Prediction as a humanitarian and pragmatic contribution from human cognitive neuroscience

Altered functional brain connectivity, efficiency, and information flow associated with brain fog after mild to moderate COVID-19 infection

COVID-19 is associated with increased risk for cognitive decline but very little is known regarding the neural mechanisms of this risk. We enrolled 49 adults (55% female, mean age = 30.7 ± 8.7), 25 with and 24 without a history of COVID-19 infection. We administered standardized tests of cognitive function and acquired brain connectivity data using MRI. The COVID-19 group demonstrated significantly lower cognitive function (W = 475, p < 0.001, effect size r = 0.58) and lower functional connectivity in multiple brain regions (mean t = 3.47 ±0.36, p = 0.03, corrected, effect size d = 0.92 to 1.5). Hypo-connectivity of these regions was inversely correlated with subjective cognitive function and directly correlated with fatigue (p < 0.05, corrected). These regions demonstrated significantly reduced local efficiency (p < 0.026, corrected) and altered effective connectivity (p < 0.001, corrected). COVID-19 may have a widespread effect on the functional connectome characterized by lower functional connectivity and altered patterns of information processing efficiency and effective information flow. This may serve as an adaptation to the pathology of SARS-CoV-2 wherein the brain can continue functioning at near expected objective levels, but patients experience lowered efficiency as brain fog.

MRI economics: Balancing sample size and scan duration in brain wide association studies

A pervasive dilemma in neuroimaging is whether to prioritize sample size or scan time given fixed resources. Here, we systematically investigate this trade-off in the context of brain-wide association studies (BWAS) using functional magnetic resonance imaging (fMRI). We find that total scan duration (sample size x scan time per participant) robustly explains individual-level phenotypic prediction accuracy via a logarithmic model, suggesting that sample size and scan time are broadly interchangeable up to 20-30 min of data. However, the returns of scan time diminish relative to sample size, which we explain with principled theoretical derivations. When accounting for fixed overhead costs associated with each participant (e.g., recruitment, non-imaging measures), prediction accuracy in many small-scale and some large-scale BWAS might benefit from longer scan time than typically assumed. These results generalize across phenotypic domains, scanners, acquisition protocols, racial groups, mental disorders, age groups, as well as resting-state and task-state functional connectivity. Overall, our study emphasizes the importance of scan time, which is ignored in standard power calculations. Standard power calculations maximize sample size, at the expense of scan time, which can result in sub-optimal prediction accuracies and inefficient use of resources. Our empirically informed reference is available for future study design: WEB_APPLICATION_LINK.

Negative or positive left or right? The influence of attribute label position on IAT effects in picture-word IATs and word IATs

The Implicit Association Test (IAT) is a widely used measure of implicit attitudes. Despite its application in various fields, the malleability of the IAT by different methodological factors has been shown frequently. In this article, we focus on two factors that potentially influence the IAT effect, but which have received either inconsistent or no support so far: the IAT version (i.e., picture-word IAT vs. word IAT) and the position of the attribute labels on the screen (i.e., the positive or negative label on the left side). In two experiments, we used the original flower-insect IAT to systematically analyse the effects of the position of attribute labels (i.e., the assignment of the positive or the negative attribute label to the left screen position) and the block order of compatible (e.g., flower and positive) and incompatible blocks (e.g., flower and negative) as between-subjects factors. Reliable IAT effects were observed for the picture-word IAT and the word IAT when calculating the IAT effect as a difference in the response times as well as when computing the recommended D Score as IAT outcome. Smaller IAT effects occurred in the picture-word IAT than in the word IAT, supporting existing literature. In addition, an effect of the position of the attribute labels on the screen was found in both experiments, resulting in larger IAT effects when the negative attribute label was positioned on the left. This effect also appeared when calculating the D Score. The study highlights the importance of methodical factors for the IAT outcome.

The paradoxical brain: paradoxes impact conflict perspectives through increased neural alignment

Mental perspectives can sometimes be changed by psychological interventions. For instance, when applied in the context of intergroup conflicts, interventions, such as the paradoxical thinking intervention, may unfreeze ingrained negative outgroup attitudes and thereby promote progress toward peacemaking. Yet, at present, the evaluation of interventions' impact relies almost exclusively on self-reported and behavioral measures that are informative, but are also prone to social desirability and self-presentational biases. In the present study, magnetoencephalography tracked neural alignment, before and after the paradoxical thinking intervention, during the processing of auditory narratives over the Israeli-Palestinian conflict and thereby evaluated the intervention's potential to change individuals' (n = 80) mental perspectives over the conflict. Compared to baseline, the conflict-targeted intervention yielded a specific significant increased neural alignment in the posterior superior temporal sulcus while processing incongruent as well as congruent political narratives of the conflict. This may be interpreted as a possible change in perspective over the conflict. The results and their interpretations are discussed in view of the critical added value of neuroimaging when assessing interventions to potentially reveal changes in mental perspectives or the way in which they are processed, even in contexts of entrenched resistance to reconsider one's ideological stance.

Determinants of exercise adherence in sedentary middle-aged and older adults

Regular exercise positively impacts neurocognitive health, particularly in aging individuals. However, low adherence, particularly among older adults, hinders the adoption of exercise routines. While brain plasticity mechanisms largely support the cognitive benefits of exercise, the link between physiological and behavioral factors influencing exercise adherence remains unclear. This study aimed to explore this association in sedentary middle-aged and older adults. Thirty-one participants underwent an evaluation of cortico-motor plasticity using transcranial magnetic stimulation (TMS) to measure changes in motor-evoked potentials following intermittent theta-burst stimulation (iTBS). Health history, cardiorespiratory fitness, and exercise-related behavioral factors were also assessed. The participants engaged in a 2-month supervised aerobic exercise program, attending sessions three times a week for 60 min each, totaling 24 sessions at a moderate-to-vigorous intensity. They were divided into Completers (n = 19), who attended all sessions, and Dropouts (n = 12), who withdrew early. Completers exhibited lower smoking rates, exercise barriers, and resting heart rates compared to Dropouts. For Completers, TMS/iTBS cortico-motor plasticity was associated with better exercise adherence (r = -.53, corrected p = .019). Exploratory hypothesis-generating regression analysis suggested that post-iTBS changes (β = -7.78, p = .013) and self-efficacy (β = -.51, p = .019) may predict exercise adherence (adjusted-R2 = .44). In conclusion, this study highlights the significance of TMS/iTBS cortico-motor plasticity, self-efficacy, and cardiovascular health in exercise adherence. Given the well-established cognitive benefits of exercise, addressing sedentary behavior and enhancing self-efficacy are crucial for promoting adherence and optimizing brain health. Clinicians and researchers should prioritize assessing these variables to improve the effectiveness of exercise programs.

Multimodal frontal neuroimaging markers predict longitudinal craving reduction in abstinent individuals with heroin use disorder

The variation in improvement among individuals with addiction after abstinence is a critical issue. Here, we aimed to identify robust multimodal markers associated with high response to 8-month abstinence in the individuals with heroin use disorder (HUD) and explore whether the identified markers could be generalized to the individuals with methamphetamine use disorder (MUD). According to the median of craving changes, 53 individuals with HUD with 8-month abstinence were divided into two groups: higher craving reduction and lower craving reduction. At baseline, clinical variables, cortical thickness and subcortical volume, fractional anisotropy (FA) of fibers and resting-state functional connectivity (RSFC) were extracted. Different strategies (single metric, multimodal neuroimaging fusion and multimodal neuroimaging-clinical data fusion) were used to identify reliable features for discriminating the individuals with HUD with higher craving reduction from those with lower reduction. The generalization ability of the identified features was validated in the 21 individuals with MUD. Multimodal neuroimaging-clinical fusion features with best performance was achieved an 87.1 ± 3.89% average accuracy in individuals with HUD, with a moderate accuracy of 66.7% when generalizing to individuals with MUD. The multimodal neuroimaging features, primarily converging in frontal regions (e.g., the left superior frontal (LSF) thickness, FA of the LSF-occipital tract, and RSFC of left middle frontal-right superior temporal lobe), collectively contributed to prediction alongside dosage and attention impulsiveness. In this study, we identified the validated multimodal frontal neuroimaging markers associated with higher response to long-term abstinence and revealed insights for the neural mechanisms of addiction abstinence, contributing to clinical strategies and treatment for addiction.

Challenges and prospects in bridging precision medicine and artificial intelligence in genomic psychiatric treatment

Precision medicine is transforming psychiatric treatment by tailoring personalized healthcare interventions based on clinical, genetic, environmental, and lifestyle factors to optimize medication management. This study investigates how artificial intelligence (AI) and machine learning (ML) can address key challenges in integrating pharmacogenomics (PGx) into psychiatric care. In this integration, AI analyzes vast genomic datasets to identify genetic markers linked to psychiatric conditions. AI-driven models integrating genomic, clinical, and demographic data demonstrated high accuracy in predicting treatment outcomes for major depressive disorder and bipolar disorder. This study also examines the pressing challenges and provides strategic directions for integrating AI and ML in genomic psychiatry, highlighting the importance of ethical considerations and the need for personalized treatment. Effective implementation of AI-driven clinical decision support systems within electronic health records is crucial for translating PGx into routine psychiatric care. Future research should focus on developing enhanced AI-driven predictive models, privacy-preserving data exchange, and robust informatics systems to optimize patient outcomes and advance precision medicine in psychiatry.

Overcoming boundaries: Interdisciplinary challenges and opportunities in cognitive neuroscience

Cognitive neuroscience has considerable untapped potential to translate our understanding of brain function into applications that maintain, restore, or enhance human cognition. Complex, real-world phenomena encountered in daily life, professional contexts, and in the arts, can also be a rich source of information for better understanding cognition, which in turn can lead to advances in knowledge and health outcomes. Interdisciplinary work is needed for these bi-directional benefits to be realized. Our cognitive neuroscience team has been collaborating on several interdisciplinary projects: hardware and software development for brain stimulation, measuring human operator state in safety-critical robotics environments, and exploring emotional regulation in actors who perform traumatic narratives. Our approach is to study research questions of mutual interest in the contexts of domain-specific applications, using (and sometimes improving) the experimental tools and techniques of cognitive neuroscience. These interdisciplinary attempts are described as case studies in the present work to illustrate non-trivial challenges that come from working across traditional disciplinary boundaries. We reflect on how obstacles to interdisciplinary work can be overcome, with the goals of enriching our understanding of human cognition and amplifying the positive effects cognitive neuroscientists have on society and innovation.

Neuroimaging for precision medicine in psychiatry

Although the lifetime burden due to mental disorders is increasing, we lack tools for more precise diagnosing and treating prevalent and disabling disorders such as major depressive disorder. We lack strategies for selecting among available treatments or expediting access to new treatment options. This critical review concentrates on functional neuroimaging as a modality of measurement for precision psychiatry, focusing on major depressive and anxiety disorders. We begin by outlining evidence for the use of functional neuroimaging to stratify the heterogeneity of these disorders, based on underlying circuit dysfunction. We then review the current landscape of how functional neuroimaging-derived circuit predictors can predict treatment outcomes and clinical trajectories in depression and anxiety. Future directions for advancing clinically appliable neuroimaging measures are considered. We conclude by considering the opportunities and challenges of translating neuroimaging measures into practice. As an illustration, we highlight one approach for quantifying brain circuit function at an individual level, which could serve as a model for clinical translation.

Placebo treatment affects brain systems related to affective and cognitive processes, but not nociceptive pain

Drug treatments for pain often do not outperform placebo, and a better understanding of placebo mechanisms is needed to improve treatment development and clinical practice. In a large-scale fMRI study (N = 392) with pre-registered analyses, we tested whether placebo analgesic treatment modulates nociceptive processes, and whether its effects generalize from conditioned to unconditioned pain modalities. Placebo treatment caused robust analgesia in conditioned thermal pain that generalized to unconditioned mechanical pain. However, placebo did not decrease pain-related fMRI activity in brain measures linked to nociceptive pain, including the Neurologic Pain Signature (NPS) and spinothalamic pathway regions, with strong support for null effects in Bayes Factor analyses. In addition, surprisingly, placebo increased activity in some spinothalamic regions for unconditioned mechanical pain. In contrast, placebo reduced activity in a neuromarker associated with higher-level contributions to pain, the Stimulus Intensity Independent Pain Signature (SIIPS), and affected activity in brain regions related to motivation and value, in both pain modalities. Individual differences in behavioral analgesia were correlated with neural changes in both modalities. Our results indicate that cognitive and affective processes primarily drive placebo analgesia, and show the potential of neuromarkers for separating treatment influences on nociception from influences on evaluative processes.

Connectome predictive modeling of trait mindfulness

Introduction

Trait mindfulness refers to one's disposition or tendency to pay attention to their experiences in the present moment, in a non-judgmental and accepting way. Trait mindfulness has been robustly associated with positive mental health outcomes, but its neural underpinnings are poorly understood. Prior resting-state fMRI studies have associated trait mindfulness with within- and between-network connectivity of the default-mode (DMN), fronto-parietal (FPN), and salience networks. However, it is unclear how generalizable the findings are, how they relate to different components of trait mindfulness, and how other networks and brain areas may be involved.

Methods

To address these gaps, we conducted the largest resting-state fMRI study of trait mindfulness to-date, consisting of a pre-registered connectome predictive modeling analysis in 367 adults across three samples collected at different sites.

Results

In the model-training dataset, we did not find connections that predicted overall trait mindfulness, but we identified neural models of two mindfulness subscales, Acting with Awareness and Non-judging. Models included both positive networks (sets of pairwise connections that positively predicted mindfulness with increasing connectivity) and negative networks, which showed the inverse relationship. The Acting with Awareness and Non-judging positive network models showed distinct network representations involving FPN and DMN, respectively. The negative network models, which overlapped significantly across subscales, involved connections across the whole brain with prominent involvement of somatomotor, visual and DMN networks. Only the negative networks generalized to predict subscale scores out-of-sample, and not across both test datasets. Predictions from both models were also negatively correlated with predictions from a well-established mind-wandering connectome model.

Conclusions

We present preliminary neural evidence for a generalizable connectivity models of trait mindfulness based on specific affective and cognitive facets. However, the incomplete generalization of the models across all sites and scanners, limited stability of the models, as well as the substantial overlap between the models, underscores the difficulty of finding robust brain markers of mindfulness facets.

Amplification of positivity for depression and anxiety: Neural prediction of treatment response

Psychosocial treatments targeting the positive valence system (PVS) in depression and anxiety demonstrate efficacy in enhancing positive affect (PA), but response to treatment varies. We examined whether individual differences in neural activation to positive and negative valence incentive cues underlies differences in benefitting from a PVS-targeted treatment. Individuals with clinically elevated depression and/or anxiety (N = 88, ages 18 to 55) participated in one of two randomized, waitlist-controlled trials of Amplification of Positivity (AMP; NCT02330627, NCT03196544), a cognitive and behavioral intervention targeting the PVS. Participants completed a monetary incentive delay (MID) task during fMRI acquisition at baseline measuring neural activation to the possibility of gaining or losing money. Change in PA from before to after treatment was assessed using the Positive and Negative Affect Schedule. No significant associations were observed between baseline neural activation during gain anticipation and AMP-related changes in PA in regions of interest (striatum and insula) or whole-brain analyses. However, higher baseline striatal and insula activation during loss anticipation was associated with greater increases in PA post-AMP. This study provides preliminary evidence suggesting neural reactivity to negative valence cues may inform who stands to benefit most from treatments targeting the PVS.

Predicting creative behavior using resting-state electroencephalography

Neuroscience research has shown that specific brain patterns can relate to creativity during multiple tasks but also at rest. Nevertheless, the electrophysiological correlates of a highly creative brain remain largely unexplored. This study aims to uncover resting-state networks related to creative behavior using high-density electroencephalography (HD-EEG) and to test whether the strength of functional connectivity within these networks could predict individual creativity in novel subjects. We acquired resting state HD-EEG data from 90 healthy participants who completed a creative behavior inventory. We then employed connectome-based predictive modeling; a machine-learning technique that predicts behavioral measures from brain connectivity features. Using a support vector regression, our results reveal functional connectivity patterns related to high and low creativity, in the gamma frequency band (30-45 Hz). In leave-one-out cross-validation, the combined model of high and low networks predicts individual creativity with very good accuracy (r = 0.36, p = 0.00045). Furthermore, the model's predictive power is established through external validation on an independent dataset (N = 41), showing a statistically significant correlation between observed and predicted creativity scores (r = 0.35, p = 0.02). These findings reveal large-scale networks that could predict creative behavior at rest, providing a crucial foundation for developing HD-EEG-network-based markers of creativity.

Semantic associative abilities and executive control functions predict novelty and appropriateness of idea generation

Novelty and appropriateness are two fundamental components of creativity. However, the way in which novelty and appropriateness are separated at behavioral and neural levels remains poorly understood. In the present study, we aim to distinguish behavioral and neural bases of novelty and appropriateness of creative idea generation. In alignment with two established theories of creative thinking, which respectively, emphasize semantic association and executive control, behavioral results indicate that novelty relies more on associative abilities, while appropriateness relies more on executive functions. Next, employing a connectome predictive modeling (CPM) approach in resting-state fMRI data, we define two functional network-based models-dominated by interactions within the default network and by interactions within the limbic network-that respectively, predict novelty and appropriateness (i.e., cross-brain prediction). Furthermore, the generalizability and specificity of the two functional connectivity patterns are verified in additional resting-state fMRI and task fMRI. Finally, the two functional connectivity patterns, respectively mediate the relationship between semantic association/executive control and novelty/appropriateness. These findings provide global and predictive distinctions between novelty and appropriateness in creative idea generation.

Brain activity during reappraisal and associations with psychotherapy response in social anxiety and major depression: a randomized trial

BACKGROUND

Cognitive behavioral therapy (CBT) is an effective treatment for patients with social anxiety disorder (SAD) or major depressive disorder (MDD), yet there is variability in clinical improvement. Though prior research suggests pre-treatment engagement of brain regions supporting cognitive reappraisal (e.g. dorsolateral prefrontal cortex [dlPFC]) foretells CBT response in SAD, it remains unknown if this extends to MDD or is specific to CBT. The current study examined associations between pre-treatment neural activity during reappraisal and clinical improvement in patients with SAD or MDD following a trial of CBT or supportive therapy (ST), a common-factors comparator arm.

METHODS

Participants were 75 treatment-seeking patients with SAD (n = 34) or MDD (n = 41) randomized to CBT (n = 40) or ST (n = 35). Before randomization, patients completed a cognitive reappraisal task during functional magnetic resonance imaging. Additionally, patients completed clinician-administered symptom measures and a self-report cognitive reappraisal measure before treatment and every 2 weeks throughout treatment.

RESULTS

Results indicated that pre-treatment neural activity during reappraisal differentially predicted CBT and ST response. Specifically, greater trajectories of symptom improvement throughout treatment were associated with less ventrolateral prefrontal cortex (vlPFC) activity for CBT patients, but more vlPFC activity for ST patients. Also, less baseline dlPFC activity corresponded with greater trajectories of self-reported reappraisal improvement, regardless of treatment arm.

CONCLUSIONS

If replicated, findings suggest individual differences in brain response during reappraisal may be transdiagnostically associated with treatment-dependent improvement in symptom severity, but improvement in subjective reappraisal following psychotherapy, more broadly.

TractGeoNet: A geometric deep learning framework for pointwise analysis of tract microstructure to predict language assessment performance

We propose a geometric deep-learning-based framework, TractGeoNet, for performing regression using diffusion magnetic resonance imaging (dMRI) tractography and associated pointwise tissue microstructure measurements. By employing a point cloud representation, TractGeoNet can directly utilize tissue microstructure and positional information from all points within a fiber tract without the need to average or bin data along the streamline as traditionally required by dMRI tractometry methods. To improve regression performance, we propose a novel loss function, the Paired-Siamese Regression loss, which encourages the model to focus on accurately predicting the relative differences between regression label scores rather than just their absolute values. In addition, to gain insight into the brain regions that contribute most strongly to the prediction results, we propose a Critical Region Localization algorithm. This algorithm identifies highly predictive anatomical regions within the white matter fiber tracts for the regression task. We evaluate the effectiveness of the proposed method by predicting individual performance on two neuropsychological assessments of language using a dataset of 20 association white matter fiber tracts from 806 subjects from the Human Connectome Project Young Adult dataset. The results demonstrate superior prediction performance of TractGeoNet compared to several popular regression models that have been applied to predict individual cognitive performance based on neuroimaging features. Of the twenty tracts studied, we find that the left arcuate fasciculus tract is the most highly predictive of the two studied language performance assessments. Within each tract, we localize critical regions whose microstructure and point information are highly and consistently predictive of language performance across different subjects and across multiple independently trained models. These critical regions are widespread and distributed across both hemispheres and all cerebral lobes, including areas of the brain considered important for language function such as superior and anterior temporal regions, pars opercularis, and precentral gyrus. Overall, TractGeoNet demonstrates the potential of geometric deep learning to enhance the study of the brain's white matter fiber tracts and to relate their structure to human traits such as language performance.

Disentangling the Individual-Shared and Individual-Specific Subspace of Altered Brain Functional Connectivity in Autism Spectrum Disorder

BACKGROUND

Despite considerable effort toward understanding the neural basis of autism spectrum disorder (ASD) using case-control analyses of resting-state functional magnetic resonance imaging data, findings are often not reproducible, largely due to biological and clinical heterogeneity among individuals with ASD. Thus, exploring the individual-shared and individual-specific altered functional connectivity (AFC) in ASD is important to understand this complex, heterogeneous disorder.

METHODS

We considered 254 individuals with ASD and 295 typically developing individuals from the Autism Brain Imaging Data Exchange to explore the individual-shared and individual-specific subspaces of AFC. First, we computed AFC matrices of individuals with ASD compared with typically developing individuals. Then, common orthogonal basis extraction was used to project AFC of ASD onto 2 subspaces: an individual-shared subspace, which represents altered connectivity patterns shared across ASD, and an individual-specific subspace, which represents the remaining individual characteristics after eliminating the individual-shared altered connectivity patterns.

RESULTS

Analysis yielded 3 common components spanning the individual-shared subspace. Common components were associated with differences of functional connectivity at the group level. AFC in the individual-specific subspace improved the prediction of clinical symptoms. The default mode network-related and cingulo-opercular network-related magnitudes of AFC in the individual-specific subspace were significantly correlated with symptom severity in social communication deficits and restricted, repetitive behaviors in ASD.

CONCLUSIONS

Our study decomposed AFC of ASD into individual-shared and individual-specific subspaces, highlighting the importance of capturing and capitalizing on individual-specific brain connectivity features for dissecting heterogeneity. Our analysis framework provides a blueprint for parsing heterogeneity in other prevalent neurodevelopmental conditions.

The use of automated and AI-driven algorithms for the detection of hippocampal sclerosis and focal cortical dysplasia

In drug-resistant epilepsy, magnetic resonance imaging (MRI) plays a central role in detecting lesions as it offers unmatched spatial resolution and whole-brain coverage. In addition, the last decade has witnessed continued developments in MRI-based computer-aided machine-learning techniques for improved diagnosis and prognosis. In this review, we focus on automated algorithms for the detection of hippocampal sclerosis and focal cortical dysplasia, particularly in cases deemed as MRI negative, with an emphasis on studies with histologically validated data. In addition, we discuss imaging-derived prognostic markers, including response to anti-seizure medication, post-surgical seizure outcome, and cognitive reserves. We also highlight the advantages and limitations of these approaches and discuss future directions toward person-centered care.

Functional and Structural Abnormalities in the Pain Network of Generalized Anxiety Disorder Patients with Pain Symptoms

Pain symptoms significantly impact the well-being and work capacity of individuals with generalized anxiety disorder (GAD), and hinder treatment and recovery. Despite existing literature focusing on the neural substrate of pain and anxiety separately, further exploration is needed to understand the possible neuroimaging mechanisms of the pain symptoms in GAD patients. We recruited 73 GAD patients and 75 matched healthy controls (HC) for clinical assessments, as well as resting-state functional and structural magnetic resonance imaging scans. We defined a pain-related network through a published meta-analysis, including the insula, thalamus, periaqueductal gray, prefrontal cortex, anterior cingulate cortex, amygdala, and hippocampus. Subsequently, we conducted the regional homogeneity (ReHo) and the gray matter volume (GMV) within the pain-related network. Correlation analysis was then employed to explore associations between abnormal regions and self-reported outcomes, assessed using the Patient Health Questionnaire-15 (PHQ-15) and pain scores. We observed significantly increased ReHo in the bilateral insula but decreased GMV in the bilateral thalamus of GAD compared to HC. Further correlation analysis revealed a positive correlation between ReHo of the left anterior insula and pain scores in GAD patients, while a respective negative correlation between GMV of the bilateral thalamus and PHQ-15 scores. In summary, GAD patients exhibit structural and functional abnormalities in pain-related networks. The enhanced ReHo in the left anterior insula is correlated with pain symptoms, which might be a crucial brain region of pain symptoms in GAD.

Whole-brain functional connectivity predicts groupwise and sex-specific tau PET in preclincal Alzheimer's disease

Preclinical Alzheimer's disease, characterized by the initial accumulation of amyloid and tau pathologies without symptoms, presents a critical opportunity for early intervention. Yet, the interplay between these pathological markers and the functional connectome during this window remains understudied. We therefore set out to elucidate the relationship between the functional connectome and amyloid and tau, as assessed by PET imaging, in individuals with preclinical AD using connectome-based predictive modeling (CPM). We found that functional connectivity predicts tau PET, outperforming amyloid PET models. These models were predominantly governed by linear relationships between functional connectivity and tau. Tau models demonstrated a stronger correlation to global connectivity than underlying tau PET. Furthermore, we identify sex-based differences in the ability to predict regional tau, without any underlying differences in tau PET or global connectivity. Taken together, these results suggest tau is more closely coupled to functional connectivity than amyloid in preclinical disease, and that multimodal predictive modeling approaches stand to identify unique relationships that any one modality may be insufficient to discern.

Brain templates for Chinese babies from newborn to three months of age

The infant brain develops rapidly and this area of research has great clinical implications. Neurodevelopmental disorders such as autism and developmental delay have their origins, potentially, in abnormal early brain maturation. Searching for potential early neural markers requires a priori knowledge about infant brain development and anatomy. One of the most common methods of characterizing brain features requires normalization of individual images into a standard stereotactic space and conduct of group-based analyses in this space. A population representative brain template is critical for these population-based studies. Little research is available on constructing brain templates for typical developing Chinese infants. In the present work, a total of 120 babies from 5 to 89 days of age were included with high resolution structural magnetic resonance imaging scans. T1-weighted and T2-weighted templates were constructed using an unbiased registration approach for babies from newborn to 3 months of age. Age-specific templates were also estimated for babies aged at 0, 1, 2 and 3 months old. Then we conducted a series of evaluations and statistical analyses over whole tissue segmentations and brain parcellations. Compared to the use of population mismatched templates, using our established templates resulted in lower deformation energy to transform individual images into the template space and produced a smaller registration error, i.e., smaller standard deviation of the registered images. Significant volumetric growth was observed across total brain tissues and most of the brain regions within the first three months of age. The total brain tissues exhibited larger volumes in baby boys compared to baby girls. To the best of our knowledge, this is the first study focusing on the construction of Chinese infant brain templates. These templates can be used for investigating birth related conditions such as preterm birth, detecting neural biomarkers for neurological and neurodevelopmental disorders in Chinese populations, and exploring genetic and cultural effects on the brain.

Neural alignment during outgroup intervention predicts future change of affect towards outgroup

While social psychology studies have shown that paradoxical thinking intervention has a moderating effect on negative attitudes toward members from rival social groups (i.e. outgroup), the neural underpinnings of the intervention have not been studied. Here, we investigate this by examining neural alignment across individuals at different phases during the intervention regarding Covid-19 vaccine-supporters' attitudes against vaccine-opposers. We raise two questions: Whether neural alignment varies during the intervention, and whether it predicts a change in outgroup attitudes measured via a survey 2 days after the intervention and compared to baseline. We test the neural alignment using magnetoencephalography-recorded neural oscillations and multiset canonical correlation analysis. We find a build-up of neural alignment which emerges at the final phase of the paradoxical thinking intervention in the precuneus-a hub of mentalizing; there was no such effect in the control conditions. In parallel, we find a behavioral build-up of dissent to the interventional stimuli. These neural and behavioral patterns predict a prosocial future change in affect and actions toward the outgroup. Together, these findings reveal a new operational pattern of mentalizing on the outgroup, which can change the way individuals may feel and behave toward members of that outgroup.

Prediction of image interpretation cognitive ability under different mental workloads: a task-state fMRI study

Visual imaging experts play an important role in multiple fields, and studies have shown that the combination of functional magnetic resonance imaging and machine learning techniques can predict cognitive abilities, which provides a possible method for selecting individuals with excellent image interpretation skills. We recorded behavioral data and neural activity of 64 participants during image interpretation tasks under different workloads. Based on the comprehensive image interpretation ability, participants were divided into two groups. general linear model analysis showed that during image interpretation tasks, the high-ability group exhibited higher activation in middle frontal gyrus (MFG), fusiform gyrus, inferior occipital gyrus, superior parietal gyrus, inferior parietal gyrus, and insula compared to the low-ability group. The radial basis function Support Vector Machine (SVM) algorithm shows the most excellent performance in predicting participants' image interpretation abilities (Pearson correlation coefficient = 0.54, R2 = 0.31, MSE = 0.039, RMSE = 0.002). Variable importance analysis indicated that the activation features of the fusiform gyrus and MFG played an important role in predicting this ability. Our study revealed the neural basis related to image interpretation ability when exposed to different mental workloads. Additionally, our results demonstrated the efficacy of machine learning algorithms in extracting neural activation features to predict such ability.

Sex differences in functional connectivity and the predictive role of the connectome-based predictive model in Alzheimer's disease

Alzheimer's disease (AD) is a neurodegenerative disease characterized by cognitive decline. Sex differences in the progression of AD exist, but the neural mechanisms are not well understood. The purpose of the current study was to explore sex differences in brain functional connectivity (FC) at different stages of AD and their predictive ability on Montreal Cognitive Assessment (MoCA) scores using connectome-based predictive modeling (CPM). Resting-state functional magnetic resonance imaging was collected from 81 AD patients (44 females), 78 amnestic mild cognitive impairment patients (44 females), and 92 healthy controls (50 females). The FC analysis was conducted and the interaction effect between sex and group was investigated using two-factor variance analysis. The CPM was used to predict MoCA scores. There were sex-by-group interaction effects on FC between the left dorsolateral superior frontal gyrus and left middle temporal gyrus, left precuneus and right calcarine fissure surrounding cortex, left precuneus and left middle occipital gyrus, left middle temporal gyrus and left precentral gyrus, and between the left middle temporal gyrus and right cuneus. In the CPM, the positive network predictive model significantly predicted MoCA scores in both males and females. There were significant sex-by-group interaction effects on FC between the left precuneus and left middle occipital gyrus, and between the left middle temporal gyrus and right cuneus could predict MoCA scores in female patients. Our results suggest that there are sex differences in FC at different stages of AD. The sex-specific FC can further predict MoCA scores at individual level.

Brain connectomics improve prediction of 1-year decreased quality of life in breast cancer: A multi-voxel pattern analysis

PURPOSE

Whether brain connectomics can predict 1-year decreased Quality of Life (QoL) in patients with breast cancer are unclear. A longitudinal study was utilized to explore their prediction abilities with a multi-center sample.

METHODS

232 breast cancer patients were consecutively enrolled and 214 completed the 1-year QoL assessment (92.2%). Resting state functional magnetic resonance imaging was collected before the treatment and a multivoxel pattern analysis (MVPA) was performed to differentiate whole-brain resting-state connectivity patterns. Net Reclassification Improvement (NRI) as well as Integrated Discrimination Improvement (IDI) were calculated to estimate the incremental value of brain connectomics over conventional risk factors.

RESULTS

Paracingulate Gyrus, Superior Frontal Gyrus and Frontal Pole were three significant brain areas. Brain connectomics yielded 7.8-17.2% of AUC improvement in predicting 1-year decreased QoL. The NRI and IDI ranged from 20.27 to 54.05%, 13.21-33.34% respectively.

CONCLUSION

Brain connectomics contribute to a more accurate prediction of 1-year decreased QoL in breast cancer. Significant brain areas in the prefrontal lobe could be used as potential intervention targets (i.e., Cognitive Behavioral Group Therapy) to improve long-term QoL outcomes in breast cancer.

White matter and literacy: A dynamic system in flux

Cross-sectional studies have linked differences in white matter tissue properties to reading skills. However, past studies have reported a range of, sometimes conflicting, results. Some studies suggest that white matter properties act as individual-level traits predictive of reading skill, whereas others suggest that reading skill and white matter develop as a function of an individual's educational experience. In the present study, we tested two hypotheses: a) that diffusion properties of the white matter reflect stable brain characteristics that relate to stable individual differences in reading ability or b) that white matter is a dynamic system, linked with learning over time. To answer these questions, we examined the relationship between white matter and reading in a five-year longitudinal dataset and a series of large-scale, single-observation, cross-sectional datasets (N = 14,249 total participants). We find that gains in reading skill correspond to longitudinal changes in the white matter. However, in the cross-sectional datasets, we find no evidence for the hypothesis that individual differences in white matter predict reading skill. These findings highlight the link between dynamic processes in the white matter and learning.

The Brain Economy: Advancing Brain Science to Better Understand the Modern Economy

The coming years are likely to be turbulent due to a myriad of factors or polycrisis, including an escalation in climate extremes, emerging public health threats, weak productivity, increases in global economic instability and further weakening in the integrity of global democracy. These formidable challenges are not exogenous to the economy but are in some cases generated by the system itself. They can be overcome, but only with far-reaching changes to global economics. Our current socio-economic paradigm is insufficient for addressing these complex challenges, let alone sustaining human development, well-being and happiness. To support the flourishing of the global population in the age of polycrisis, we need a novel, person-centred and collective paradigm. The brain economy leverages insights from neuroscience to provide a novel way of centralising the human contribution to the economy, how the economy in turn shapes our lives and positive feedbacks between the two. The brain economy is primarily based on Brain Capital, an economic asset integrating brain health and brain skills, the social, emotional, and the diversity of cognitive brain resources of individuals and communities. People with healthy brains are essential to navigate increasingly complex systems. Policies and investments that improve brain health and hence citizens' cognitive functions and boost brain performance can increase productivity, stimulate greater creativity and economic dynamism, utilise often underdeveloped intellectual resources, afford social cohesion, and create a more resilient, adaptable and sustainability-engaged population.

Multilayer meta-matching: translating phenotypic prediction models from multiple datasets to small data

Resting-state functional connectivity (RSFC) is widely used to predict phenotypic traits in individuals. Large sample sizes can significantly improve prediction accuracies. However, for studies of certain clinical populations or focused neuroscience inquiries, small-scale datasets often remain a necessity. We have previously proposed a "meta-matching" approach to translate prediction models from large datasets to predict new phenotypes in small datasets. We demonstrated large improvement of meta-matching over classical kernel ridge regression (KRR) when translating models from a single source dataset (UK Biobank) to the Human Connectome Project Young Adults (HCP-YA) dataset. In the current study, we propose two meta-matching variants ("meta-matching with dataset stacking" and "multilayer meta-matching") to translate models from multiple source datasets across disparate sample sizes to predict new phenotypes in small target datasets. We evaluate both approaches by translating models trained from five source datasets (with sample sizes ranging from 862 participants to 36,834 participants) to predict phenotypes in the HCP-YA and HCP-Aging datasets. We find that multilayer meta-matching modestly outperforms meta-matching with dataset stacking. Both meta-matching variants perform better than the original "meta-matching with stacking" approach trained only on the UK Biobank. All meta-matching variants outperform classical KRR and transfer learning by a large margin. In fact, KRR is better than classical transfer learning when less than 50 participants are available for finetuning, suggesting the difficulty of classical transfer learning in the very small sample regime. The multilayer meta-matching model is publicly available at GITHUB_LINK.

Inter-subject correlation during long narratives reveals widespread neural correlates of reading ability

Recent work using fMRI inter-subject correlation analysis has provided new information about the brain's response to video and audio narratives, particularly in frontal regions not typically activated by single words. This approach is very well suited to the study of reading, where narrative is central to natural experience. But since past reading paradigms have primarily presented single words or phrases, the influence of narrative on semantic processing in the brain - and how that influence might change with reading ability - remains largely unexplored. In this study, we presented coherent stories to adolescents and young adults with a wide range of reading abilities. The stories were presented in alternating visual and auditory blocks. We used a dimensional inter-subject correlation analysis to identify regions in which better and worse readers had varying levels of consistency with other readers. This analysis identified a widespread set of brain regions in which activity timecourses were more similar among better readers than among worse readers. These differences were not detected with standard block activation analyses. Worse readers had higher correlation with better readers than with other worse readers, suggesting that the worse readers had "idiosyncratic" responses rather than using a single compensatory mechanism. Close inspection confirmed that these differences were not explained by differences in IQ or motion. These results suggest an expansion of the current view of where and how reading ability is reflected in the brain, and in doing so, they establish inter-subject correlation as a sensitive tool for future studies of reading disorders.

Risk Assessment of Maladaptive Behaviors in Adolescents: Nutrition, Screen Time, Prenatal Exposure, Childhood Adversities - Adolescent Brain Cognitive Development Study

PURPOSE

We aimed to identify significant contributing factors to the risk of maladaptive behaviors, such as alcohol use disorder or obesity, in children. To achieve this, we utilized the extensive adolescent brain cognitive development data set, which encompasses a wide range of environmental, social, and nutritional factors.

METHODS

We divided our sample into equal sets (test, validation; n = 3,415 each). On exploratory factor analysis, six factor domains were identified as most significant (fat/sugar intake, screen time, and prenatal alcohol exposure, parental aggressiveness, hyperactivity, family violence, parental education, and family income) and used to stratify the children into low- (n = 975), medium- (n = 967), high- (n = 977) risk groups. Regression models were used to analyze the relationship between identified risk groups, and differences in reward sensitivity, and behavioral problems at 2-year follow-up.

RESULTS

The functional magnetic resonance imaging analyses showed reduced activation in several brain regions during reward or loss anticipation in high/medium-risk (vs. low-risk) children on a monetary incentive delay task. High-risk children exhibited heightened middle frontal cortex activity when receiving large rewards. They also displayed increased impulsive and motivated reward-seeking behaviors, along with behavioral problems. These findings replicated in our validation set, and a negative correlation between middle frontal cortexthickness and impulsivity behavior was observed in high-risk children.

DISCUSSION

Our findings show altered reward function and increased impulsiveness in high-risk adolescents. This study has implications for early risk identification and the development of prevention strategies for maladaptive behaviors in children, particularly those at high risk.

Placebo treatment affects brain systems related to affective and cognitive processes, but not nociceptive pain

Placebo analgesia is a replicable and well-studied phenomenon, yet it remains unclear to what degree it includes modulation of nociceptive processes. Some studies find effects consistent with nociceptive effects, but meta-analyses show that these effects are often small. We analyzed placebo analgesia in a large fMRI study (N = 392), including placebo effects on brain responses to noxious stimuli. Placebo treatment caused robust analgesia in both conditioned thermal and unconditioned mechanical pain. Placebo did not decrease fMRI activity in nociceptive pain regions, including the Neurologic Pain Signature (NPS) and pre-registered spinothalamic pathway regions, with strong support from Bayes Factor analyses. However, placebo treatment affected activity in pre-registered analyses of a second neuromarker, the Stimulus Intensity Independent Pain Signature (SIIPS), and several associated a priori brain regions related to motivation and value, in both thermal and mechanical pain. Individual differences in behavioral analgesia were correlated with neural changes in both thermal and mechanical pain. Our results indicate that processes related to affective and cognitive aspects of pain primarily drive placebo analgesia.

Phonemic mismatch negativity mediates the association between phoneme awareness and character reading ability in young Chinese children

Poor phonological awareness is associated with greater risk for reading disability. The underlying neural mechanism of such association may lie in the brain processing of phonological information. Lower amplitude of auditory mismatch negativity (MMN) has been associated with poor phonological awareness and with the presence of reading disability. The current study recorded auditory MMN to phoneme and lexical tone contrast with odd-ball paradigm and examined whether auditory MMN mediated the associations between phonological awareness and character reading ability through a three-year longitudinal study in 78 native Mandarin-speaking kindergarten children. Hierarchical linear regression and mediation analyses showed that the effect of phoneme awareness on the character reading ability was mediated by the phonemic MMN in young Chinese children. Findings underscore the key role of phonemic MMN as the underlying neurodevelopmental mechanism linking phoneme awareness and reading ability.

Neural shifts in alpha rhythm's dual functioning during empathy maturation

INTRODUCTION

Empathy is a social-cognitive process that operates by relying mainly on the suppression of the cortical alpha rhythm. This phenomenon has been evidenced in dozens of electrophysiological studies targeting adult human subjects. Yet, recent neurodevelopmental studies indicated that at a younger age, empathy involves reversed brain responses (e.g., alpha enhancement patterns). In this multimodal study, we capture neural activity at the alpha range, and hemodynamic response and target subjects at approximately 20 years old as a unique time window in development that allows investigating both low-alpha suppression and high-alpha enhancement. We aim to further investigate the functional role of low-alpha power suppression and high-alpha power enhancement during empathy development.

METHODS

Brain data from 40 healthy individuals were recorded in two consecutive sessions of magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI) while subjects perceived vicarious physical pain or no pain.

RESULTS

MEG revealed that the alpha pattern shift during empathy happens in an all-or-none pattern: power enhancement before 18 and suppression after 18 years of age. Additionally, MEG and fMRI highlight a correspondence between high-alpha power increase and blood-oxygen-level-dependent (BOLD) decrease before 18, but low-alpha power decrease and BOLD increase after 18. Importantly, this neurodevelopmental transition was not revealed by four other measures: self-reported (a) ratings of the task stimuli, (b) ratings of naturalistic vignettes of vicarious pain, (c) trait empathy, or neural data from (d) a control neuroimaging task.

DISCUSSION

Findings suggest that at the critical age of around 18, empathy is underpinned by an all-or-none transition from high-alpha power enhancement and functional inhibition to low-alpha power suppression and functional activation in particular brain regions, possibly indicating a marker of maturation in empathic ability. This work advances a recent neurodevelopmental line of studies and provides insight into the functional maturation of empathy at the coming of age.

The challenges and prospects of brain-based prediction of behaviour

Relating individual brain patterns to behaviour is fundamental in system neuroscience. Recently, the predictive modelling approach has become increasingly popular, largely due to the recent availability of large open datasets and access to computational resources. This means that we can use machine learning models and interindividual differences at the brain level represented by neuroimaging features to predict interindividual differences in behavioural measures. By doing so, we could identify biomarkers and neural correlates in a data-driven fashion. Nevertheless, this budding field of neuroimaging-based predictive modelling is facing issues that may limit its potential applications. Here we review these existing challenges, as well as those that we anticipate as the field develops. We focus on the impacts of these challenges on brain-based predictions. We suggest potential solutions to address the resolvable challenges, while keeping in mind that some general and conceptual limitations may also underlie the predictive modelling approach.

Neural Predictors of Improvement With Cognitive Behavioral Therapy for Adolescents With Depression: An Examination of Reward Responsiveness and Emotion Regulation

Earlier depression onsets are associated with more debilitating courses and poorer life quality, highlighting the importance of effective early intervention. Many youths fail to improve with evidence-based treatments for depression, likely due in part to heterogeneity within the disorder. Multi-method assessment of individual differences in positive and negative emotion processing could improve predictions of treatment outcomes. The current study examined self-report and neurophysiological measures of reward responsiveness and emotion regulation as predictors of response to cognitive-behavioral therapy (CBT). Adolescents (14-18 years) with depression (N = 70) completed monetary reward and emotion regulation tasks while electroencephalogram (EEG) was recorded, and self-report measures of reward responsiveness, emotion regulation, and depressive symptoms at intake. Adolescents then completed a 16-session group CBT program, with depressive symptoms and clinician-rated improvement assessed across treatment. Lower reward positivity amplitudes, reflecting reduced neural reward responsiveness, predicted lower depressive symptoms with treatment. Larger late positive potential residuals during reappraisal, potentially reflecting difficulty with emotion regulation, predicted greater clinician-rated improvement. Self-report measures were not significant predictors. Results support the clinical utility of EEG measures, with impairments in positive and negative emotion processing predicting greater change with interventions that target these processes.

Connectome-based machine learning models are vulnerable to subtle data manipulations

Neuroimaging-based predictive models continue to improve in performance, yet a widely overlooked aspect of these models is "trustworthiness," or robustness to data manipulations. High trustworthiness is imperative for researchers to have confidence in their findings and interpretations. In this work, we used functional connectomes to explore how minor data manipulations influence machine learning predictions. These manipulations included a method to falsely enhance prediction performance and adversarial noise attacks designed to degrade performance. Although these data manipulations drastically changed model performance, the original and manipulated data were extremely similar (r = 0.99) and did not affect other downstream analysis. Essentially, connectome data could be inconspicuously modified to achieve any desired prediction performance. Overall, our enhancement attacks and evaluation of existing adversarial noise attacks in connectome-based models highlight the need for counter-measures that improve the trustworthiness to preserve the integrity of academic research and any potential translational applications.

Volume of hippocampus-amygdala transition area predicts outcomes of electroconvulsive therapy in major depressive disorder: high accuracy validated in two independent cohorts

BACKGROUND

Although many previous studies reported structural plasticity of the hippocampus and amygdala induced by electroconvulsive therapy (ECT) in major depressive disorder (MDD), yet the exact roles of both areas for antidepressant effects are still controversial.

METHODS

In the current study, segmentation of amygdala and hippocampal sub-regions was used to investigate the longitudinal changes of volume, the relationship between volume and antidepressant effects, and prediction performances for ECT in MDD patients before and after ECT using two independent datasets.

RESULTS

As a result, MDD patients showed selectively and consistently increased volume in the left lateral nucleus, right accessory basal nucleus, bilateral basal nucleus, bilateral corticoamygdaloid transition (CAT), bilateral paralaminar nucleus of the amygdala, and bilateral hippocampus-amygdala transition area (HATA) after ECT in both datasets, whereas marginally significant increase of volume in bilateral granule cell molecular layer of the head of dentate gyrus, the bilateral head of cornu ammonis (CA) 4, and left head of CA 3. Correlation analyses revealed that increased volume of left HATA was significantly associated with antidepressant effects after ECT. Moreover, volumes of HATA in the MDD patients before ECT could be served as potential biomarkers to predict ECT remission with the highest accuracy of 86.95% and 82.92% in two datasets (The predictive models were trained on Dataset 2 and the sensitivity, specificity and accuracy of Dataset 2 were obtained from leave-one-out-cross-validation. Thus, they were not independent and very likely to be inflated).

CONCLUSIONS

These results not only suggested that ECT could selectively induce structural plasticity of the amygdala and hippocampal sub-regions associated with antidepressant effects of ECT in MDD patients, but also provided potential biomarkers (especially HATA) for effectively and timely interventions for ECT in clinical applications.

A generalizable connectome-based marker of in-scan sustained attention in neurodiverse youth

Difficulty with attention is an important symptom in many conditions in psychiatry, including neurodiverse conditions such as autism. There is a need to better understand the neurobiological correlates of attention and leverage these findings in healthcare settings. Nevertheless, it remains unclear if it is possible to build dimensional predictive models of attentional state in a sample that includes participants with neurodiverse conditions. Here, we use 5 datasets to identify and validate functional connectome-based markers of attention. In dataset 1, we use connectome-based predictive modeling and observe successful prediction of performance on an in-scan sustained attention task in a sample of youth, including participants with a neurodiverse condition. The predictions are not driven by confounds, such as head motion. In dataset 2, we find that the attention network model defined in dataset 1 generalizes to predict in-scan attention in a separate sample of neurotypical participants performing the same attention task. In datasets 3-5, we use connectome-based identification and longitudinal scans to probe the stability of the attention network across months to years in individual participants. Our results help elucidate the brain correlates of attentional state in youth and support the further development of predictive dimensional models of other clinically relevant phenotypes.

Aberrant topology of white matter networks in patients with methamphetamine dependence and its application in support vector machine-based classification

Brain white matter (WM) networks have been widely studied in neuropsychiatric disorders. However, few studies have evaluated alterations in WM network topological organization in patients with methamphetamine (MA) dependence. Therefore, using machine learning classification methods to analyze WM network topological attributes may give new insights into patients with MA dependence. In the study, diffusion tensor imaging-based probabilistic tractography was used to map the weighted WM networks in 46 MA-dependent patients and 46 control subjects. Using graph-theoretical analyses, the global and regional topological attributes of WM networks for both groups were calculated and compared to determine inter-group differences using a permutation-based general linear model. In addition, the study used a support vector machine (SVM) learning approach to construct a classifier for discriminating subjects with MA dependence from control subjects. Relative to the control group, the MA-dependent group exhibited abnormal topological organization, as evidenced by decreased small-worldness and modularity, and increased nodal efficiency in the right medial superior temporal gyrus, right pallidum, and right ventromedial putamen; the MA-dependent group had the higher hubness scores in 25 regions, which were mainly located in the default mode network. An SVM trained with topological attributes achieved classification accuracy, sensitivity, specificity, and kappa values of 98.09% ± 2.59%, 98.24% ± 4.00%, 97.94% ± 4.26%, and 96.18% ± 5.19% for patients with MA dependence. Our results may suggest altered global WM structural networks in MA-dependent patients. Furthermore, the abnormal WM network topological attributes may provide promising features for the construction of high-efficacy classification models.

Relationship Between Prediction Accuracy and Feature Importance Reliability: an Empirical and Theoretical Study

There is significant interest in using neuroimaging data to predict behavior. The predictive models are often interpreted by the computation of feature importance, which quantifies the predictive relevance of an imaging feature. Tian and Zalesky (2021) suggest that feature importance estimates exhibit low split-half reliability, as well as a trade-off between prediction accuracy and feature importance reliability across parcellation resolutions. However, it is unclear whether the trade-off between prediction accuracy and feature importance reliability is universal. Here, we demonstrate that, with a sufficient sample size, feature importance (operationalized as Haufe-transformed weights) can achieve fair to excellent split-half reliability. With a sample size of 2600 participants, Haufe-transformed weights achieve average intra-class correlation coefficients of 0.75, 0.57 and 0.53 for cognitive, personality and mental health measures respectively. Haufe-transformed weights are much more reliable than original regression weights and univariate FC-behavior correlations. Original regression weights are not reliable even with 2600 participants. Intriguingly, feature importance reliability is strongly positively correlated with prediction accuracy across phenotypes. Within a particular behavioral domain, there is no clear relationship between prediction performance and feature importance reliability across regression models. Furthermore, we show mathematically that feature importance reliability is necessary, but not sufficient, for low feature importance error. In the case of linear models, lower feature importance error is mathematically related to lower prediction error. Therefore, higher feature importance reliability might yield lower feature importance error and higher prediction accuracy. Finally, we discuss how our theoretical results relate with the reliability of imaging features and behavioral measures. Overall, the current study provides empirical and theoretical insights into the relationship between prediction accuracy and feature importance reliability.

Task fMRI paradigms may capture more behaviorally relevant information than resting-state functional connectivity

Characterizing the optimal fMRI paradigms for detecting behaviorally relevant functional connectivity (FC) patterns is a critical step to furthering our knowledge of the neural basis of behavior. Previous studies suggested that FC patterns derived from task fMRI paradigms, which we refer to as task-based FC, are better correlated with individual differences in behavior than resting-state FC, but the consistency and generalizability of this advantage across task conditions was not fully explored. Using data from resting-state fMRI and three fMRI tasks from the Adolescent Brain Cognitive Development Study ® (ABCD), we tested whether the observed improvement in behavioral prediction power of task-based FC can be attributed to changes in brain activity induced by the task design. We decomposed the task fMRI time course of each task into the task model fit (the fitted time course of the task condition regressors from the single-subject general linear model) and the task model residuals, calculated their respective FC, and compared the behavioral prediction performance of these FC estimates to resting-state FC and the original task-based FC. The FC of the task model fit was better than the FC of the task model residual and resting-state FC at predicting a measure of general cognitive ability or two measures of performance on the fMRI tasks. The superior behavioral prediction performance of the FC of the task model fit was content-specific insofar as it was only observed for fMRI tasks that probed similar cognitive constructs to the predicted behavior of interest. To our surprise, the task model parameters, the beta estimates of the task condition regressors, were equally if not more predictive of behavioral differences than all FC measures. These results showed that the observed improvement of behavioral prediction afforded by task-based FC was largely driven by the FC patterns associated with the task design. Together with previous studies, our findings highlighted the importance of task design in eliciting behaviorally meaningful brain activation and FC patterns.

How to apply variable selection machine learning algorithms with multiply imputed data: A missing discussion

Psychological researchers often use standard linear regression to identify relevant predictors of an outcome of interest, but challenges emerge with incomplete data and growing numbers of candidate predictors. Regularization methods like the LASSO can reduce the risk of overfitting, increase model interpretability, and improve prediction in future samples; however, handling missing data when using regularization-based variable selection methods is complicated. Using listwise deletion or an ad hoc imputation strategy to deal with missing data when using regularization methods can lead to loss of precision, substantial bias, and a reduction in predictive ability. In this tutorial, we describe three approaches for fitting a LASSO when using multiple imputation to handle missing data and illustrate how to implement these approaches in practice with an applied example. We discuss implications of each approach and describe additional research that would help solidify recommendations for best practices. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Accurate machine learning prediction of sexual orientation based on brain morphology and intrinsic functional connectivity

BACKGROUND

Sexual orientation in humans represents a multilevel construct that is grounded in both neurobiological and environmental factors.

OBJECTIVE

Here, we bring to bear a machine learning approach to predict sexual orientation from gray matter volumes (GMVs) or resting-state functional connectivity (RSFC) in a cohort of 45 heterosexual and 41 homosexual participants.

METHODS

In both brain assessments, we used penalized logistic regression models and nonparametric permutation.

RESULTS

We found an average accuracy of 62% (±6.72) for predicting sexual orientation based on GMV and an average predictive accuracy of 92% (±9.89) using RSFC. Regions in the precentral gyrus, precuneus and the prefrontal cortex were significantly informative for distinguishing heterosexual from homosexual participants in both the GMV and RSFC settings.

CONCLUSIONS

These results indicate that, aside from self-reports, RSFC offers neurobiological information valuable for highly accurate prediction of sexual orientation. We demonstrate for the first time that sexual orientation is reflected in specific patterns of RSFC, which enable personalized, brain-based predictions of this highly complex human trait. While these results are preliminary, our neurobiologically based prediction framework illustrates the great value and potential of RSFC for revealing biologically meaningful and generalizable predictive patterns in the human brain.

Connectome-based predictive modeling shows sex differences in brain-based predictors of memory performance

Alzheimer's disease (AD) takes a more aggressive course in women than men, with higher prevalence and faster progression. Amnestic AD specifically targets the default mode network (DMN), which subserves short-term memory; past research shows relative hyperconnectivity in the posterior DMN in aging women. Higher reliance on this network during memory tasks may contribute to women's elevated AD risk. Here, we applied connectome-based predictive modeling (CPM), a robust linear machine-learning approach, to the Lifespan Human Connectome Project-Aging (HCP-A) dataset (n = 579). We sought to characterize sex-based predictors of memory performance in aging, with particular attention to the DMN. Models were evaluated using cross-validation both across the whole group and for each sex separately. Whole-group models predicted short-term memory performance with accuracies ranging from ρ = 0.21-0.45. The best-performing models were derived from an associative memory task-based scan. Sex-specific models revealed significant differences in connectome-based predictors for men and women. DMN activity contributed more to predicted memory scores in women, while within- and between- visual network activity contributed more to predicted memory scores in men. While men showed more segregation of visual networks, women showed more segregation of the DMN. We demonstrate that women and men recruit different circuitry when performing memory tasks, with women relying more on intra-DMN activity and men relying more on visual circuitry. These findings are consistent with the hypothesis that women draw more heavily upon the DMN for recollective memory, potentially contributing to women's elevated risk of AD.

Explainable machine learning approach to predict and explain the relationship between task-based fMRI and individual differences in cognition

Despite decades of costly research, we still cannot accurately predict individual differences in cognition from task-based functional magnetic resonance imaging (fMRI). Moreover, aiming for methods with higher prediction is not sufficient. To understand brain-cognition relationships, we need to explain how these methods draw brain information to make the prediction. Here we applied an explainable machine-learning (ML) framework to predict cognition from task-based fMRI during the n-back working-memory task, using data from the Adolescent Brain Cognitive Development (n = 3,989). We compared 9 predictive algorithms in their ability to predict 12 cognitive abilities. We found better out-of-sample prediction from ML algorithms over the mass-univariate and ordinary least squares (OLS) multiple regression. Among ML algorithms, Elastic Net, a linear and additive algorithm, performed either similar to or better than nonlinear and interactive algorithms. We explained how these algorithms drew information, using SHapley Additive explanation, eNetXplorer, Accumulated Local Effects, and Friedman's H-statistic. These explainers demonstrated benefits of ML over the OLS multiple regression. For example, ML provided some consistency in variable importance with a previous study and consistency with the mass-univariate approach in the directionality of brain-cognition relationships at different regions. Accordingly, our explainable-ML framework predicted cognition from task-based fMRI with boosted prediction and explainability over standard methodologies.

Masked features of task states found in individual brain networks

Completing complex tasks requires that we flexibly integrate information across brain areas. While studies have shown how functional networks are altered during different tasks, this work has generally focused on a cross-subject approach, emphasizing features that are common across people. Here we used extended sampling "precision" fMRI data to test the extent to which task states generalize across people or are individually specific. We trained classifiers to decode state using functional network data in single-person datasets across 5 diverse task states. Classifiers were then tested on either independent data from the same person or new individuals. Individualized classifiers were able to generalize to new participants. However, classification performance was significantly higher within a person, a pattern consistent across model types, people, tasks, feature subsets, and even for decoding very similar task conditions. Notably, these findings also replicated in a new independent dataset. These results suggest that individual-focused approaches can uncover robust features of brain states, including features obscured in cross-subject analyses. Individual-focused approaches have the potential to deepen our understanding of brain interactions during complex cognition.

Investigating cognitive neuroscience theories of human intelligence: A connectome-based predictive modeling approach

Central to modern neuroscientific theories of human intelligence is the notion that general intelligence depends on a primary brain region or network, engaging spatially localized (rather than global) neural representations. Recent findings in network neuroscience, however, challenge this assumption, providing evidence that general intelligence may depend on system-wide network mechanisms, suggesting that local representations are necessary but not sufficient to account for the neural architecture of human intelligence. Despite the importance of this key theoretical distinction, prior research has not systematically investigated the role of local versus global neural representations in predicting general intelligence. We conducted a large-scale connectome-based predictive modeling study (N = 297), administering resting-state fMRI and a comprehensive cognitive battery to evaluate the efficacy of modern neuroscientific theories of human intelligence, including spatially localized theories (Lateral Prefrontal Cortex Theory, Parieto-Frontal Integration Theory, and Multiple Demand Theory) and recent global accounts (Process Overlap Theory and Network Neuroscience Theory). The results of our study demonstrate that general intelligence can be predicted by local functional connectivity profiles but is most robustly explained by global profiles of whole-brain connectivity. Our findings further suggest that the improved efficacy of global theories is not reducible to a greater strength or number of connections, but instead results from considering both strong and weak connections that provide the basis for intelligence (as predicted by the Network Neuroscience Theory). Our results highlight the importance of considering local neural representations in the context of a global information-processing architecture, suggesting future directions for theory-driven research on system-wide network mechanisms underlying general intelligence.

An fMRI-Based Brain Marker of Individual Differences in Delay Discounting

Individual differences in delay discounting-how much we discount future compared to immediate rewards-are associated with general life outcomes, psychopathology, and obesity. Here, we use machine learning on fMRI activity during an intertemporal choice task to develop a functional brain marker of these individual differences in human adults. Training and cross-validating the marker in one dataset (Study 1, N = 110 male adults) resulted in a significant prediction-outcome correlation (r = 0.49), generalized to predict individual differences in a completely independent dataset (Study 2: N = 145 male and female adults, r = 0.45), and predicted discounting several weeks later. Out-of-sample responses of the functional brain marker, but not discounting behavior itself, differed significantly between overweight and lean individuals in both studies, and predicted fasting-state blood levels of insulin, c-peptide, and leptin in Study 1. Significant predictive weights of the marker were found in cingulate, insula, and frontoparietal areas, among others, suggesting an interplay among regions associated with valuation, conflict processing, and cognitive control. This new functional brain marker is a step toward a generalizable brain model of individual differences in delay discounting. Future studies can evaluate it as a potential transdiagnostic marker of altered decision-making in different clinical and developmental populations.SIGNIFICANCE STATEMENT People differ substantially in how much they prefer smaller sooner rewards or larger later rewards such as spending money now versus saving it for retirement. These individual differences are generally stable over time and have been related to differences in mental and bodily health. What is their neurobiological basis? We applied machine learning to brain-imaging data to identify a novel brain activity pattern that accurately predicts how much people prefer sooner versus later rewards, and which can be used as a new brain-based measure of intertemporal decision-making in future studies. The resulting functional brain marker also predicts overweight and metabolism-related blood markers, providing new insight into the possible links between metabolism and the cognitive and brain processes involved in intertemporal decision-making.