Reproducible brain-wide association studies require thousands of individuals.

Magnetic resonance imaging (MRI) has transformed our understanding of the human brain through well-replicated mapping of abilities to specific structures (for example, lesion studies) and functions1-3 (for example, task functional MRI (fMRI)). Mental health research and care have yet to realize similar advances from MRI. A primary challenge has been replicating associations between inter-individual differences in brain structure or function and complex cognitive or mental health phenotypes (brain-wide association studies (BWAS)). Such BWAS have typically relied on sample sizes appropriate for classical brain mapping4 (the median neuroimaging study sample size is about 25), but potentially too small for capturing reproducible brain-behavioural phenotype associations5,6. Here we used three of the largest neuroimaging datasets currently available-with a total sample size of around 50,000 individuals-to quantify BWAS effect sizes and reproducibility as a function of sample size. BWAS associations were smaller than previously thought, resulting in statistically underpowered studies, inflated effect sizes and replication failures at typical sample sizes. As sample sizes grew into the thousands, replication rates began to improve and effect size inflation decreased. More robust BWAS effects were detected for functional MRI (versus structural), cognitive tests (versus mental health questionnaires) and multivariate methods (versus univariate). Smaller than expected brain-phenotype associations and variability across population subsamples can explain widespread BWAS replication failures. In contrast to non-BWAS approaches with larger effects (for example, lesions, interventions and within-person), BWAS reproducibility requires samples with thousands of individuals.

The Development of Socially Directed Attention: An Functional Magnetic Resonance Imaging Study in Infant Monkeys.

Socially guided visual attention, such as gaze following and joint attention, represents the building block of higher-level social cognition in primates, although their neurodevelopmental processes are still poorly understood. Atypical development of these social skills has served as early marker of autism spectrum disorder and Williams syndrome. In this study, we trace the developmental trajectories of four neural networks underlying visual and attentional social engagement in the translational rhesus monkey model. Resting-state fMRI (rs-fMRI) data and gaze following skills were collected in infant rhesus macaques from birth through 6 months of age. Developmental trajectories from subjects with both resting-state fMRI and eye-tracking data were used to explore brain-behavior relationships. Our findings indicate robust increases in functional connectivity (FC) between primary visual areas (primary visual cortex [V1] - extrastriate area 3 [V3] and V3 - middle temporal area, ventral motion areas middle temporal area - AST, as well as between TE and amygdala (AMY) as infants mature. Significant FC decreases were found in more rostral areas of the pathways, such as areas temporal area occipital part - TE in the ventral object pathway, V3 - lateral intraparietal (LIP) of the dorsal visual attention pathway and V3 - temporo-parietal area of the ventral attention pathway. No changes in FC were found between cortical areas LIP-FEF and temporo-parietal area - Area 12 of the dorsal and ventral attention pathways or between AST-AMY and AMY-insula. Developmental trajectory of gaze following revealed a period of dynamic changes with gradual increases from 1 to 2 months, followed by slight decreases from 3 to 6 months. Exploratory association findings across the 6-month period showed that infants with higher gaze following had lower FC between primary visual areas V1-V3, but higher FC in the dorsal attention areas V3-LIP, both in the right hemisphere. Together, the first 6 months of life in rhesus macaques represent a critical period for the emergence of gaze following skills associated with maturational changes in FC of socially guided attention pathways.

QSIPrep: an integrative platform for preprocessing and reconstructing diffusion MRI data.

Diffusion-weighted magnetic resonance imaging (dMRI) is the primary method for noninvasively studying the organization of white matter in the human brain. Here we introduce QSIPrep, an integrative software platform for the processing of diffusion images that is compatible with nearly all dMRI sampling schemes. Drawing on a diverse set of software suites to capitalize on their complementary strengths, QSIPrep facilitates the implementation of best practices for processing of diffusion images.

Thalamo-cortical and cerebello-cortical functional connectivity in development.

The thalamus is a critical relay center for neural pathways involving sensory, motor, and cognitive functions, including cortico-striato-thalamo-cortical and cortico-ponto-cerebello-thalamo-cortical loops. Despite the importance of these circuits, their development has been understudied. One way to investigate these pathways in human development in vivo is with functional connectivity MRI, yet few studies have examined thalamo-cortical and cerebello-cortical functional connectivity in development. Here, we used resting-state functional connectivity to measure functional connectivity in the thalamus and cerebellum with previously defined cortical functional networks in 2 separate data sets of children (7-12 years old) and adults (19-40 years old). In both data sets, we found stronger functional connectivity between the ventral thalamus and the somatomotor face cortical functional network in children compared with adults, extending previous cortico-striatal functional connectivity findings. In addition, there was more cortical network integration (i.e. strongest functional connectivity with multiple networks) in the thalamus in children than in adults. We found no developmental differences in cerebello-cortical functional connectivity. Together, these results suggest different maturation patterns in cortico-striato-thalamo-cortical and cortico-ponto-cerebellar-thalamo-cortical pathways.

Early-in-life isoflurane exposure alters resting-state functional connectivity in juvenile non-human primates.

BACKGROUND: Clinical studies suggest that anaesthesia exposure early in life affects neurobehavioural development. We designed a non-human primate (NHP) study to evaluate cognitive, behavioural, and brain functional and structural alterations after isoflurane exposure during infancy. These NHPs displayed decreased close social behaviour and increased astrogliosis in specific brain regions, most notably in the amygdala. Here we hypothesise that resting-state functional connectivity MRI can detect alterations in connectivity of brain areas that relate to these social behaviours and astrogliosis. METHODS: Imaging was performed in 2-yr-old NHPs under light anaesthesia, after early-in-life (postnatal days 6-12) exposure to 5 h of isoflurane either one or three times, or to room air. Brain images were segmented into 82 regions of interest; the amygdala and the posterior cingulate cortex were chosen for a seed-based resting-state functional connectivity MRI analysis. RESULTS: We found differences between groups in resting-state functional connectivity of the amygdala and the auditory cortices, medial premotor cortex, and posterior cingulate cortex. There were also alterations in resting-state functional connectivity between the posterior cingulate cortex and secondary auditory, polar prefrontal, and temporal cortices, and the anterior insula. Relationships were identified between resting-state functional connectivity alterations and the decrease in close social behaviour and increased astrogliosis. CONCLUSIONS: Early-in-life anaesthesia exposure in NHPs is associated with resting-state functional connectivity alterations of the amygdala and the posterior cingulate cortex with other brain regions, evident at the juvenile age of 2 yr. These changes in resting-state functional connectivity correlate with the decrease in close social behaviour and increased astrogliosis. Using resting-state functional connectivity MRI to study the neuronal underpinnings of early-in-life anaesthesia-induced behavioural alterations could facilitate development of a biomarker for anaesthesia-induced developmental neurotoxicity.

Filtering respiratory motion artifact from resting state fMRI data in infant and toddler populations.

The importance of motion correction when processing resting state functional magnetic resonance imaging (rs-fMRI) data is well-established in adult cohorts. This includes adjustments based on self-limited, large amplitude subject head motion, as well as factitious rhythmic motion induced by respiration. In adults, such respiration artifact can be effectively removed by applying a notch filter to the motion trace, resulting in higher amounts of data retained after frame censoring (e.g., "scrubbing") and more reliable correlation values. Due to the unique physiological and behavioral characteristics of infants and toddlers, rs-fMRI processing pipelines, including methods to identify and remove colored noise due to subject motion, must be appropriately modified to accurately reflect true neuronal signal. These younger cohorts are characterized by higher respiration rates and lower-amplitude head movements than adults; thus, the presence and significance of comparable respiratory artifact and the subsequent necessity of applying similar techniques remain unknown. Herein, we identify and characterize the consistent presence of respiratory artifact in rs-fMRI data collected during natural sleep in infants and toddlers across two independent cohorts (aged 8-24 months) analyzed using different pipelines. We further demonstrate how removing this artifact using an age-specific notch filter allows for both improved data quality and data retention in measured results. Importantly, this work reveals the critical need to identify and address respiratory-driven head motion in fMRI data acquired in young populations through the use of age-specific motion filters as a mechanism to optimize the accuracy of measured results in this population.

An open-access accelerated adult equivalent of the ABCD Study neuroimaging dataset (a-ABCD).

As public access to longitudinal developmental datasets like the Adolescent Brain Cognitive Development StudySM (ABCD Study®) increases, so too does the need for resources to benchmark time-dependent effects. Scan-to-scan changes observed with repeated imaging may reflect development but may also reflect practice effects, day-to-day variability in psychological states, and/or measurement noise. Resources that allow disentangling these time-dependent effects will be useful in quantifying actual developmental change. We present an accelerated adult equivalent of the ABCD Study dataset (a-ABCD) using an identical imaging protocol to acquire magnetic resonance imaging (MRI) structural, diffusion-weighted, resting-state and task-based data from eight adults scanned five times over five weeks. We report on the task-based imaging data (n = 7). In-scanner stop-signal (SST), monetary incentive delay (MID), and emotional n-back (EN-back) task behavioral performance did not change across sessions. Post-scan recognition memory for emotional n-back stimuli, however, did improve as participants became more familiar with the stimuli. Functional MRI analyses revealed that patterns of task-based activation reflecting inhibitory control in the SST, reward success in the MID task, and working memory in the EN-back task were more similar within individuals across repeated scan sessions than between individuals. Within-subject, activity was more consistent across sessions during the EN-back task than in the SST and MID task, demonstrating differences in fMRI data reliability as a function of task. The a-ABCD dataset provides a unique testbed for characterizing the reliability of brain function, structure, and behavior across imaging modalities in adulthood and benchmarking neurodevelopmental change observed in the open-access ABCD Study.

Behavioral and Neural Signatures of Working Memory in Childhood.

Working memory function changes across development and varies across individuals. The patterns of behavior and brain function that track individual differences in working memory during human development, however, are not well understood. Here, we establish associations between working memory, other cognitive abilities, and functional MRI (fMRI) activation in data from over 11,500 9- to 10-year-old children (both sexes) enrolled in the Adolescent Brain Cognitive Development (ABCD) Study, an ongoing longitudinal study in the United States. Behavioral analyses reveal robust relationships between working memory, short-term memory, language skills, and fluid intelligence. Analyses relating out-of-scanner working memory performance to memory-related fMRI activation in an emotional n-back task demonstrate that frontoparietal activity during a working memory challenge indexes working memory performance. This relationship is domain specific, such that fMRI activation related to emotion processing during the emotional n-back task, inhibitory control during a stop-signal task (SST), and reward processing during a monetary incentive delay (MID) task does not track memory abilities. Together, these results inform our understanding of individual differences in working memory in childhood and lay the groundwork for characterizing the ways in which they change across adolescence.SIGNIFICANCE STATEMENT Working memory is a foundational cognitive ability that changes over time and varies across individuals. Here, we analyze data from over 11,500 9- to 10-year-olds to establish relationships between working memory, other cognitive abilities, and frontoparietal brain activity during a working memory challenge, but not during other cognitive challenges. Our results lay the groundwork for assessing longitudinal changes in working memory and predicting later academic and other real-world outcomes.

Analysis of structural brain asymmetries in attention-deficit/hyperactivity disorder in 39 datasets.

OBJECTIVE: Some studies have suggested alterations of structural brain asymmetry in attention-deficit/hyperactivity disorder (ADHD), but findings have been contradictory and based on small samples. Here, we performed the largest ever analysis of brain left-right asymmetry in ADHD, using 39 datasets of the ENIGMA consortium. METHODS: We analyzed asymmetry of subcortical and cerebral cortical structures in up to 1,933 people with ADHD and 1,829 unaffected controls. Asymmetry Indexes (AIs) were calculated per participant for each bilaterally paired measure, and linear mixed effects modeling was applied separately in children, adolescents, adults, and the total sample, to test exhaustively for potential associations of ADHD with structural brain asymmetries. RESULTS: There was no evidence for altered caudate nucleus asymmetry in ADHD, in contrast to prior literature. In children, there was less rightward asymmetry of the total hemispheric surface area compared to controls (t = 2.1, p = .04). Lower rightward asymmetry of medial orbitofrontal cortex surface area in ADHD (t = 2.7, p = .01) was similar to a recent finding for autism spectrum disorder. There were also some differences in cortical thickness asymmetry across age groups. In adults with ADHD, globus pallidus asymmetry was altered compared to those without ADHD. However, all effects were small (Cohen's d from -0.18 to 0.18) and would not survive study-wide correction for multiple testing. CONCLUSION: Prior studies of altered structural brain asymmetry in ADHD were likely underpowered to detect the small effects reported here. Altered structural asymmetry is unlikely to provide a useful biomarker for ADHD, but may provide neurobiological insights into the trait.

Maternal Interleukin-6 Is Associated With Macaque Offspring Amygdala Development and Behavior.

Human and animal cross-sectional studies have shown that maternal levels of the inflammatory cytokine interleukin-6 (IL-6) may compromise brain phenotypes assessed at single time points. However, how maternal IL-6 associates with the trajectory of brain development remains unclear. We investigated whether maternal IL-6 levels during pregnancy relate to offspring amygdala volume development and anxiety-like behavior in Japanese macaques. Magnetic resonance imaging (MRI) was administered to 39 Japanese macaque offspring (Female: 18), providing at least one or more time points at 4, 11, 21, and 36 months of age with a behavioral assessment at 11 months of age. Increased maternal third trimester plasma IL-6 levels were associated with offspring's smaller left amygdala volume at 4 months, but with more rapid amygdala growth from 4 to 36 months. Maternal IL-6 predicted offspring anxiety-like behavior at 11 months, which was mediated by reduced amygdala volumes in the model's intercept (i.e., 4 months). The results increase our understanding of the role of maternal inflammation in the development of neurobehavioral disorders by detailing the associations of a commonly examined inflammatory indicator, IL-6, on amygdala volume growth over time, and anxiety-like behavior.

Correction of respiratory artifacts in MRI head motion estimates.

Head motion represents one of the greatest technical obstacles in magnetic resonance imaging (MRI) of the human brain. Accurate detection of artifacts induced by head motion requires precise estimation of movement. However, head motion estimates may be corrupted by artifacts due to magnetic main field fluctuations generated by body motion. In the current report, we examine head motion estimation in multiband resting state functional connectivity MRI (rs-fcMRI) data from the Adolescent Brain and Cognitive Development (ABCD) Study and comparison 'single-shot' datasets. We show that respirations contaminate movement estimates in functional MRI and that respiration generates apparent head motion not associated with functional MRI quality reductions. We have developed a novel approach using a band-stop filter that accurately removes these respiratory effects from motion estimates. Subsequently, we demonstrate that utilizing a band-stop filter improves post-processing fMRI data quality. Lastly, we demonstrate the real-time implementation of motion estimate filtering in our FIRMM (Framewise Integrated Real-Time MRI Monitoring) software package.

Obesogenic diet-associated C-reactive protein predicts reduced central dopamine and corticostriatal functional connectivity in female rhesus monkeys.

Alterations in dopamine (DA) signaling and reductions in functional connectivity (FC; a measure of temporal correlations of activity between different brain regions) within dopaminergic reward pathways are implicated in the etiology of psychopathology and have been associated with increased concentrations of inflammatory markers, including C-reactive protein. Peripheral and central inflammatory cytokines that have been shown to disrupt DA signaling and corticostriatal FC are associated with C-reactive protein, an acute phase reactant that is used translationally as a marker of systemic inflammation. One factor that can significantly increase systemic inflammation to produce neuroadaptations in reward pathways is a diet that results in fat mass accumulation (e.g. obesogenic diet). The current study in female rhesus monkeys maintained in a standard laboratory chow (n = 18) or on obesogenic diet (n = 16) for 12-months tested the hypothesis that an obesogenic diet would alter central DA and homovanillic acid (HVA) concentrations, and be associated with increased CRP concentrations and decreased FC between corticostriatal regions at 12-months following dietary intervention. We specifically assessed FC between the nucleus accumbens (NAcc) and two sub-regions of the prefrontal cortex (PFC) previously associated with CRP concentrations, the ventromedial PFC (vmPFC) and the orbitofrontal cortex (OFC), which are also involved in emotional and motivational salience assessment, and in goal-directed behavior, impulse control and the salience/value of food, respectively. Results showed that CSF DA concentrations were decreased (p = 0.002), HVA:DA ratios were increased (p = 0.016), and body mass index was increased (p = 0.047) over the 12-months of consuming an obesogenic diet. At 12-months, females maintained in the obesogenic diet exhibited higher CRP concentrations than females consuming chow-only (p = 0.008). Linear regression analyses revealed significant CRP by dietary condition interactions on DA concentrations (ß = -5.10; p = 0.017) and HVA:DA ratios (ß = 5.14; p = 0.029). Higher CRP concentrations were associated with lower CSF DA concentrations (r = -0.69; p = 0.004) and greater HVA:DA ratios only in females maintained in the obesogenic dietary condition (r = 0.58; p = 0.024). Resting-state magnetic resonance neuroimaging (rs-fMRI) in a subset of females from each diet condition (n = 8) at 12-months showed that higher CRP concentrations were associated decreased FC between the NAcc and subregions of the prefrontal cortex (PFC; p's < 0.05). Decreased FC between the NAcc and PFC subregions were also associated with lower concentrations of DA and greater HVA:DA ratios (p's < 0.05). Overall, these data suggest that increased inflammatory signaling driving heightened CRP levels may mediate the adverse consequences of obesogenic diets on DA neurochemistry and corticostriatal connectivity.

Developmental outcomes of early adverse care on amygdala functional connectivity in nonhuman primates.

Despite the strong link between childhood maltreatment and psychopathology, the underlying neurodevelopmental mechanisms are poorly understood and difficult to disentangle from heritable and prenatal factors. This study used a translational macaque model of infant maltreatment in which the adverse experience occurs in the first months of life, during intense maturation of amygdala circuits important for stress and emotional regulation. Thus, we examined the developmental impact of maltreatment on amygdala functional connectivity (FC) longitudinally, from infancy through the juvenile period. Using resting state functional magnetic resonance imaging (MRI) we performed amygdala-prefrontal cortex (PFC) region-of-interest and exploratory whole-brain amygdala FC analyses. The latter showed (a) developmental increases in amygdala FC with many regions, likely supporting increased processing of socioemotional-relevant stimuli with age; and (b) maltreatment effects on amygdala coupling with arousal and stress brain regions (locus coeruleus, laterodorsal tegmental area) that emerged with age. Maltreated juveniles showed weaker FC than controls, which was negatively associated with infant hair cortisol concentrations. Findings from the region-of-interest analysis also showed weaker amygdala FC with PFC regions in maltreated animals than controls since infancy, whereas bilateral amygdala FC was stronger in maltreated animals. These effects on amygdala FC development may underlie the poor behavioral outcomes associated with this adverse experience.

Correlated Gene Expression and Anatomical Communication Support Synchronized Brain Activity in the Mouse Functional Connectome.

Cognition and behavior depend on synchronized intrinsic brain activity that is organized into functional networks across the brain. Research has investigated how anatomical connectivity both shapes and is shaped by these networks, but not how anatomical connectivity interacts with intra-areal molecular properties to drive functional connectivity. Here, we present a novel linear model to explain functional connectivity by integrating systematically obtained measurements of axonal connectivity, gene expression, and resting-state functional connectivity MRI in the mouse brain. The model suggests that functional connectivity arises from both anatomical links and inter-areal similarities in gene expression. By estimating these effects, we identify anatomical modules in which correlated gene expression and anatomical connectivity support functional connectivity. Along with providing evidence that not all genes equally contribute to functional connectivity, this research establishes new insights regarding the biological underpinnings of coordinated brain activity measured by BOLD fMRI.SIGNIFICANCE STATEMENT Efforts at characterizing the functional connectome with fMRI have risen exponentially over the last decade. Yet despite this rise, the biological underpinnings of these functional measurements are still primarily unknown. The current report begins to fill this void by investigating the molecular underpinnings of the functional connectome through an integration of systematically obtained structural information and gene expression data throughout the rodent brain. We find that both white matter connectivity and similarity in regional gene expression relate to resting-state functional connectivity. The current report furthers our understanding of the biological underpinnings of the functional connectome and provides a linear model that can be used to streamline preclinical animal studies of disease.

Comparing directed functional connectivity between groups with confirmatory subgrouping GIMME.

Connectivity modeling in functional neuroimaging has become widely used method of analysis for understanding functional architecture. One method for deriving directed connectivity models is Group Iterative Multiple Model Estimation (GIMME; Gates and Molenaar, 2012). GIMME looks for commonalities across the sample to detect signal from noise and arrive at edges that exist across the majority in the group ("group-level edges") and individual-level edges. In this way, GIMME obtains generalizable results via the group-level edges while also allowing for between subject heterogeneity in connectivity, moving the field closer to obtaining reliable personalized connectivity maps. In this article, we present a novel extension of GIMME, confirmatory subgrouping GIMME, which estimates subgroup-level edges for a priori known groups (e.g. typically developing controls vs. clinical group). Detecting edges that consistently exist for individuals within predefined subgroups aids in interpretation of the heterogeneity in connectivity maps and allows for subgroup-specific inferences. We describe this algorithm, as well as several methods to examine the results. We present an empirical example that finds similarities and differences in resting state functional connectivity among four groups of children: typically developing controls (TDC), children with autism spectrum disorder (ASD), children with Inattentive (ADHD-I) and Combined (ADHD-C) Type ADHD. Findings from this study suggest common involvement of the left Broca's area in all the clinical groups, as well as several unique patterns of functional connectivity specific to a given disorder. Overall, the current approach and proof of principle findings highlight a novel and reliable tool for capturing heterogeneity in complex mental health disorders.

Image processing and analysis methods for the Adolescent Brain Cognitive Development Study.

The Adolescent Brain Cognitive Development (ABCD) Study is an ongoing, nationwide study of the effects of environmental influences on behavioral and brain development in adolescents. The main objective of the study is to recruit and assess over eleven thousand 9-10-year-olds and follow them over the course of 10 years to characterize normative brain and cognitive development, the many factors that influence brain development, and the effects of those factors on mental health and other outcomes. The study employs state-of-the-art multimodal brain imaging, cognitive and clinical assessments, bioassays, and careful assessment of substance use, environment, psychopathological symptoms, and social functioning. The data is a resource of unprecedented scale and depth for studying typical and atypical development. The aim of this manuscript is to describe the baseline neuroimaging processing and subject-level analysis methods used by ABCD. Processing and analyses include modality-specific corrections for distortions and motion, brain segmentation and cortical surface reconstruction derived from structural magnetic resonance imaging (sMRI), analysis of brain microstructure using diffusion MRI (dMRI), task-related analysis of functional MRI (fMRI), and functional connectivity analysis of resting-state fMRI. This manuscript serves as a methodological reference for users of publicly shared neuroimaging data from the ABCD Study.

Behavioral interventions for reducing head motion during MRI scans in children.

A major limitation to structural and functional MRI (fMRI) scans is their susceptibility to head motion artifacts. Even submillimeter movements can systematically distort functional connectivity, morphometric, and diffusion imaging results. In patient care, sedation is often used to minimize head motion, but it incurs increased costs and risks. In research settings, sedation is typically not an ethical option. Therefore, safe methods that reduce head motion are critical for improving MRI quality, especially in high movement individuals such as children and neuropsychiatric patients. We investigated the effects of (1) viewing movies and (2) receiving real-time visual feedback about head movement in 24 children (5-15 years old). Children completed fMRI scans during which they viewed a fixation cross (i.e., rest) or a cartoon movie clip, and during some of the scans they also received real-time visual feedback about head motion. Head motion was significantly reduced during movie watching compared to rest and when receiving feedback compared to receiving no feedback. However, these results depended on age, such that the effects were largely driven by the younger children. Children older than 10 years showed no significant benefit. We also found that viewing movies significantly altered the functional connectivity of fMRI data, suggesting that fMRI scans during movies cannot be equated to standard resting-state fMRI scans. The implications of these results are twofold: (1) given the reduction in head motion with behavioral interventions, these methods should be tried first for all clinical and structural MRIs in lieu of sedation; and (2) for fMRI research scans, these methods can reduce head motion in certain groups, but investigators must keep in mind the effects on functional MRI data.

Real-time motion analytics during brain MRI improve data quality and reduce costs.

Head motion systematically distorts clinical and research MRI data. Motion artifacts have biased findings from many structural and functional brain MRI studies. An effective way to remove motion artifacts is to exclude MRI data frames affected by head motion. However, such post-hoc frame censoring can lead to data loss rates of 50% or more in our pediatric patient cohorts. Hence, many scanner operators collect additional 'buffer data', an expensive practice that, by itself, does not guarantee sufficient high-quality MRI data for a given participant. Therefore, we developed an easy-to-setup, easy-to-use Framewise Integrated Real-time MRI Monitoring (FIRMM) software suite that provides scanner operators with head motion analytics in real-time, allowing them to scan each subject until the desired amount of low-movement data has been collected. Our analyses show that using FIRMM to identify the ideal scan time for each person can reduce total brain MRI scan times and associated costs by 50% or more.

Reliability and predictability of phenotype information from functional connectivity in large imaging datasets.

One of the central objectives of contemporary neuroimaging research is to create predictive models that can disentangle the connection between patterns of functional connectivity across the entire brain and various behavioral traits. Previous studies have shown that models trained to predict behavioral features from the individual's functional connectivity have modest to poor performance. In this study, we trained models that predict observable individual traits (phenotypes) and their corresponding singular value decomposition (SVD) representations - herein referred to as latent phenotypes from resting state functional connectivity. For this task, we predicted phenotypes in two large neuroimaging datasets: the Human Connectome Project (HCP) and the Philadelphia Neurodevelopmental Cohort (PNC). We illustrate the importance of regressing out confounds, which could significantly influence phenotype prediction. Our findings reveal that both phenotypes and their corresponding latent phenotypes yield similar predictive performance. Interestingly, only the first five latent phenotypes were reliably identified, and using just these reliable phenotypes for predicting phenotypes yielded a similar performance to using all latent phenotypes. This suggests that the predictable information is present in the first latent phenotypes, allowing the remainder to be filtered out without any harm in performance. This study sheds light on the intricate relationship between functional connectivity and the predictability and reliability of phenotypic information, with potential implications for enhancing predictive modeling in the realm of neuroimaging research.