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Background Binge eating and other forms of disordered eating behavior (DEB) are associated with failed inhibitory control. This study investigated the neural correlates of failed inhibitory control as a potential biomarker for DEB. Methods The study used prospective longitudinal data from the European IMAGEN study adolescent cohort. Participants completed baseline assessments (questionnaires and a brain scan [functional magnetic resonance imaging]) at 14 years of age and a follow-up assessment (questionnaires) at 16 years of age. Self-reported binge eating and/or purging were used to indicate presence of DEB. Neural correlates of failed inhibition were assessed using the stop signal task. Participants were categorized as healthy control subjects (reported no DEB at both time points), maintainers (reported DEB at both time points), recoverers (reported DEB at baseline only), and developers (reported DEB at follow-up only). Forty-three individuals per group with complete scanning data were matched on gender, age, puberty, and intelligence (N = 172). Results At baseline, despite similar task performance, incorrectly responding to stop signals (failed inhibitory control) was associated with greater recruitment of the medial prefrontal cortex and anterior cingulate cortex in the developers compared with healthy control subjects and recoverers. Conclusions Greater recruitment of the medial prefrontal and anterior cingulate regions during failed inhibition accords with abnormal evaluation of errors contributing to DEB development. As this precedes symptom onset and is evident despite normal task performance, neural responses during failed inhibition may be a useful biomarker of vulnerability for DEB. This study highlights the potential value of prospective neuroimaging studies for identifying markers of illness before the emergence of behavior changes.

Motivation is important for learning and cognition. Although dopaminergic (D2) transmission in the ventral striatum (VS) is associated with motivation, learning, and cognition are more strongly associated with function of the dorsal striatum, including activation in the caudate nucleus. A recent study found an interaction between intrinsic motivation and the DRD2/ANKK1 polymorphism (rs1800497), suggesting that A-carriers of rs1800497 are significantly more sensitive to motivation in order to improve during working memory (WM) training. Using data from the two large-scale imaging genetic data sets, IMAGEN (n=1080, age 13–15 years) and BrainChild (n∼300, age 6–27), we investigated whether rs1800497 is associated with WM. In the IMAGEN data set, we tested whether VS/caudate activation during reward anticipation was associated with WM performance and whether rs1800497 and VS/caudate activation interact to affect WM performance. We found that rs1800497 was associated with WM performance in IMAGEN and BrainChild. Higher VS and caudate activation during reward processing were significantly associated with higher WM performance (p

Face expressions are a rich source of social signals. Here we estimated the proportion of phenotypic variance in the brain response to facial expressions explained by common genetic variance captured by ∼500,000 single nucleotide polymorphisms. Using genomic-relationship-matrix restricted maximum likelihood (GREML), we related this global genetic variance to that in the brain response to facial expressions, as assessed with functional magnetic resonance imaging (fMRI) in a community-based sample of adolescents (n = 1,620). Brain response to facial expressions was measured in 25 regions constituting a face network, as defined previously. In 9 out of these 25 regions, common genetic variance explained a significant proportion of phenotypic variance (40–50%) in their response to ambiguous facial expressions; this was not the case for angry facial expressions. Across the network, the strength of the genotype-phenotype relationship varied as a function of the inter-individual variability in the number of functional connections possessed by a given region (R2 = 0.38, p, Author Summary We measured brain response to facial expressions in a large sample of typically developing adolescents (n = 1,620) and assessed “heritability” of the response using common genetic variations across the genome. In a subset of brain regions, we explained 40–50% of phenotypic variance by genetic variance. These brain regions appear to differ from the rest of the face network in the degree of inter-individual variations in their functional connectivity. We propose that these regions, including the prefrontal and premotor cortex, represent “Optional” part of the network co-opted by its “Obligatory” members, including the posterior part of the superior temporal sulcus, fusiform face area and the lateral occipital cortex, concerned with processing complex visual stimuli.

Defining cases and controls on the basis of biomarkers rather than clinical diagnosis may reduce sample sizes required for genetic studies. The aim of this study was to assess whether characterizing case/control status on the basis of cerebrospinal fluid (CSF) profile would increase power to replicate known genetic associations for Alzheimer's disease (AD). Independent of clinical diagnosis, Alzheimer's Disease Neuroimaging Initiative (ADNI) subjects with 2 CSF biomarkers for AD (Aβ1-42 < 192 pg/mL and tau phosphorylated at threonine 181 (p-tau) > 23 pg/mL, "CSF-positive") were compared with those without CSF evidence for AD (Aβ1-42 > 192 pg/mL and 181-phosphorylated tau < 23 pg/mL, "CSF-negative"). Minor allele frequency (MAF) and odds ratios (ORs) between these 2 groups were calculated for 7 single-nucleotide polymorphisms (SNPs) of interest. Two hundred thirty-two individuals were CSF-positive and 94 CSF-negative. There were no differences in age (74.7 ± 7.2 vs. 75.0 ± 6.5 years, p = 0.7), but significant differences in Mini Mental State Examination (MMSE) (25.9 ± 2.6 vs. 28.2 ± 1.7, p < 0.001) between the CSF-positive and CSF-negative groups. Significant differences in MAF (p < 0.05, uncorrected) were seen for CR1 (rs1408077; OR, 1.59; 95% confidence interval [CI], 1.01-2.49), PICALM (rs541458; OR, 0.68, 95% CI, 0.47-0.98), TOMM40 (rs2075650; OR, 4.30; 95% CI, 2.61-7.06); and possession of 1 or more APOE ε4 alleles (OR, 9.84; 95% CI, 5.48-17.67). These results suggest that using biomarkers of AD pathology to define case and control status may increase power in genetic association studies

To assess whether single-time-point and longitudinal volumetric magnetic resonance (MR) imaging measures provide predictive prognostic information in patients with amnestic mild cognitive impairment (MCI).This study was conducted with institutional review board approval and in compliance with HIPAA regulations. Written informed consent was obtained from all participants or the participants' legal guardians. Cross-validated discriminant analyses of MR imaging measures were performed to differentiate 164 Alzheimer disease (AD) cases from 203 healthy control cases. Separate analyses were performed by using data from MR images obtained at one time point or by combining single-time-point measures with 1-year change measures. Resulting discriminant functions were applied to 317 MCI cases to derive individual patient risk scores. Risk of conversion to AD was estimated as a continuous function of risk score percentile. Kaplan-Meier survival curves were computed for risk score quartiles. Odds ratios (ORs) for the conversion to AD were computed between the highest and lowest quartile scores.Individualized risk estimates from baseline MR examinations indicated that the 1-year risk of conversion to AD ranged from 3% to 40% (average group risk, 17%; OR, 7.2 for highest vs lowest score quartiles). Including measures of 1-year change in global and regional volumes significantly improved risk estimates (P = 001), with the risk of conversion to AD in the subsequent year ranging from 3% to 69% (average group risk, 27%; OR, 12.0 for highest vs lowest score quartiles).Relative to the risk of conversion to AD conferred by the clinical diagnosis of MCI alone, MR imaging measures yield substantially more informative patient-specific risk estimates. Such predictive prognostic information will be critical if disease-modifying therapies become available.http://radiology.rsna.org/lookup/suppl/doi:10.1148/radiol.11101975/-/DC1.

Amyloid-β accumulation in the brain is thought to be one of the earliest events in Alzheimer's disease, possibly leading to synaptic dysfunction, neurodegeneration and cognitive/functional decline. The earliest detectable changes seen with neuroimaging appear to be amyloid-β accumulation detected by (11)C-labelled Pittsburgh compound B positron emission tomography imaging. However, some individuals tolerate high brain amyloid-β loads without developing symptoms, while others progressively decline, suggesting that events in the brain downstream from amyloid-β deposition, such as regional brain atrophy rates, play an important role. The main purpose of this study was to understand the relationship between the regional distributions of increased amyloid-β and the regional distribution of increased brain atrophy rates in patients with mild cognitive impairment. To simultaneously capture the spatial distributions of amyloid-β and brain atrophy rates, we employed the statistical concept of parallel independent component analysis, an effective method for joint analysis of multimodal imaging data. Parallel independent component analysis identified significant relationships between two patterns of amyloid-β deposition and atrophy rates: (i) increased amyloid-β burden in the left precuneus/cuneus and medial-temporal regions was associated with increased brain atrophy rates in the left medial-temporal and parietal regions; and (ii) in contrast, increased amyloid-β burden in bilateral precuneus/cuneus and parietal regions was associated with increased brain atrophy rates in the right medial temporal regions. The spatial distribution of increased amyloid-β and the associated spatial distribution of increased brain atrophy rates embrace a characteristic pattern of brain structures known for a high vulnerability to Alzheimer's disease pathology, encouraging for the use of (11)C-labelled Pittsburgh compound B positron emission tomography measures as early indicators of Alzheimer's disease. These results may begin to shed light on the mechanisms by which amyloid-β deposition leads to neurodegeneration and cognitive decline and the development of a more specific Alzheimer's disease-specific imaging signature for diagnosis and use of this knowledge in the development of new anti-therapies for Alzheimer's disease.