Contrasting neurofunctional correlates of face- and visuospatial-processing in children and adolescents with Williams syndrome: convergent results from four fMRI paradigms.

Understanding neurogenetic mechanisms underlying neuropsychiatric disorders such as schizophrenia and autism is complicated by their inherent clinical and genetic heterogeneity. Williams syndrome (WS), a rare neurodevelopmental condition in which both the genetic alteration (hemideletion of ~ twenty-six 7q11.23 genes) and the cognitive/behavioral profile are well-defined, offers an invaluable opportunity to delineate gene-brain-behavior relationships. People with WS are characterized by increased social drive, including particular interest in faces, together with hallmark difficulty in visuospatial processing. Prior work, primarily in adults with WS, has searched for neural correlates of these characteristics, with reports of altered fusiform gyrus function while viewing socioemotional stimuli such as faces, along with hypoactivation of the intraparietal sulcus during visuospatial processing. Here, we investigated neural function in children and adolescents with WS by using four separate fMRI paradigms, two that probe each of these two cognitive/behavioral domains. During the two visuospatial tasks, but not during the two face processing tasks, we found bilateral intraparietal sulcus hypoactivation in WS. In contrast, during both face processing tasks, but not during the visuospatial tasks, we found fusiform hyperactivation. These data not only demonstrate that previous findings in adults with WS are also present in childhood and adolescence, but also provide a clear example that genetic mechanisms can bias neural circuit function, thereby affecting behavioral traits.

Dopamine signaling enriched striatal gene set predicts striatal dopamine synthesis and physiological activity in vivo.

The polygenic architecture of schizophrenia implicates several molecular pathways involved in synaptic function. However, it is unclear how polygenic risk funnels through these pathways to translate into syndromic illness. Using tensor decomposition, we analyze gene co-expression in the caudate nucleus, hippocampus, and dorsolateral prefrontal cortex of post-mortem brain samples from 358 individuals. We identify a set of genes predominantly expressed in the caudate nucleus and associated with both clinical state and genetic risk for schizophrenia that shows dopaminergic selectivity. A higher polygenic risk score for schizophrenia parsed by this set of genes predicts greater dopamine synthesis in the striatum and greater striatal activation during reward anticipation. These results translate dopamine-linked genetic risk variation into in vivo neurochemical and hemodynamic phenotypes in the striatum that have long been implicated in the pathophysiology of schizophrenia.

Altered pubertal timing in 7q11.23 copy number variations and associated genetic mechanisms.

Pubertal timing, including age at menarche (AAM), is a heritable trait linked to lifetime health outcomes. Here, we investigate genetic mechanisms underlying AAM by combining genome-wide association study (GWAS) data with investigations of two rare genetic conditions clinically associated with altered AAM: Williams syndrome (WS), a 7q11.23 hemideletion characterized by early puberty; and duplication of the same genes (7q11.23 Duplication syndrome [Dup7]) characterized by delayed puberty. First, we confirm that AAM-derived polygenic scores in typically developing children (TD) explain a modest amount of variance in AAM (R2 = 0.09; p = 0.04). Next, we demonstrate that 7q11.23 copy number impacts AAM (WS < TD < Dup7; p = 1.2x10-8, η2 = 0.45) and pituitary volume (WS < TD < Dup7; p = 3x10-5, ηp2 = 0.2) with greater effect sizes. Finally, we relate an AAM-GWAS signal in 7q11.23 to altered expression in postmortem brains of STAG3L2 (p = 1.7x10-17), a gene we also find differentially expressed with 7q11.23 copy number (p = 0.03). Collectively, these data explicate the role of 7q11.23 in pubertal onset, with STAG3L2 and pituitary development as potential mediators.

Syntaxin1A overexpression and pain insensitivity in individuals with 7q11.23 duplication syndrome.

Genetic modifications leading to pain insensitivity phenotypes, while rare, provide invaluable insights into the molecular biology of pain and reveal targets for analgesic drugs. Pain insensitivity typically results from Mendelian loss-of-function mutations in genes expressed in nociceptive (pain-sensing) dorsal root ganglion (DRG) neurons that connect the body to the spinal cord. We document a pain insensitivity mechanism arising from gene overexpression in individuals with the rare 7q11.23 duplication syndrome (Dup7), who have 3 copies of the approximately 1.5-megabase Williams syndrome (WS) critical region. Based on parental accounts and pain ratings, people with Dup7, mainly children in this study, are pain insensitive following serious injury to skin, bones, teeth, or viscera. In contrast, diploid siblings (2 copies of the WS critical region) and individuals with WS (1 copy) show standard reactions to painful events. A converging series of human assessments and cross-species cell biological and transcriptomic studies identified 1 likely candidate in the WS critical region, STX1A, as underlying the pain insensitivity phenotype. STX1A codes for the synaptic vesicle fusion protein syntaxin1A. Excess syntaxin1A was demonstrated to compromise neuropeptide exocytosis from nociceptive DRG neurons. Taken together, these data indicate a mechanism for producing "genetic analgesia" in Dup7 and offer previously untargeted routes to pain control.

Dopamine and schizophrenia from bench to bedside: Discovery of a striatal co-expression risk gene set that predicts in vivo measures of striatal function.

Schizophrenia (SCZ) is characterized by a polygenic risk architecture implicating diverse molecular pathways important for synaptic function. However, how polygenic risk funnels through these pathways to translate into syndromic illness is unanswered. To evaluate biologically meaningful pathways of risk, we used tensor decomposition to characterize gene co-expression in post-mortem brain (of neurotypicals: N=154; patients with SCZ: N=84; and GTEX samples N=120) from caudate nucleus (CN), hippocampus (HP), and dorsolateral prefrontal cortex (DLPFC). We identified a CN-predominant gene set showing dopaminergic selectivity that was enriched for genes associated with clinical state and for genes associated with SCZ risk. Parsing polygenic risk score for SCZ based on this specific gene set (parsed-PRS), we found that greater pathway-specific SCZ risk predicted greater in vivo striatal dopamine synthesis capacity measured by [ 18 F]-FDOPA PET in three independent cohorts of neurotypicals and patients (total N=235) and greater fMRI striatal activation during reward anticipation in two additional independent neurotypical cohorts (total N=141). These results reveal a 'bench to bedside' translation of dopamine-linked genetic risk variation in driving in vivo striatal neurochemical and hemodynamic phenotypes that have long been implicated in the pathophysiology of SCZ.

PET-measured human dopamine synthesis capacity and receptor availability predict trading rewards and time-costs during foraging.

Foraging behavior requires weighing costs of time to decide when to leave one reward patch to search for another. Computational and animal studies suggest that striatal dopamine is key to this process; however, the specific role of dopamine in foraging behavior in humans is not well characterized. We use positron emission tomography (PET) imaging to directly measure dopamine synthesis capacity and D1 and D2/3 receptor availability in 57 healthy adults who complete a computerized foraging task. Using voxelwise data and principal component analysis to identify patterns of variation across PET measures, we show that striatal D1 and D2/3 receptor availability and a pattern of mesolimbic and anterior cingulate cortex dopamine function are important for adjusting the threshold for leaving a patch to explore, with specific sensitivity to changes in travel time. These findings suggest a key role for dopamine in trading reward benefits against temporal costs to modulate behavioral adaptions to changes in the reward environment critical for foraging.

Dorsal visual stream and LIMK1: hemideletion, haplotype, and enduring effects in children with Williams syndrome.

BACKGROUND: Williams syndrome (WS), a rare neurodevelopmental disorder caused by hemizygous deletion of ~ 25 genes from chromosomal band 7q11.23, affords an exceptional opportunity to study associations between a well-delineated genetic abnormality and a well-characterized neurobehavioral profile. Clinically, WS is typified by increased social drive (often termed "hypersociability") and severe visuospatial construction deficits. Previous studies have linked visuospatial problems in WS with alterations in the dorsal visual processing stream. We investigated the impacts of hemideletion and haplotype variation of LIMK1, a gene hemideleted in WS and linked to neuronal maturation and migration, on the structure and function of the dorsal stream, specifically the intraparietal sulcus (IPS), a region known to be altered in adults with WS. METHODS: We tested for IPS structural and functional changes using longitudinal MRI in a developing cohort of children with WS (76 visits from 33 participants, compared to 280 visits from 94 typically developing age- and sex-matched participants) over the age range of 5-22. We also performed MRI studies of 12 individuals with rare, shorter hemideletions at 7q11.23, all of which included LIMK1. Finally, we tested for effects of LIMK1 variation on IPS structure and imputed LIMK1 expression in two independent cohorts of healthy individuals from the general population. RESULTS: IPS structural (p < 10-4 FDR corrected) and functional (p < .05 FDR corrected) anomalies previously reported in adults were confirmed in children with WS, and, consistent with an enduring genetic mechanism, were stable from early childhood into adulthood. In the short hemideletion cohort, IPS deficits similar to those in WS were found, although effect sizes were smaller than those found in WS for both structural and functional findings. Finally, in each of the two general population cohorts stratified by LIMK1 haplotype, IPS gray matter volume (pdiscovery < 0.05 SVC, preplication = 0.0015) and imputed LIMK1 expression (pdiscovery = 10-15, preplication = 10-23) varied according to LIMK1 haplotype. CONCLUSIONS: This work offers insight into neurobiological and genetic mechanisms responsible for the WS phenotype and also more generally provides a striking example of the mechanisms by which genetic variation, acting by means of molecular effects on a neural intermediary, can influence human cognition and, in some cases, lead to neurocognitive disorders.

Spatiotemporal Alterations in Working Memory-Related Beta Band Neuromagnetic Activity of Patients With Schizophrenia On and Off Antipsychotic Medication: Investigation With MEG.

BACKGROUND AND HYPOTHESIS: We used the uniquely high combined spatial and temporal resolution of magnetoencephalography to characterize working memory (WM)-related modulation of beta band activity in neuroleptic-free patients with schizophrenia in comparison to a large sample of performance-matched healthy controls. We also tested for effects of antipsychotic medication on identified differences in these same patients. STUDY DESIGN: Inpatients with schizophrenia (n = 21) or psychotic disorder not otherwise specified (n = 4) completed N-back and control tasks during magnetoencephalography while on placebo and during antipsychotic medication treatment, in a blinded, randomized, counterbalanced manner. Healthy, performance-matched controls (N = 100) completed the same tasks. WM-related neural activation was estimated as beta band (14-30 Hz) desynchronization throughout the brain in successive 400 ms time windows. Voxel-wise statistical comparisons were performed between controls and patients while off-medication at each time window. Significant clusters resulting from this between-groups analysis were then used as regions-of-interest, the activations of which were compared between on- and off-medication conditions in patients. STUDY RESULTS: Controls showed beta-band desynchronization (activation) of a fronto-parietal network immediately preceding correct button press responses-the time associated with WM updating and task execution. Altered activation in medication-free patients occurred largely during this time, in prefrontal, parietal, and visual cortices. Medication altered patients' neural responses such that the activation time courses in these regions-of-interest more closely resembled those of controls. CONCLUSIONS: These findings demonstrate that WM-related beta band alterations in schizophrenia are time-specific and associated with neural systems targeted by antipsychotic medications. Future studies may investigate this association by examining its potential neurochemical basis.

A Combined Region- and Pixel-Based Deep Learning Approach for Quantifying Abdominal Adipose Tissue in Adolescents Using Dixon Magnetic Resonance Imaging.

BACKGROUND: The development of adipose tissue during adolescence may provide valuable insights into obesity-associated diseases. We propose an automated convolutional neural network (CNN) approach using Dixon-based magnetic resonance imaging (MRI) to quantity abdominal subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT) in children and adolescents. METHODS: 474 abdominal Dixon MRI scans of 136 young healthy volunteers (aged 8-18) were included in this study. For each scan, an axial fat-only Dixon image located at the L2-L3 disc space and another image at the L4-L5 disc space were selected for quantification. For each image, an outer and an inner region around the abdomen wall, as well as SAT and VAT pixel masks, were generated by expert readers as reference standards. A standard U-Net CNN architecture was then used to train two models: one for region segmentation and one for fat pixel classification. The performance was evaluated using the dice similarity coefficient (DSC) with fivefold cross-validation, and by Pearson correlation and the Student's t-test against the reference standards. RESULTS: For the DSC results, means and standard deviations of the outer region, inner region, SAT, and VAT comparisons were 0.974 ± 0.026, 0.997 ± 0.003, 0.981 ± 0.025, and 0.932 ± 0.047, respectively. Pearson coefficients were 1.000 for both outer and inner regions, and 1.000 and 0.982 for SAT and VAT comparisons, respectively (all p = NS). CONCLUSION: These results show that our method not only provides excellent agreement with the reference SAT and VAT measurements, but also accurate abdominal wall region segmentation. The proposed combined region- and pixel-based CNN approach provides automated abdominal wall segmentation as well as SAT and VAT quantification with Dixon MRI and enables objective longitudinal assessment of adipose tissues in children during adolescence.

Polymorphism in the ZNF804A Gene and Variation in D1 and D2/D3 Dopamine Receptor Availability in the Healthy Human Brain: A Dual Positron Emission Tomography Study.

BACKGROUND: The rs1344706 single nucleotide polymorphism in the ZNF804A gene has been associated with risk for psychosis in multiple genome-wide association studies, yet mechanisms underlying this association are not known. Given preclinical work suggesting an impact of ZNF804A on dopamine receptor gene transcription and clinical studies establishing dopaminergic dysfunction in patients with schizophrenia, we hypothesized that the ZNF804A risk single nucleotide polymorphism would be associated with variation in dopamine receptor availability in the human brain. METHODS: In this study, 72 healthy individuals genotyped for rs1344706 completed both [18F]fallypride and [11C]NNC-112 positron emission tomography scans to measure D2/D3 and D1 receptor availability, respectively. Genetic effects on estimates of binding potential for each ligand were tested first with canonical subject-specific striatal regions of interest analyses, followed by exploratory whole-brain voxelwise analyses to test for more localized striatal signals and for extrastriatal effects. RESULTS: Region of interest analyses revealed significantly less D2/D3 receptor availability in risk-allele homozygotes (TT) compared with non-risk allele carriers (G-allele carrier group: TG and GG) in the associative striatum and sensorimotor striatum, but no significant differences in striatal D1 receptor availability. CONCLUSIONS: These data suggest that ZNF804A genotype may be meaningfully linked to dopaminergic function in the human brain. The results also may provide information to guide future studies of ZNF804A-related mechanisms of schizophrenia risk.

Electrophysiological measures from human iPSC-derived neurons are associated with schizophrenia clinical status and predict individual cognitive performance.

Neurons derived from human induced pluripotent stem cells (hiPSCs) have been used to model basic cellular aspects of neuropsychiatric disorders, but the relationship between the emergent phenotypes and the clinical characteristics of donor individuals has been unclear. We analyzed RNA expression and indices of cellular function in hiPSC-derived neural progenitors and cortical neurons generated from 13 individuals with high polygenic risk scores (PRSs) for schizophrenia (SCZ) and a clinical diagnosis of SCZ, along with 15 neurotypical individuals with low PRS. We identified electrophysiological measures in the patient-derived neurons that implicated altered Na+ channel function, action potential interspike interval, and gamma-aminobutyric acid-ergic neurotransmission. Importantly, electrophysiological measures predicted cardinal clinical and cognitive features found in these SCZ patients. The identification of basic neuronal physiological properties related to core clinical characteristics of illness is a potentially critical step in generating leads for novel therapeutics.

Comparison of Transcranial Sonography and [18 F]-Fluorodopa PET Imaging in GBA1 Mutation Carriers.

BACKGROUND: Mutations in GBA1 are a common genetic risk factor for parkinsonism; however, penetrance is incomplete, and biomarkers of future progression to parkinsonism are needed. Both nigral sonography and striatal [18 F]-FDOPA PET assay dopamine system health, but their utility and coherence in this context are unclear. OBJECTIVE: The aim of this study is to evaluate the utility and coherence of these modalities in GBA1-associated parkinsonism. METHODS: A total of 34 patients with GBA1 mutations (7 with parkinsonism) underwent both transcranial studies for substantia nigra echogenicity and [18 F]-FDOPA PET to determine striatal tracer-specific uptake (Ki ). RESULTS: Larger nigral echogenic areas and reduced striatal Ki were exclusively observed in parkinsonian patients. Sonographic and PET measurements showed strong inverse correlations but only in individuals with clinical parkinsonism. CONCLUSIONS: Close correspondence between nigral echogenicity and striatal presynaptic dopamine synthesis capacity observed only in GBA1 carriers with parkinsonism provides validation that these two modalities may conjointly capture aspects of the biology underlying clinical parkinsonism but raises questions about their utility as predictive tools in at-risk subjects. © 2022 International Parkinson and Movement Disorder Society.

Clinical correlation but no elevation of striatal dopamine synthesis capacity in two independent cohorts of medication-free individuals with schizophrenia.

Dysregulation of dopamine systems has been considered a foundational driver of pathophysiological processes in schizophrenia, an illness characterized by diverse domains of symptomatology. Prior work observing elevated presynaptic dopamine synthesis capacity in some patient groups has not always identified consistent symptom correlates, and studies of affected individuals in medication-free states have been challenging to obtain. Here we report on two separate cohorts of individuals with schizophrenia spectrum illness who underwent blinded medication withdrawal and medication-free neuroimaging with [18F]-FDOPA PET to assess striatal dopamine synthesis capacity. Consistently in both cohorts, we found no significant differences between patient and matched, healthy comparison groups; however, we did identify and replicate robust inverse relationships between negative symptom severity and tracer-specific uptake widely throughout the striatum: [18F]-FDOPA specific uptake was lower in patients with a greater preponderance of negative symptoms. Complementary voxel-wise and region of interest analyses, both with and without partial volume correction, yielded consistent results. These data suggest that for some individuals, striatal hyperdopaminergia may not be a defining or enduring feature of primary psychotic illness. However, clinical differences across individuals may be significantly linked to variability in striatal dopaminergic tone. These findings call for further experimentation aimed at parsing the heterogeneity of dopaminergic systems function in schizophrenia.

Antipsychotic medication-mediated cognitive change in schizophrenia and polygenic score for cognitive ability.

Cognitive symptoms of schizophrenia are reported to be minimally responsive to treatment with antipsychotic medications, though variability exists and many prior studies have significant confounds. Here, we examined the response of cognitive symptoms to antipsychotic medications in 71 inpatients with schizophrenia on and off antipsychotic medications in a blinded, placebo-controlled, cross-over study design. Patients received either antipsychotic medication monotherapy or placebo for 4-6 weeks before switching conditions. Neuropsychological testing, including working memory, intelligence, episodic memory, and verbal fluency tests, was administered during each condition. Additionally, we assessed whether polygenic scores for cognitive ability (PGScog) related to variability in antipsychotic medication-induced changes in cognitive performance. Overall, significant changes in cognition were not observed in response to medications (p's > 0.05) except for in episodic memory (p = 0.01), which showed a medication-related improvement. Some antipsychotic medication-related cognitive changes were associated with genetic predisposition to cognitive ability: PGScog showed positive correlations with medication-induced improvements in verbal list learning (p = 0.02) and category fluency (p = 0.03). Our primary results reinforce the notion that in general, cognitive measures are minimally responsive to antipsychotic medication. However, PGScog results suggest that genetic variation may influence the ability of current treatments to affect cognitive change within this patient population. This study underscores the need for development of novel treatment options specifically targeting cognitive symptoms as well as the importance of genetic variability in treatment response for patients with schizophrenia.

Neanderthal-derived genetic variation in living humans relates to schizophrenia diagnosis, to psychotic symptom severity, and to dopamine synthesis.

Schizophrenia has been hypothesized to be a human-specific condition, but experimental approaches to testing this idea have been limited. Because Neanderthals, our closest evolutionary relatives, interbred with modern humans prior to their disappearance from the fossil record, leaving a residual echo that survives in our DNA today, we leveraged new discoveries about ancient hominid DNA to explore this hypothesis in living people in three converging ways. First, in four independent case-control datasets totaling 9,362 individuals, individuals with schizophrenia had less Neanderthal-derived genetic variation than controls (p = .044). Second, in 49 unmedicated inpatients with schizophrenia, having more Neanderthal admixture predicted less severe positive symptoms (p = .046). Finally, using 18 F-fluorodopa PET scanning in 172 healthy individuals, having greater Neanderthal introgression was significantly associated with lower dopamine synthesis capacity in the striatum and pons (p's < 2 × 10-5 ), which is fundamentally important in the pathophysiology and treatment of psychosis. These results may help to elucidate the evolutionary history of a devastating neuropsychiatric disease by supporting the notion of schizophrenia as a human-specific condition. Additionally, the relationship between Neanderthal admixture and dopamine function suggests a potential mechanism whereby Neanderthal admixture may have affected our gene pool to alter schizophrenia risk and/or course.

A generative-discriminative framework that integrates imaging, genetic, and diagnosis into coupled low dimensional space.

We propose a novel optimization framework that integrates imaging and genetics data for simultaneous biomarker identification and disease classification. The generative component of our model uses a dictionary learning framework to project the imaging and genetic data into a shared low dimensional space. We have coupled both the data modalities by tying the linear projection coefficients to the same latent space. The discriminative component of our model uses logistic regression on the projection vectors for disease diagnosis. This prediction task implicitly guides our framework to find interpretable biomarkers that are substantially different between a healthy and disease population. We exploit the interconnectedness of different brain regions by incorporating a graph regularization penalty into the joint objective function. We also use a group sparsity penalty to find a representative set of genetic basis vectors that span a low dimensional space where subjects are easily separable between patients and controls. We have evaluated our model on a population study of schizophrenia that includes two task fMRI paradigms and single nucleotide polymorphism (SNP) data. Using ten-fold cross validation, we compare our generative-discriminative framework with canonical correlation analysis (CCA) of imaging and genetics data, parallel independent component analysis (pICA) of imaging and genetics data, random forest (RF) classification, and a linear support vector machine (SVM). We also quantify the reproducibility of the imaging and genetics biomarkers via subsampling. Our framework achieves higher class prediction accuracy and identifies robust biomarkers. Moreover, the implicated brain regions and genetic variants underlie the well documented deficits in schizophrenia.

Subgenual cingulate resting regional cerebral blood flow in premenstrual dysphoric disorder: differential regulation by ovarian steroids and preliminary evidence for an association with expression of ESC/E(Z) complex genes.

Substantial evidence suggests that circulating ovarian steroids modulate behavior differently in women with PMDD than in those without this condition. However, hormonal state-related abnormalities of neural functioning in PMDD remain to be better characterized. In addition, while altered neural function in PMDD likely co-exists with alterations in intrinsic cellular function, such a relationship has not been explored. Here, we investigated the effects of ovarian steroids on basal, resting regional cerebral blood flow (rCBF) in PMDD, and, in an exploratory analysis, we tested whether the rCBF findings were linked to the expression of ESC/E(Z) genes, which form an essential ovarian steroid-regulated gene-silencing complex. Resting rCBF was measured with oxygen-15 water PET (189 PET sessions in 43 healthy women and 20 women with PMDD) during three self-as-own-control conditions: GnRH agonist (Lupron)-induced ovarian suppression, estradiol add-back, and progesterone add-back. ESC/E(Z) gene expression data were obtained from RNA-sequencing of lymphoblastoid cell lines performed in a previous study and were examined in relation to hormone-induced changes in rCBF. In the rCBF PET data, there was a significant diagnosis-by-hormone interaction in the subgenual cingulate (PFDR = 0.05), an important neuroanatomical hub for regulating affective state. Whereas control women showed no hormonally-related changes in resting rCBF, those with PMDD showed decreased resting rCBF during both estradiol (P = 0.02) and progesterone (P = 0.0002) add-back conditions. In addition, in PMDD, ESC/E(Z) gene expression correlated with the change in resting rCBF between Lupron-alone and progesterone conditions (Pearson r = -0.807, P = 0.016). This work offers a formulation of PMDD that integrates behavioral, neural circuit, and cellular mechanisms, and may provide new targets for future therapeutic interventions.

Beyond linearity in neuroimaging: Capturing nonlinear relationships with application to longitudinal studies.

The ubiquitous adoption of linearity for quantitative predictors in statistical modeling is likely attributable to its advantages of straightforward interpretation and computational feasibility. The linearity assumption may be a reasonable approximation especially when the variable is confined within a narrow range, but it can be problematic when the variable's effect is non-monotonic or complex. Furthermore, visualization and model assessment of a linear fit are usually omitted because of challenges at the whole brain level in neuroimaging. By adopting a principle of learning from the data in the presence of uncertainty to resolve the problematic aspects of conventional polynomial fitting, we introduce a flexible and adaptive approach of multilevel smoothing splines (MSS) to capture any nonlinearity of a quantitative predictor for population-level neuroimaging data analysis. With no prior knowledge regarding the underlying relationship other than a parsimonious assumption about the extent of smoothness (e.g., no sharp corners), we express the unknown relationship with a sufficient number of smoothing splines and use the data to adaptively determine the specifics of the nonlinearity. In addition to introducing the theoretical framework of MSS as an efficient approach with a counterbalance between flexibility and stability, we strive to (a) lay out the specific schemes for population-level nonlinear analyses that may involve task (e.g., contrasting conditions) and subject-grouping (e.g., patients vs controls) factors; (b) provide modeling accommodations to adaptively reveal, estimate and compare any nonlinear effects of a predictor across the brain, or to more accurately account for the effects (including nonlinear effects) of a quantitative confound; (c) offer the associated program 3dMSS to the neuroimaging community for whole-brain voxel-wise analysis as part of the AFNI suite; and (d) demonstrate the modeling approach and visualization processes with a longitudinal dataset of structural MRI scans.