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Accurate classification or prediction of the brain state across individual subject, i.e., healthy, or with brain disorders, is generally a more difficult task than merely finding group differences. The former must be approached with highly informative and sensitive biomarkers as well as effective pattern classification/feature selection approaches. In this paper, we propose a systematic methodology to discriminate attention deficit hyperactivity disorder (ADHD) patients from healthy controls on the individual level. Multiple neuroimaging markers that are proved to be sensitive features are identified, which include multiscale characteristics extracted from blood oxygenation level dependent (BOLD) signals, such as regional homogeneity (ReHo) and amplitude of low-frequency fluctuations. Functional connectivity derived from Pearson, partial, and spatial correlation is also utilized to reflect the abnormal patterns of functional integration, or, dysconnectivity syndromes in the brain. These neuroimaging markers are calculated on either voxel or regional level. Advanced feature selection approach is then designed, including a brain-wise association study (BWAS). Using identified features and proper feature integration, a support vector machine (SVM) classifier can achieve a cross-validated classification accuracy of 76.15% across individuals from a large dataset consisting of 141 healthy controls and 98 ADHD patients, with the sensitivity being 63.27% and the specificity being 85.11%. Our results show that the most discriminative features for classification are primarily associated with the frontal and cerebellar regions. The proposed methodology is expected to improve clinical diagnosis and evaluation of treatment for ADHD patient, and to have wider applications in diagnosis of general neuropsychiatric disorders.

Dysfunctional reward processing is implicated in various mental disorders, including attention deficit hyperactivity disorder (ADHD) and addictions. Such impairments might involve different components of the reward process, including brain activity during reward anticipation. We examined brain nodes engaged by reward anticipation in 1,544 adolescents and identified a network containing a core striatal node and cortical nodes facilitating outcome prediction and response preparation. Distinct nodes and functional connections were preferentially associated with either adolescent hyperactivity or alcohol consumption, thus conveying specificity of reward processing to clinically relevant behavior. We observed associations between the striatal node, hyperactivity, and the vacuolar protein sorting-associated protein 4A (VPS4A) gene in humans, and the causal role of Vps4 for hyperactivity was validated in Drosophila. Our data provide a neurobehavioral model explaining the heterogeneity of rewardrelated behaviors and generate a hypothesis accounting for their enduring nature.uccessful behavioral adaptation requires effective reward processing that determines whether a desired goal is approached and maintained. Reward processing can be separated into behavioral anticipation or reward expectancy as a consequence of learning and behavioral and subjective responses to rewarding outcomes (1). In humans, dysfunctional reward processing (in particular, dysfunctional reward anticipation) has been implicated in various externalizing disorders, including attention-deficit hyperactivity disorder (ADHD) (2) and addiction (3). Brain regions involved in reward anticipation include the ventral tegmental area, the medial forebrain bundle, and the nucleus accumbens/ventral striatum (VS; including the ventral caudate-putamen) as well as the ventromedial and insular cortices (4). More recently, observations have been reported to link reward processing in humans with cortical activation (5), including the primary somatosensory (6), primary visual (V1) (7), and auditory (8) cortices. Dopamine is the principal neurotransmitter regulating reward processing, particularly through the mesocorticolimbic pathway (9), the neuronal projection from the ventral tegmental area to the VS and prefrontal cortex. A general feature of striatal information processing is the control by rewardrelated dopamine signals of direct and indirect cortical inputs from different neurotransmitter systems, including noradrenaline, glutamate, and GABA as well as acetylcholine, endogenous opioids, and cannabinoids (10). As a consequence, striatal dopaminergic activity integrates cortical and subcortical inputs with reward response. In addition to direct and indirect regulation by heteroceptors, dopamine release is regulated by presynaptic autoreceptors of the D2 family, in particular D2 dopamine receptors (DRD2) that Author contributions: T.J., S.D., C.P.M., J.F., A.R., H.F., and G.S. designed research; T.J., C.M., S.D., D.A.G., C.T., B.R., F.N., T.B., G.J.B., A.L.W.B., U.B...

To analyze the involvement of different brain regions in behavioral inhibition and impulsiveness, differences in activation were investigated in fMRI data from a response inhibition task, the stop-signal task, in 1709 participants. First, areas activated more in stop-success (SS) than stop-failure (SF) included the lateral orbitofrontal cortex (OFC) extending into the inferior frontal gyrus (ventrolateral prefrontal cortex, BA 47/12), and the dorsolateral prefrontal cortex (DLPFC). Second, the anterior cingulate and anterior insula (AI) were activated more on failure trials, specifically in SF versus SS. The interaction between brain region and SS versus SF activations was significant (P = 5.6 * 10 ). The results provide new evidence from this "big data" investigation consistent with the hypotheses that the lateral OFC is involved in the stop-related processing that inhibits the action; that the DLPFC is involved in attentional processes that influence task performance; and that the AI and anterior cingulate are involved in emotional processes when failure occurs. The investigation thus emphasizes the role of the human lateral OFC BA 47/12 in changing behavior, and inhibiting behavior when necessary. A very similar area in BA47/12 is involved in changing behavior when an expected reward is not obtained, and has been shown to have high functional connectivity in depression. Hum Brain Mapp, 2017. © 2017 Wiley Periodicals, Inc.

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