Genetic variants for head size share genes and pathways with cancer.

The size of the human head is highly heritable, but genetic drivers of its variation within the general population remain unmapped. We perform a genome-wide association study on head size (N = 80,890) and identify 67 genetic loci, of which 50 are novel. Neuroimaging studies show that 17 variants affect specific brain areas, but most have widespread effects. Gene set enrichment is observed for various cancers and the p53, Wnt, and ErbB signaling pathways. Genes harboring lead variants are enriched for macrocephaly syndrome genes (37-fold) and high-fidelity cancer genes (9-fold), which is not seen for human height variants. Head size variants are also near genes preferentially expressed in intermediate progenitor cells, neural cells linked to evolutionary brain expansion. Our results indicate that genes regulating early brain and cranial growth incline to neoplasia later in life, irrespective of height. This warrants investigation of clinical implications of the link between head size and cancer.

Diffusion imaging genomics provides novel insight into early mechanisms of cerebral small vessel disease.

Cerebral small vessel disease (cSVD) is a leading cause of stroke and dementia. Genetic risk loci for white matter hyperintensities (WMH), the most common MRI-marker of cSVD in older age, were recently shown to be significantly associated with white matter (WM) microstructure on diffusion tensor imaging (signal-based) in young adults. To provide new insights into these early changes in WM microstructure and their relation with cSVD, we sought to explore the genetic underpinnings of cutting-edge tissue-based diffusion imaging markers across the adult lifespan. We conducted a genome-wide association study of neurite orientation dispersion and density imaging (NODDI) markers in young adults (i-Share study: N = 1 758, (mean[range]) 22.1[18-35] years), with follow-up in young middle-aged (Rhineland Study: N = 714, 35.2[30-40] years) and late middle-aged to older individuals (UK Biobank: N = 33 224, 64.3[45-82] years). We identified 21 loci associated with NODDI markers across brain regions in young adults. The most robust association, replicated in both follow-up cohorts, was with Neurite Density Index (NDI) at chr5q14.3, a known WMH locus in VCAN. Two additional loci were replicated in UK Biobank, at chr17q21.2 with NDI, and chr19q13.12 with Orientation Dispersion Index (ODI). Transcriptome-wide association studies showed associations of STAT3 expression in arterial and adipose tissue (chr17q21.2) with NDI, and of several genes at chr19q13.12 with ODI. Genetic susceptibility to larger WMH volume, but not to vascular risk factors, was significantly associated with decreased NDI in young adults, especially in regions known to harbor WMH in older age. Individually, seven of 25 known WMH risk loci were associated with NDI in young adults. In conclusion, we identified multiple novel genetic risk loci associated with NODDI markers, particularly NDI, in early adulthood. These point to possible early-life mechanisms underlying cSVD and to processes involving remyelination, neurodevelopment and neurodegeneration, with a potential for novel approaches to prevention.

Prediction of dementia risk from multimodal repeated measures: The added value of brain MRI biomarkers.

Abstract: The utility of brain magnetic resonance imaging (MRI) for predicting dementia is debated. We evaluated the added value of repeated brain MRI, including atrophy and cerebral small vessel disease markers, for dementia prediction. We conducted a landmark competing risk analysis in 1716 participants of the French population-based Three-City Study to predict the 5-year risk of dementia using repeated measures of 41 predictors till year 4 of follow-up. Brain MRI markers improved significantly the individual prediction of dementia after accounting for demographics, health measures, and repeated measures of cognition and functional dependency (area under the ROC curve [95% CI] improved from 0.80 [0.79 to 0.82] to 0.83 [0.81 to 0.84]). Nonetheless, accounting for the change over time through repeated MRIs had little impact on predictive abilities. These results highlight the importance of multimodal analysis to evaluate the added predictive abilities of repeated brain MRI for dementia and offer new insights into the predictive performances of various MRI markers. Highlights: We evaluated whether repeated brain volumes and cSVD markers improve dementia prediction.The 5-year prediction of dementia is slightly improved when considering brain MRI markers.Measures of hippocampus volume are the main MRI predictors of dementia.Adjusted on cognition, repeated MRI has poor added value over single MRI for dementia prediction.We utilized a longitudinal analysis that considers error-and-missing-prone predictors, and competing death.

Early detection of white matter hyperintensities using SHIVA-WMH detector.

White matter hyperintensities (WMHs) are well-established markers of cerebral small vessel disease, and are associated with an increased risk of stroke, dementia, and mortality. Although their prevalence increases with age, small and punctate WMHs have been reported with surprisingly high frequency even in young, neurologically asymptomatic adults. However, most automated methods to segment WMH published to date are not optimized for detecting small and sparse WMH. Here we present the SHIVA-WMH tool, a deep-learning (DL)-based automatic WMH segmentation tool that has been trained with manual segmentations of WMH in a wide range of WMH severity. We show that it is able to detect WMH with high efficiency in subjects with only small punctate WMH as well as in subjects with large WMHs (i.e., with confluency) in evaluation datasets from three distinct databases: magnetic resonance imaging-Share consisting of young university students, MICCAI 2017 WMH challenge dataset consisting of older patients from memory clinics, and UK Biobank with community-dwelling middle-aged and older adults. Across these three cohorts with a wide-ranging WMH load, our tool achieved voxel-level and individual lesion cluster-level Dice scores of 0.66 and 0.71, respectively, which were higher than for three reference tools tested: the lesion prediction algorithm implemented in the lesion segmentation toolbox (LPA: Schmidt), PGS tool, a DL-based algorithm and the current winner of the MICCAI 2017 WMH challenge (Park et al.), and HyperMapper tool (Mojiri Forooshani et al.), another DL-based method with high reported performance in subjects with mild WMH burden. Our tool is publicly and openly available to the research community to facilitate investigations of WMH across a wide range of severity in other cohorts, and to contribute to our understanding of the emergence and progression of WMH.

Association of retinal nerve layers thickness and brain imaging in healthy young subjects from the i-Share-Bordeaux study.

Given the anatomical and functional similarities between the retina and the brain, the retina could be a "window" for viewing brain structures. We investigated the association between retinal nerve fiber layers (peripapillary retinal nerve fiber layer, ppRNFL; macular ganglion cell-inner plexiform layer, GC-IPL; and macular ganglion cell complex, GCC), and brain magnetic resonance imaging (MRI) parameters in young health adults. We included 857 students (mean age: 23.3 years, 71.3% women) from the i-Share study. We used multivariate linear models to study the cross-sectional association of each retinal nerve layer thickness assessed by spectral-domain optical coherence tomography (SD-OCT) with structural (volumes and cortical thickness), and microstructural brain markers, assessed on MRI globally and regionally. Microstructural MRI parameters included diffusion tensor imaging (DTI) and Neurite Orientation Dispersion and Density Imaging (NODDI). On global brain analysis, thicker ppRNFL, GC-IPL and GCC were all significantly associated with patterns of diffusion metrics consistent with higher WM microstructural integrity. In regional analyses, after multiple testing corrections, our results suggested significant associations of some retinal nerve layers with brain regional gray matter occipital volumes and with diffusion MRI parameters in a region involved in the visual pathway and in regions containing associative tracts. No associations were found with global volumes or with global or regional cortical thicknesses. Results of this study suggest that some retinal nerve layers may reflect brain structures. Further studies are needed to confirm these results in young subjects.

Genomics of perivascular space burden unravels early mechanisms of cerebral small vessel disease.

Perivascular space (PVS) burden is an emerging, poorly understood, magnetic resonance imaging marker of cerebral small vessel disease, a leading cause of stroke and dementia. Genome-wide association studies in up to 40,095 participants (18 population-based cohorts, 66.3 ± 8.6 yr, 96.9% European ancestry) revealed 24 genome-wide significant PVS risk loci, mainly in the white matter. These were associated with white matter PVS already in young adults (N = 1,748; 22.1 ± 2.3 yr) and were enriched in early-onset leukodystrophy genes and genes expressed in fetal brain endothelial cells, suggesting early-life mechanisms. In total, 53% of white matter PVS risk loci showed nominally significant associations (27% after multiple-testing correction) in a Japanese population-based cohort (N = 2,862; 68.3 ± 5.3 yr). Mendelian randomization supported causal associations of high blood pressure with basal ganglia and hippocampal PVS, and of basal ganglia PVS and hippocampal PVS with stroke, accounting for blood pressure. Our findings provide insight into the biology of PVS and cerebral small vessel disease, pointing to pathways involving extracellular matrix, membrane transport and developmental processes, and the potential for genetically informed prioritization of drug targets.

Genomic Studies Across the Lifespan Point to Early Mechanisms Determining Subcortical Volumes.

BACKGROUND: Subcortical brain structures play a key role in pathological processes of age-related neurodegenerative disorders. Mounting evidence also suggests that early-life factors may have an impact on the development of common late-life neurological diseases, including genetic factors that can influence both brain maturation and neurodegeneration. METHODS: Using large population-based brain imaging datasets across the lifespan (N ≤ 40,628), we aimed to 1) estimate the heritability of subcortical volumes in young (18-35 years), middle (35-65 years), and older (65+ years) age, and their genetic correlation across age groups; 2) identify whether genetic loci associated with subcortical volumes in older persons also show associations in early adulthood, and explore underlying genes using transcriptome-wide association studies; and 3) explore their association with neurological phenotypes. RESULTS: Heritability of subcortical volumes consistently decreased with increasing age. Genetic risk scores for smaller caudate nucleus, putamen, and hippocampus volume in older adults were associated with smaller volumes in young adults. Individually, 10 loci associated with subcortical volumes in older adults also showed associations in young adults. Within these loci, transcriptome-wide association studies showed that expression of several genes in brain tissues (especially MYLK2 and TUFM) was associated with subcortical volumes in both age groups. One risk variant for smaller caudate nucleus volume (TUFM locus) was associated with lower cognitive performance. Genetically predicted Alzheimer's disease was associated with smaller subcortical volumes in middle and older age. CONCLUSIONS: Our findings provide novel insights into the genetic determinants of subcortical volumes across the lifespan. More studies are needed to decipher the underlying biology and clinical impact.

Age-Related Variations in Regional White Matter Volumetry and Microstructure During the Post-adolescence Period: A Cross-Sectional Study of a Cohort of 1,713 University Students.

Human brain white matter undergoes a protracted maturation that continues well into adulthood. Recent advances in diffusion-weighted imaging (DWI) methods allow detailed characterizations of the microstructural architecture of white matter, and they are increasingly utilized to study white matter changes during development and aging. However, relatively little is known about the late maturational changes in the microstructural architecture of white matter during post-adolescence. Here we report on regional changes in white matter volume and microstructure in young adults undergoing university-level education. As part of the MRi-Share multi-modal brain MRI database, multi-shell, high angular resolution DWI data were acquired in a unique sample of 1,713 university students aged 18-26. We assessed the age and sex dependence of diffusion metrics derived from diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI) in the white matter regions as defined in the John Hopkins University (JHU) white matter labels atlas. We demonstrate that while regional white matter volume is relatively stable over the age range of our sample, the white matter microstructural properties show clear age-related variations. Globally, it is characterized by a robust increase in neurite density index (NDI), and to a lesser extent, orientation dispersion index (ODI). These changes are accompanied by a decrease in diffusivity. In contrast, there is minimal age-related variation in fractional anisotropy. There are regional variations in these microstructural changes: some tracts, most notably cingulum bundles, show a strong age-related increase in NDI coupled with decreases in radial and mean diffusivity, while others, mainly cortico-spinal projection tracts, primarily show an ODI increase and axial diffusivity decrease. These age-related variations are not different between males and females, but males show higher NDI and ODI and lower diffusivity than females across many tracts. These findings emphasize the complexity of changes in white matter structure occurring in this critical period of late maturation in early adulthood.

Novel characterization of the relationship between verbal list-learning outcomes and hippocampal subfields in healthy adults.

The relationship between hippocampal subfield volumetry and verbal list-learning test outcomes have mostly been studied in clinical and elderly populations, and remain controversial. For the first time, we characterized a relationship between verbal list-learning test outcomes and hippocampal subfield volumetry on two large separate datasets of 447 and 1,442 healthy young and middle-aged adults, and explored the processes that could explain this relationship. We observed a replicable positive linear correlation between verbal list-learning test free recall scores and CA1 volume, specific to verbal list learning as demonstrated by the hippocampal subfield volumetry independence from verbal intelligence. Learning meaningless items was also positively correlated with CA1 volume, pointing to the role of the test design rather than word meaning. Accordingly, we found that association-based mnemonics mediated the relationship between verbal list-learning test outcomes and CA1 volume. This mediation suggests that integrating items into associative representations during verbal list-learning tests explains CA1 volume variations: this new explanation is consistent with the associative functions of the human CA1.

3D Segmentation of Perivascular Spaces on T1-Weighted 3 Tesla MR Images With a Convolutional Autoencoder and a U-Shaped Neural Network.

We implemented a deep learning (DL) algorithm for the 3-dimensional segmentation of perivascular spaces (PVSs) in deep white matter (DWM) and basal ganglia (BG). This algorithm is based on an autoencoder and a U-shaped network (U-net), and was trained and tested using T1-weighted magnetic resonance imaging (MRI) data from a large database of 1,832 healthy young adults. An important feature of this approach is the ability to learn from relatively sparse data, which gives the present algorithm a major advantage over other DL algorithms. Here, we trained the algorithm with 40 T1-weighted MRI datasets in which all "visible" PVSs were manually annotated by an experienced operator. After learning, performance was assessed using another set of 10 MRI scans from the same database in which PVSs were also traced by the same operator and were checked by consensus with another experienced operator. The Sorensen-Dice coefficients for PVS voxel detection in DWM (resp. BG) were 0.51 (resp. 0.66), and 0.64 (resp. 0.71) for PVS cluster detection (volume threshold of 0.5 within a range of 0 to 1). Dice values above 0.90 could be reached for detecting PVSs larger than 10 mm3 and 0.95 for PVSs larger than 15 mm3. We then applied the trained algorithm to the rest of the database (1,782 individuals). The individual PVS load provided by the algorithm showed a high agreement with a semi-quantitative visual rating done by an independent expert rater, both for DWM and for BG. Finally, we applied the trained algorithm to an age-matched sample from another MRI database acquired using a different scanner. We obtained a very similar distribution of PVS load, demonstrating the interoperability of this algorithm.

The MRi-Share database: brain imaging in a cross-sectional cohort of 1870 university students.

We report on MRi-Share, a multi-modal brain MRI database acquired in a unique sample of 1870 young healthy adults, aged 18-35 years, while undergoing university-level education. MRi-Share contains structural (T1 and FLAIR), diffusion (multispectral), susceptibility-weighted (SWI), and resting-state functional imaging modalities. Here, we described the contents of these different neuroimaging datasets and the processing pipelines used to derive brain phenotypes, as well as how quality control was assessed. In addition, we present preliminary results on associations of some of these brain image-derived phenotypes at the whole brain level with both age and sex, in the subsample of 1722 individuals aged less than 26 years. We demonstrate that the post-adolescence period is characterized by changes in both structural and microstructural brain phenotypes. Grey matter cortical thickness, surface area and volume were found to decrease with age, while white matter volume shows increase. Diffusivity, either radial or axial, was found to robustly decrease with age whereas fractional anisotropy only slightly increased. As for the neurite orientation dispersion and densities, both were found to increase with age. The isotropic volume fraction also showed a slight increase with age. These preliminary findings emphasize the complexity of changes in brain structure and function occurring in this critical period at the interface of late maturation and early ageing.

Prevalence, Severity, and Clinical Management of Brain Incidental Findings in Healthy Young Adults: MRi-Share Cross-Sectional Study.

Background and Objectives: Young adults represent an increasingly large proportion of healthy volunteers in brain imaging research, but descriptions of incidental findings (IFs) in this age group are scarce. We aimed to assess the prevalence and severity of IFs on brain MRIs of healthy young research participants aged 18-35 years, and to describe the protocol implemented to handle them. Methods: The study population comprised 1,867 participants aged 22.1 ± 2.3 years (72% women) from MRi-Share, the cross-sectional brain MRI substudy of the i-Share student cohort. IFs were flagged during the MRI quality control. We estimated the proportion of participants with IFs [any, requiring medical referral, potentially serious (PSIFs) as defined in the UK biobank]: overall, by type and severity of the final diagnosis, as well as the number of IFs. Results: 78/1,867 participants had at least one IF [4.2%, 95% Confidence Interval (CI) 3.4-5.2%]. IFs requiring medical referral (n = 38) were observed in 36/1,867 participants (1.9%, 1.4-2.7%), and represented 47.5% of the 80 IFs initially flagged. Referred IFs were retrospectively classified as PSIFs in 25/1,867 participants (1.3%, 0.9-2.0%), accounting for 68.4% of anomalies referred (26/38). The most common final diagnosis was cysts or ventricular abnormalities in all participants (9/1,867; 0.5%, 0.2-0.9%) and in those with referred IFs (9/36; 25.0%, 13.6-41.3%), while it was multiple sclerosis or radiologically isolated syndrome in participants with PSIFs (5/19; 26.3%, 11.5-49.1%) who represented 0.1% (0.0-0.4%) and 0.2% (0.03-0.5%) of all participants, respectively. Final diagnoses were considered serious in 11/1,867 participants (0.6%, 0.3-1.1%). Among participants with referred IFs, 13.9% (5/36) required active intervention, while 50.0% (18/36) were put on clinical surveillance. Conclusions: In a large brain imaging study of young healthy adults participating in research we observed a non-negligible frequency of IFs. The etiological pattern differed from what has been described in older adults.

Causal Prefrontal Contributions to Stop-Signal Task Performance in Humans.

The frontal lobes have long been implicated in inhibitory control, but a full understanding of the underlying mechanisms remains elusive. The stop-signal task has been widely used to probe instructed response inhibition in cognitive neuroscience. The processes involved have been modeled and related to putative brain substrates. However, there has been surprisingly little human lesion research using this task, with the few existing studies implicating different prefrontal regions. Here, we tested the effects of focal prefrontal damage on stop-signal task performance in a large sample of people with chronic focal damage affecting the frontal lobes (n = 42) and demographically matched healthy individuals (n = 60). Patients with damage to the left lateral, right lateral, dorsomedial, or ventromedial frontal lobe had slower stop-signal RT compared to healthy controls. There were systematic differences in the patterns of impairment across frontal subgroups: Those with damage to the left or right lateral and dorsomedial frontal lobes, but not those with ventromedial frontal damage, were slower than controls to "go" as well as to stop. These findings suggest that multiple prefrontal regions make necessary but distinct contributions to stop-signal task performance. As a consequence, stop-signal RT slowing is not strongly localizing within the frontal lobes.

Cerebral small vessel disease genomics and its implications across the lifespan.

White matter hyperintensities (WMH) are the most common brain-imaging feature of cerebral small vessel disease (SVD), hypertension being the main known risk factor. Here, we identify 27 genome-wide loci for WMH-volume in a cohort of 50,970 older individuals, accounting for modification/confounding by hypertension. Aggregated WMH risk variants were associated with altered white matter integrity (p = 2.5×10-7) in brain images from 1,738 young healthy adults, providing insight into the lifetime impact of SVD genetic risk. Mendelian randomization suggested causal association of increasing WMH-volume with stroke, Alzheimer-type dementia, and of increasing blood pressure (BP) with larger WMH-volume, notably also in persons without clinical hypertension. Transcriptome-wide colocalization analyses showed association of WMH-volume with expression of 39 genes, of which four encode known drug targets. Finally, we provide insight into BP-independent biological pathways underlying SVD and suggest potential for genetic stratification of high-risk individuals and for genetically-informed prioritization of drug targets for prevention trials.

Age-Related Changes of Peak Width Skeletonized Mean Diffusivity (PSMD) Across the Adult Lifespan: A Multi-Cohort Study.

Parameters of water diffusion in white matter derived from diffusion-weighted imaging (DWI), such as fractional anisotropy (FA), mean, axial, and radial diffusivity (MD, AD, and RD), and more recently, peak width of skeletonized mean diffusivity (PSMD), have been proposed as potential markers of normal and pathological brain ageing. However, their relative evolution over the entire adult lifespan in healthy individuals remains partly unknown during early and late adulthood, and particularly for the PSMD index. Here, we gathered and analyzed cross-sectional diffusion tensor imaging (DTI) data from 10 population-based cohort studies in order to establish the time course of white matter water diffusion phenotypes from post-adolescence to late adulthood. DTI data were obtained from a total of 20,005 individuals aged 18.1 to 92.6 years and analyzed with the same pipeline for computing skeletonized DTI metrics from DTI maps. For each individual, MD, AD, RD, and FA mean values were computed over their FA volume skeleton, PSMD being calculated as the 90% peak width of the MD values distribution across the FA skeleton. Mean values of each DTI metric were found to strongly vary across cohorts, most likely due to major differences in DWI acquisition protocols as well as pre-processing and DTI model fitting. However, age effects on each DTI metric were found to be highly consistent across cohorts. RD, MD, and AD variations with age exhibited the same U-shape pattern, first slowly decreasing during post-adolescence until the age of 30, 40, and 50 years, respectively, then progressively increasing until late life. FA showed a reverse profile, initially increasing then continuously decreasing, slowly until the 70s, then sharply declining thereafter. By contrast, PSMD constantly increased, first slowly until the 60s, then more sharply. These results demonstrate that, in the general population, age affects PSMD in a manner different from that of other DTI metrics. The constant increase in PSMD throughout the entire adult life, including during post-adolescence, indicates that PSMD could be an early marker of the ageing process.

Material-specific interference control is dissociable and lateralized in human prefrontal cortex.

The prefrontal cortex (PFC) plays a key role in the ability to pursue a particular goal in the face of competing alternatives, an ability that is fundamental to higher-order human behavior. Whether this region contributes to cognitive control through material-general mechanisms, or through hemispheric specialization of component abilities, remains unclear. Here we show that left or right ventrolateral PFC damage in humans leads to doubly dissociable deficits in two classic tests of interference control. Patients with damage centered on left ventrolateral prefrontal cortex had exaggerated interference effects in the color-word Stroop, but not the Eriksen flanker task, whereas patients with damage affecting right ventrolateral prefrontal cortex showed the opposite pattern. Thus, effective interference resolution requires either right or left lateral PFC, depending on the nature of the task. This finding supports a lateralized, material-specific account of cognitive control in humans.

Are core component processes of executive function dissociable within the frontal lobes? Evidence from humans with focal prefrontal damage.

Executive function encompasses a range of control processes supporting flexible, goal-directed behaviour. Attentional set-shifting, updating of information in working memory, and inhibitory control have been proposed as key components of executive function, but debate continues as to the validity of this conceptual framework, and the neural substrates of these putative components. Here we examined prefrontal structure-function relationships for each of these component processes in a large cohort of patients with focal prefrontal damage. Forty-five patients with focal damage to various sectors of prefrontal cortex (PFC), and 50 demographically matched healthy control subjects performed an attention shifting task, the Stroop colour naming task, and a spatial search task. Voxel-based lesion-symptom mapping revealed that damage to left ventrolateral PFC led to impaired performance on both the Stroop and attention shifting tasks. In contrast, performance of the spatial search task depended on several regions within PFC, but notably not left ventrolateral PFC. These observations were confirmed with direct comparison of performance between patients grouped according to lesion location. This dissociation partly supports the component process view of executive function, distinguishing the goal-directed regulation of attention (perhaps specifically in the verbal domain) from the requirements of the spatial search task, including the updating of information in spatial working memory. These findings are easier to reconcile with modular, material-specific accounts than with more unitary models of executive function.

Are you upset? Distinct roles for orbitofrontal and lateral prefrontal cortex in detecting and distinguishing facial expressions of emotion.

Navigating our complex social world requires effective processing of subtle emotional signals, such as those conveyed by facial expressions. Failure to do so may underlie some of the disabling social-emotional deficits common in a range of neuropsychiatric and neurological conditions. Prefrontal cortex (PFC) has long been implicated in these processes, but the particular contributions of subregions within PFC remain unclear. We used a sensitive facial emotion rating task in patients with focal lesions to different regions within PFC to identify distinct contributions of 2 prefrontal regions to recognizing emotions from facial expressions. A combination of region-of-interest and voxel-based lesion-symptom mapping established that damage to ventromedial PFC impaired the detection of subtle facial expressions of emotion. Such patients had difficulty distinguishing emotional from neutral expressions. In contrast, patients with left ventrolateral PFC were able to detect the presence of emotional signals but had difficulty discriminating between specific emotions. These effects were regionally specific: Dorsomedial prefrontal damage had no effect on either aspect of emotion recognition. These findings suggest that separable processes relying critically on distinct regions within PFC responsible, on the one hand, for detecting emotional signals from facial expressions and, on the other, for correctly classifying such signals.

Double dissociation of stimulus-value and action-value learning in humans with orbitofrontal or anterior cingulate cortex damage.

Adaptive decision making involves selecting the most valuable option, typically by taking an action. Such choices require value comparisons, but there is debate about whether these comparisons occur at the level of stimuli (goods-based) value, action-based value, or both. One view is that value processes occur in series, with stimulus value informing action value. However, lesion work in nonhuman primates suggests that these two kinds of choice are dissociable. Here, we examined action-value and stimulus-value learning in humans with focal frontal lobe damage. Orbitofrontal damage disrupted the ability to sustain the correct choice of stimulus, but not of action, after positive feedback, while damage centered on dorsal anterior cingulate cortex led to the opposite deficit. These findings argue that there are distinct, domain-specific mechanisms by which outcome value is applied to guide subsequent decisions, depending on whether the choice is between stimuli or between actions.

Beyond reversal: a critical role for human orbitofrontal cortex in flexible learning from probabilistic feedback.

Damage to the orbitofrontal cortex (OFC) has been linked to impaired reinforcement processing and maladaptive behavior in changing environments across species. Flexible stimulus-outcome learning, canonically captured by reversal learning tasks, has been shown to rely critically on OFC in rats, monkeys, and humans. However, the precise role of OFC in this learning remains unclear. Furthermore, whether other frontal regions also contribute has not been definitively established, particularly in humans. In the present study, a reversal learning task with probabilistic feedback was administered to 39 patients with focal lesions affecting various sectors of the frontal lobes and to 51 healthy, demographically matched control subjects. Standard groupwise comparisons were supplemented with voxel-based lesion-symptom mapping to identify regions within the frontal lobes critical for task performance. Learning in this dynamic stimulus-reinforcement environment was considered both in terms of overall performance and at the trial-by-trial level. In this challenging, probabilistic context, OFC damage disrupted both initial and reversal learning. Trial-by-trial performance patterns suggest that OFC plays a critical role in interpreting feedback from a particular trial within the broader context of the outcome history across trials rather than in simply suppressing preexisting stimulus-outcome associations. The findings show that OFC, and not other prefrontal regions, plays a necessary role in flexible stimulus-reinforcement learning in humans.