Study on the Neuropsychological Impact of Remission of Multiple Sclerosis Following Clinical-Trial Treatment

Abstract Objective The impact of neurodegenerative diseases on cognitive functioning is well documented; however less is known about the lasting impacts following remission of diseases with previously limited treatability. The present case uses neuropsychological assessment in furthering the understanding of the brain-behavior relationship in a case of multiple sclerosis (MS) in remission subsequent to clinical-trial treatment complicated by a history of medical comorbidities, childhood trauma, underlying psychiatric conditions, and preexisting learning disabilities. Method The patient is a 45-year-old right-handed male with nine years of education, diagnosed with MS (in remission) complicated by cerebrovascular disease, myocardial infarction, cerebral vascular accident, past substance-abuse, and multisource childhood trauma., He was referred for neuropsychological evaluation by his primary-care physician pursuant to learning, attention, and emotional concerns impacted by his medical history. Results Neuropsychological testing reflects global cognitive deficits evidenced by difficulties with intellectual functioning, motor abilities, complex sequencing, executive functioning, problem-solving, phonemic fluency, and variable attention, which affects learning and recall tasks. A self-report of psychological functioning revealed emotional difficulties associated with anxiety, depression, health concerns, hypersensitivity, emotional discord, a history of self-harm, cognitive inefficiency, and difficulty managing anger and past and present stressors. Findings were most consistent with cognitive decline associated with medical conditions, learning difficulties, and attention deficit disorder. Conclusions This neuropsychological case study provides an example of the long-term consequences of neurodegenerative diseases across multiple areas of neuropsychological functioning and quality of life, even when ground-breaking and innovative medical interventions result in remission. It further emphasizes the complex interactions between neurological disease and biopsychosocial factors.

Exploring the Relationship between Cortical Thickness and Cognitive Abilities: A Multi-Modal Imaging Approach

Background: Cortical thickness has been associated with cognitive abilities in prior research, but the specific relationships remain unclear. Objectives: This study aimed to further examine associations between cortical thickness in various brain regions and performance on tests of intelligence and executive function. Methods: Structural MRI scans were obtained from 65 healthy adults aged 18-35. Cortical thickness was estimated across the cortex using Free Surfer software. Participants completed standard tests measuring IQ, working memory, cognitive flexibility, and inhibitory control. Correlation and regression analyses were used to relate regional cortical thickness to cognitive scores. Results: This study showed cortical thickness in prefrontal regions positively correlated with IQ, working memory, and cognitive flexibility. No significant associations were found between cortical thickness and response inhibition. Conclusions: The findings provide evidence for links between PFC thickness and higher cognitive abilities. A multi-modal neuroimaging approach combining structural MRI and cognitive testing is useful for elucidating brain-behavior relationships. Further research with larger samples is needed to fully characterize these associations.

Task-based functional neural correlates of social cognition across autism and schizophrenia spectrum disorders

BackgroundAutism and schizophrenia spectrum disorders (SSDs) both feature atypical social cognition. Despite evidence for comparable group-level performance in lower-level emotion processing and higher-level mentalizing, limited research has examined the neural basis of social cognition across these conditions. Our goal was to compare the neural correlates of social cognition in autism, SSDs, and typically developing controls (TDCs).MethodsData came from two harmonized studies in individuals diagnosed with autism or SSDs and TDCs (aged 16–35 years), including behavioral social cognitive metrics and two functional magnetic resonance imaging (fMRI) tasks: a social mirroring Imitate/Observe (ImObs) task and the Empathic Accuracy (EA) task. Group-level comparisons, and transdiagnostic analyses incorporating social cognitive performance, were run using FSL’s PALM for each task, covarying for age and sex (1000 permutations, thresholded at p < 0.05 FWE-corrected). Exploratory region of interest (ROI)-based analyses were also conducted.ResultsImObs and EA analyses included 164 and 174 participants, respectively (autism N = 56/59, SSD N = 50/56, TDC N = 58/59). EA and both lower- and higher-level social cognition scores differed across groups. While canonical social cognitive networks were activated, no significant whole-brain or ROI-based group-level differences in neural correlates for either task were detected. Transdiagnostically, neural activity during the EA task, but not the ImObs task, was associated with lower- and higher-level social cognitive performance.LimitationsDespite attempting to match our groups on age, sex, and race, significant group differences remained. Power to detect regional brain differences is also influenced by sample size and multiple comparisons in whole-brain analyses. Our findings may not generalize to autism and SSD individuals with co-occurring intellectual disabilities.ConclusionsThe lack of whole-brain and ROI-based group-level differences identified and the dimensional EA brain-behavior relationship observed across our sample suggest that the EA task may be well-suited to target engagement in novel intervention testing. Our results also emphasize the potential utility of cross-condition approaches to better understand social cognition across autism and SSDs.

Multivariate Association Between Functional Connectivity Gradients and Cognition in Schizophrenia Spectrum Disorders

BackgroundSchizophrenia spectrum disorders (SSDs), which are characterized by social cognitive deficits, have been associated with dysconnectivity in unimodal (e.g., visual, auditory) and multimodal (e.g., default mode and frontoparietal) cortical networks. However, little is known about how such dysconnectivity is related to social and nonsocial cognition and how such brain-behavior relationships associate with clinical outcomes of SSDs. MethodsWe analyzed cognitive (nonsocial and social) measures and resting-state functional magnetic resonance imaging data from the SPINS [Social Processes Initiative in Neurobiology of the Schizophrenia(s)] study (247 stable participants with SSDs and 172 healthy control participants, ages 18–55 years). We extracted gradients from parcellated connectomes and examined the association between the first 3 gradients and the cognitive measures using partial least squares correlation (PLSC). We then correlated the PLSC dimensions with functioning and symptoms in the SSD group. ResultsThe SSD group showed significantly lower differentiation on all 3 gradients. The first PLSC dimension explained 68.53% (p < .001) of the covariance and showed a significant difference between the SSD and the control group (bootstrap p < .05). PLSC showed that all cognitive measures were associated with gradient scores of unimodal and multimodal networks (gradient 1); auditory, sensorimotor, and visual networks (gradient 2); and perceptual networks and the striatum (gradient 3), which were less differentiated in SSDs. Furthermore, the first dimension was positively correlated with negative symptoms and functioning in the SSD group. ConclusionsThese results suggest a potential role of lower differentiation of brain networks in cognitive and functional impairments in SSDs.

Health literacy is associated with cognition and everyday functioning in a consecutive clinical series of people with epilepsy in a surgical setting

ObjectiveLow health literacy is common among people with epilepsy (PWE) and may play an important role in disease management and outcomes. The current study evaluated whether health literacy is related to cognition, health, and everyday functioning in PWE. MethodsThis cross-sectional, correlational study included 25 demographically comparable healthy adults retrospectively matched to a consecutive series of 89 PWE presenting for neuropsychological evaluation in a surgical setting and who completed the Newest Vital Sign and Brief Health Literacy Screener. The PWE also completed a comprehensive neuropsychological battery and measures of quality of life and everyday functioning. ResultsPWE had significantly lower health literacy as compared to healthy adults (ps < 0.05) at a medium-to-large effect size. In analyses covarying for education and oral word reading literacy in the PWE sample, lower health literacy was independently associated with bilateral seizure onsets, greater antiseizure medication burden, poorer performance on measures of memory and information processing speed, and difficulties with self-care (ps < 0.05). SignificanceFindings suggest that PWE are at risk for low health literacy, which may be partly attributable to disrupted brain-behavior relationships and contribute to poorer everyday functioning. Future studies are needed to identify effective methods to support and improve health literacy in PWE.

Brain-Hand Function Relationships Based on Level of Grasp Function in Chronic Left-Hemisphere Stroke

Background and Objective The biomarkers of hand function may differ based on level of motor impairment after stroke. The objective of this study was to determine the relationship between resting state functional connectivity (RsFC) and unimanual contralesional hand function after stroke and whether brain-behavior relationships differ based on level of grasp function. Methods Sixty-two individuals with chronic, left-hemisphere stroke were separated into three functional levels based on Box and Blocks Test performance with the contralesional hand: Low (moved 0 blocks), Moderate (moved >0% but <90% of blocks relative to the ipsilesional hand), and High (moved ≥90% of blocks relative to the ipsilesional hand). Results RsFC in the ipsilesional and interhemispheric motor networks was reduced in the Low group compared to the Moderate and High groups. While interhemispheric RsFC correlated with hand function (grip strength and Stroke Impact Scale Hand) across the sample, contralesional RsFC correlated with hand function in the Low group and no measures of connectivity correlated with hand function in the Moderate and High groups. Linear regression modeling found that contralesional RsFC significantly predicted hand function in the Low group, while no measure correlated with hand function in the High group. Corticospinal tract integrity was the only predictor of hand function for the Moderate group and in an analysis across the entire sample. Conclusions Differences in brain-hand function relationships based on level of motor impairment may have implications for predictive models of treatment response and the development of intervention protocols aimed at improving hand function after stroke.

Estimating the total variance explained by whole-brain imaging for zero-inflated outcomes

There is a dearth of statistical models that adequately capture the total signal attributed to whole-brain imaging features. The total signal is often widely distributed across the brain, with individual imaging features exhibiting small effect sizes for predicting neurobehavioral phenotypes. The challenge of capturing the total signal is compounded by the distribution of neurobehavioral data, particularly responses to psychological questionnaires, which often feature zero-inflated, highly skewed outcomes. To close this gap, we have developed a novel Variational Bayes algorithm that characterizes the total signal captured by whole-brain imaging features for zero-inflated outcomes. Our zero-inflated variance (ZIV) estimator estimates the fraction of variance explained (FVE) and the proportion of non-null effects (PNN) from large-scale imaging data. In simulations, ZIV demonstrates superior performance over other linear models. When applied to data from the Adolescent Brain Cognitive DevelopmentSM (ABCD) Study, we found that whole-brain imaging features contribute to a larger FVE for externalizing behaviors compared to internalizing behaviors. Moreover, focusing on features contributing to the PNN, ZIV estimator localized key neurocircuitry associated with neurobehavioral traits. To the best of our knowledge, the ZIV estimator is the first specialized method for analyzing zero-inflated neuroimaging data, enhancing future studies on brain-behavior relationships and improving the understanding of neurobehavioral disorders.

Miniscope Recording Calcium Signals at Hippocampus of Mice Navigating an Odor Plume.

This protocol describes how to investigate the brain-behavior relationship in hippocampal CA1 in mice navigating an odor plume. This article provides a step-by-step protocol, including the surgery to access imaging of the hippocampus, behavioral training, miniscope GCaMP6f recording and processing of the brain and behavioral data to decode the mouse position from ROI neural activity.

Behavioral neuroscience in zebrafish: unravelling the complexity of brain-behavior relationships.

This paper reviews the utility of zebrafish (Danio rerio) as a model system for exploring neurobehavioral phenomena in preclinical research, focusing on physiological processes, disorders, and neurotoxicity biomarkers. A comprehensive review of the current literature was conducted to summarize the various behavioral characteristics of zebrafish. The study examined the etiological agents used to induce neurotoxicity and the biomarkers involved, including Aβ42, tau, MMP-13, MAO, NF-Кβ, and GFAP. Additionally, the different zebrafish study models and their responses to neurobehavioral analysis were discussed. The review identified several key biomarkers of neurotoxicity in zebrafish, each impacting different aspects of neurogenesis, inflammation, and neurodegeneration. Aβ42 was found to alter neuronal growth and stem cell function. Tau's interaction with tubulin affected microtubule stability and led to tauopathies under pathological conditions. MMP-13 was linked to oxidative assault and sensory neuron degeneration. MAO plays a role in neurotransmitter metabolism and neurotoxicity conversion. NF-Кβ was involved in pro-inflammatory pathways, and GFAP was indicative of neuroinflammation and astroglial activation. Zebrafish provide a valuable model for neurobehavioral research, adhering to the "3Rs" philosophy. Their neurotoxicity biomarkers offer insights into the mechanisms of neurogenesis, inflammation, and neurodegeneration. This model system aids in evaluating physiological and pathological conditions, enhancing our understanding of neurobehavioral phenomena and potential therapeutic interventions.

Linking functional and structural brain organisation with behaviour in healthy adults.

Multimodal data integration approaches, such as Linked Independent Component Analysis (LICA), increase sensitivity to brain-behaviour relationships and allow us to probe the relationship between modalities. Here we focus on inter-regional functional and structural organisation to determine if organisational patterns persist across modalities and if investigating multi-modality organisations provides increased sensitivity to brain-behaviour associations. We utilised multimodal magnetic resonance imaging (MRI; T1w, resting-state functional [fMRI] and diffusion weighted [DWI]) and behavioural data from the Human Connectome Project (HCP, n=676; 51% female). Unimodal features were extracted to produce individual grey matter density maps, probabilistic tractography connectivity matrices and connectopic maps from the T1w, DWI and fMRI data, respectively. DWI and fMRI analyses were restricted to subcortical regions for computational reasons. LICA was then used to integrate features, generating 100 novel independent components. Associations between these components and demographic/behavioural (n=308) variables were examined. 15 components were significantly associated with various demographic/behavioural measures. 2 components were strongly related to various measures of intoxication, driven by DWI and resemble components previously identified. Another component was driven by striatal functional data and related to working memory. A small number of components showed shared variance between structure and function but none of these displayed any significant behavioural associations. Our working memory findings provide support for the use of fMRI connectopic mapping in future research of working memory. Given the lack of behaviourally relevant shared variance between functional and structural organisation, as indexed here, we question the utility of integrating connectopic maps and tractography data.

Brain structure correlates of social information use: an exploratory machine learning approach.

Individual differences in social learning impact many important decisions, from voting behavior to polarization. Prior research has found that there are consistent and stable individual differences in social information use. However, the underlying mechanisms of these individual differences are still poorly understood. We used two complementary exploratory machine learning approaches to identify brain volumes related to individual differences in social information use. Using lasso regression and random forest regression we were able to capture linear and non-linear brain-behavior relationships. Consistent with previous studies, our results suggest there is a robust positive relationship between the volume of the left pars triangularis and social information use. Moreover, our results largely overlap with common social brain network regions, such as the medial prefrontal cortex, superior temporal sulcus, temporal parietal junction, and anterior cingulate cortex. Besides, our analyses also revealed several novel regions related to individual differences in social information use, such as the postcentral gyrus, the left caudal middle frontal gyrus, the left pallidum, and the entorhinal cortex. Together, these results provide novel insights into the neural mechanisms that underly individual differences in social learning and provide important new leads for future research.

Comparing the stability and reproducibility of brain-behavior relationships found using canonical correlation analysis and partial least squares within the ABCD sample.

Canonical correlation analysis (CCA) and partial least squares correlation (PLS) detect linear associations between two data matrices by computing latent variables (LVs) having maximal correlation (CCA) or covariance (PLS). This study compared the similarity and generalizability of CCA- and PLS-derived brain-behavior relationships. Data were accessed from the baseline Adolescent Brain Cognitive Development (ABCD) dataset (N > 9,000, 9-11 years). The brain matrix consisted of cortical thickness estimates from the Desikan-Killiany atlas. Two phenotypic scales were examined separately as the behavioral matrix; the Child Behavioral Checklist (CBCL) subscale scores and NIH Toolbox performance scores. Resampling methods were used to assess significance and generalizability of LVs. LV1 for the CBCL brain relationships was found to be significant, yet not consistently stable or reproducible, across CCA and PLS models (singular value: CCA = .13, PLS = .39, p < .001). LV1 for the NIH brain relationships showed similar relationships between CCA and PLS and was found to be stable and reproducible (singular value: CCA = .21, PLS = .43, p < .001). The current study suggests that stability and reproducibility of brain-behavior relationships identified by CCA and PLS are influenced by the statistical characteristics of the phenotypic measure used when applied to a large population-based pediatric sample.

Prelude to a Crisis

Abstract This article is an interdisciplinary or “biocultural” collaboration between a neuropsychologist and a sport historian. By examining the history of research on sport-related concussion, specifically as it relates to boxing and American football, we contend that there are important, if often overlooked, precursors to the post-2005 concussion “crisis.” In particular, we argue for greater attention to the development of sports neuropsychology, the scientific study of brain-behavior relationships within the context of sport. By bringing together the biological and the social—the neuropsychological and the historical—we offer a nuanced understanding of one of sport's most pressing problems.